Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation: The Problem of Chest Compression Artifact

*Mikel Leturiondo, Sofía Ruiz de Gauna, José Julio Gutiérrez, Digna M. González-Otero, Jesus M. Ruiz, Luis A. Leturiondo and Purificación Saiz*

## **Abstract**

Sudden cardiac arrest (SCA) is the sudden cessation of the heart's effective pumping function, confirmed by the absence of pulse and breathing. Without appropriate treatment, it leads to sudden cardiac death, considered responsible for half of the global cardiac disease deaths. Cardiopulmonary resuscitation (CPR) is a key intervention during SCA. Current resuscitation guidelines emphasize the use of waveform capnography during CPR in order to enhance CPR quality and improve patient outcomes. Capnography represents the concentration of the partial pressure of carbon dioxide (CO2) in respiratory gases and reflects ventilation and perfusion of the patient. Waveform capnography should be used for confirming the correct placement of the tracheal tube and monitoring ventilation. Other potential uses of capnography in resuscitation involve monitoring CPR quality, early identification of restoration of spontaneous circulation (ROSC), and determination of patient prognosis. An important role of waveform capnography is ventilation rate monitoring to prevent overventilation. However, some studies have reported the appearance of high-frequency oscillations synchronized with chest compressions superimposed on the capnogram. This chapter explores the incidence of chest compression artifact in out-of-hospital capnograms, assesses its negative influence in the automated detection of ventilations, and proposes several methods to enhance ventilation detection and capnography waveform.

**Keywords:** cardiopulmonary resuscitation, advanced life support, waveform capnography, ventilation, chest compression artifact

## **1. Introduction**

In the past century, cardiac disease was declared as one of the leading causes of global death, comprising a 30% of the global mortality [1]. It is estimated that sudden cardiac death is responsible for half of all cardiac disease deaths [1, 2], affecting more than 300,000 victims per year in the United States and around 275,000 in the Europe [3–5]. About 80% of sudden cardiac deaths are caused by out-of-hospital cardiac arrests (OHCA) [1], defined as the sudden cessation of the heart's effective pumping function confirmed by the absence of pulse and breathing and occurring in an out-of-hospital setting [6].

During OHCA, there are two prehospital life supporting emergency medical services (EMS): basic life support (BLS) and advanced life support (ALS). BLS treatment is provided by emergency medical technicians and includes early CPR and early defibrillation, usually delivered with an automated external defibrillator (AED). ALS treatment procured by clinicians during CPR usually includes manual defibrillation, advanced airway placement, and drug administration, together with CPR [7, 8].

Several studies have reported a strong correlation between the quality of CPR and the chance of successful defibrillation [9–11]. Thus, resuscitation guidelines [12, 13] globally recommend providing chest compressions with a rate in the range of 100 and 120 compressions per minute (cpm) and achieving a depth between 5 and 6 cm. Ventilations should be provided with a 30 compressions-to-2 ventilations ratio before intubation. After intubation, ALS guidelines recommend continuous chest compressions and ventilations with a ventilation rate around 10 breaths per minute [7, 8]. Despite the fact that some studies have declared hyperventilation as harmful for patient outcome, by either high rate or volume [14, 15], excessive ventilation rates (as high as 30 breaths per minute) are common in resuscitation [16–18]. Many animal studies revealed that high ventilation rates increased intrathoracic pressures and decreased coronary perfusion and survival rates [16, 19, 20]. However, another recent animal study reported no adverse hemodynamic effects, although they did observe a decrease in maximum CO2 values [21].

In order to alleviate this problem and prevent inadvertent hyperventilation, resuscitation guidelines highlight the role of capnography for ventilation rate monitoring during CPR [7, 8]. Other advantages of capnography include assessment of the correct placement of the endotracheal tube [21], monitoring the quality of chest compressions [22], early identification of restoration of spontaneous circulation (ROSC) [23], and determination of patient prognosis [7, 24, 25].

This chapter analyzes the use of capnometry for ventilation monitoring during OHCA episodes. First, we briefly introduce the evolution of capnometry and the different technologies used in the field. Then, we characterize the capnography signal during ongoing CPR. The main conclusion of this analysis is that the appearance of high-frequency oscillations superimposed on the waveform capnography is frequent during resuscitation. We then analyze the impact of these oscillations on out-of-hospital automated detection of ventilations. Finally, we propose two methods to improve ventilation detection during CPR by filtering the artifact from the capnography signal and a method to enhance capnography waveform in the presence of artifact.

## **2. Evolution of capnometry**

Since 1943, capnometry has become an essential component of standard anesthesia monitoring [26]. Capnometry represents the numerical value of the carbon dioxide (CO2) partial pressure measurement in exhaled respiratory gases. The maximum CO2 concentration at the end of the exhalation, known as end-tidal CO2 (ETCO2), reflects cardiac output and pulmonary blood flow. Preventing hypoxia, i.e., deprivation of adequate oxygen supply, during anesthesia is the primary goal of

**25**

**Figure 2.**

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

anesthesiologists. Improvements with capnometry in this field currently allow the early identification of harmful situations before hypoxia leads to irreversible brain damage. Because of these improvements, the use of capnography has spread from the operating room into emergency medicine environment and even into out-of-

Several methods have been used to determine the presence and concentration of CO2 over the years. The simplest form of CO2 detection available is colorimetric capnometry. This technology is based on a paper that changes in color in the presence of CO2, but its inability to detect breath-to-breath changes prohibits the use of this device to guide ventilation. Later, semiquantitative capnometers (**Figure 1a**) that provide a rough estimation of the ETCO2 concentration have been developed. The technology behind these devices reports the ETCO2 value in a series of stacked colors rather than providing a numerical value, being useful to confirm correct

More recently, quantitative capnometry involving infrared spectrophotometric analysis of expired gases (**Figure 1b**) has led to the most accurate method to measure ETCO2 values. This technology provides an end-tidal value for each breath, allowing an optimal control of ventilation. Improvements in the field allowed the graphical representation and recording of the CO2 concentration throughout the

Two different methods of gas sampling, illustrated in **Figure 2**, are used to measure quantitative capnometry and waveform capnography: mainstream and sidestream. The main difference is that mainstream is directly placed in the main flow of exhaled gases, while in sidestream little samples are aspirated with a capillary sampling tube. During the last two decades, improvements in high-flow sidestream capnometers turned into Microstream™ capnometers, with an aspiration flow rate

*Evolution of capnometry in out-of-hospital emergency settings. (a) Semiquantitative capnometer, (b) quantitative capnometer, and (c) waveform capnography. Courtesy of Medtronic and Masimo.*

*Brief schemes of quantitative capnometry to acquire the capnography signal, mainstream and sidestream.*

. This technology uses a highly CO2-specific infrared source where

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

breath (i.e., waveform capnography, **Figure 1c**).

hospital emergency settings.

airway placement.

of 50 ml min<sup>−</sup><sup>1</sup>

**Figure 1.**

### *Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

anesthesiologists. Improvements with capnometry in this field currently allow the early identification of harmful situations before hypoxia leads to irreversible brain damage. Because of these improvements, the use of capnography has spread from the operating room into emergency medicine environment and even into out-ofhospital emergency settings.

Several methods have been used to determine the presence and concentration of CO2 over the years. The simplest form of CO2 detection available is colorimetric capnometry. This technology is based on a paper that changes in color in the presence of CO2, but its inability to detect breath-to-breath changes prohibits the use of this device to guide ventilation. Later, semiquantitative capnometers (**Figure 1a**) that provide a rough estimation of the ETCO2 concentration have been developed. The technology behind these devices reports the ETCO2 value in a series of stacked colors rather than providing a numerical value, being useful to confirm correct airway placement.

More recently, quantitative capnometry involving infrared spectrophotometric analysis of expired gases (**Figure 1b**) has led to the most accurate method to measure ETCO2 values. This technology provides an end-tidal value for each breath, allowing an optimal control of ventilation. Improvements in the field allowed the graphical representation and recording of the CO2 concentration throughout the breath (i.e., waveform capnography, **Figure 1c**).

Two different methods of gas sampling, illustrated in **Figure 2**, are used to measure quantitative capnometry and waveform capnography: mainstream and sidestream. The main difference is that mainstream is directly placed in the main flow of exhaled gases, while in sidestream little samples are aspirated with a capillary sampling tube. During the last two decades, improvements in high-flow sidestream capnometers turned into Microstream™ capnometers, with an aspiration flow rate of 50 ml min<sup>−</sup><sup>1</sup> . This technology uses a highly CO2-specific infrared source where

**Figure 1.**

*Cardiac Diseases and Interventions in 21st Century*

and occurring in an out-of-hospital setting [6].

CPR [7, 8].

sudden cardiac death is responsible for half of all cardiac disease deaths [1, 2], affecting more than 300,000 victims per year in the United States and around 275,000 in the Europe [3–5]. About 80% of sudden cardiac deaths are caused by out-of-hospital cardiac arrests (OHCA) [1], defined as the sudden cessation of the heart's effective pumping function confirmed by the absence of pulse and breathing

During OHCA, there are two prehospital life supporting emergency medical services (EMS): basic life support (BLS) and advanced life support (ALS). BLS treatment is provided by emergency medical technicians and includes early CPR and early defibrillation, usually delivered with an automated external defibrillator (AED). ALS treatment procured by clinicians during CPR usually includes manual defibrillation, advanced airway placement, and drug administration, together with

Several studies have reported a strong correlation between the quality of CPR and the chance of successful defibrillation [9–11]. Thus, resuscitation guidelines [12, 13] globally recommend providing chest compressions with a rate in the range of 100 and 120 compressions per minute (cpm) and achieving a depth between 5 and 6 cm. Ventilations should be provided with a 30 compressions-to-2 ventilations ratio before intubation. After intubation, ALS guidelines recommend continuous chest compressions and ventilations with a ventilation rate around 10 breaths per minute [7, 8]. Despite the fact that some studies have declared hyperventilation as harmful for patient outcome, by either high rate or volume [14, 15], excessive ventilation rates (as high as 30 breaths per minute) are common in resuscitation [16–18]. Many animal studies revealed that high ventilation rates increased intrathoracic pressures and decreased coronary perfusion and survival rates [16, 19, 20]. However, another recent animal study reported no adverse hemodynamic effects,

although they did observe a decrease in maximum CO2 values [21].

(ROSC) [23], and determination of patient prognosis [7, 24, 25].

In order to alleviate this problem and prevent inadvertent hyperventilation, resuscitation guidelines highlight the role of capnography for ventilation rate monitoring during CPR [7, 8]. Other advantages of capnography include assessment of the correct placement of the endotracheal tube [21], monitoring the quality of chest compressions [22], early identification of restoration of spontaneous circulation

This chapter analyzes the use of capnometry for ventilation monitoring during OHCA episodes. First, we briefly introduce the evolution of capnometry and the different technologies used in the field. Then, we characterize the capnography signal during ongoing CPR. The main conclusion of this analysis is that the appearance of high-frequency oscillations superimposed on the waveform capnography is frequent during resuscitation. We then analyze the impact of these oscillations on out-of-hospital automated detection of ventilations. Finally, we propose two methods to improve ventilation detection during CPR by filtering the artifact from the capnography signal and a method to enhance capnography waveform in the

Since 1943, capnometry has become an essential component of standard anesthesia monitoring [26]. Capnometry represents the numerical value of the carbon dioxide (CO2) partial pressure measurement in exhaled respiratory gases. The maximum CO2 concentration at the end of the exhalation, known as end-tidal CO2 (ETCO2), reflects cardiac output and pulmonary blood flow. Preventing hypoxia, i.e., deprivation of adequate oxygen supply, during anesthesia is the primary goal of

**24**

presence of artifact.

**2. Evolution of capnometry**

*Evolution of capnometry in out-of-hospital emergency settings. (a) Semiquantitative capnometer, (b) quantitative capnometer, and (c) waveform capnography. Courtesy of Medtronic and Masimo.*

**Figure 2.**

*Brief schemes of quantitative capnometry to acquire the capnography signal, mainstream and sidestream.*

#### **Figure 3.**

*The normal capnogram. Capnography waveform representing the variation of CO2 concentration during the respiratory cycle. Segments and phases follow the nomenclature proposed by Bhavani-Shankar and Philip [27].*

the IR emitter exactly matches the absorption spectrum of the CO2 molecules. This facilitates the sample cell to use a much smaller volume that permits a low flow rate, being less likely to aspirate water and secretions.

The evolution and morphology of CO2 concentration in the respiratory cycle of a normal capnogram are depicted in **Figure 3**. The initial rapid decrease of CO2 concentration named as phase 0 represents the inspiration segment, where the lungs are filled with CO2-free respiratory gases until a zero level is reached, defining the baseline of the capnogram. The following phases represent the expiration segment: during phase I, the CO2-free gas in the anatomical dead space (between the alveoli and measurement device) is exhaled; in phase II a mixture of gases from the anatomical dead space and the alveoli quickly rises the level of CO2 concentration; finally in phase III, CO2-rich gases coming from the alveoli slowly raise the CO2 concentration until a peak level is reached, corresponding to the ETCO2 value [28].

## **3. Capnography signal during ongoing chest compressions**

The initial use of capnographs during resuscitation was initially proposed by the International Liaison Committee on Resuscitation (ILCOR) in 2010, and since 2015 it is becoming a standard of care in advanced high-quality CPR [24, 29, 30]. Among the several advantages of waveform capnography during CPR emphasized in current resuscitation guidelines, but one of its most important roles is to monitor ventilation rate, helping to avoid overventilation.

For a reliable clinical analysis, either visual or automated, of the waveform capnography, its morphology is essential. All phases of the respiratory cycle must by identifiable during CPR, and the measurement of ETCO2 should be possible. However, issues related to the capnograph as well as to the ongoing resuscitation efforts may distort the waveform capnography [29, 31, 32]. Moreover, the appearance of fast oscillations induced in the waveform capnography at different rates and with varying amplitude has been reported in several studies [33–35], often completely distorting the real tracing of the respiratory cycle as shown in **Figure 4b**.

To the best of our knowledge, studies assessing the incidence and origin of this artifact are sparse. A preliminary abstract published by Idris et al. [33] in 2010 analyzed a dataset of 210 patients and detected the presence of this artifact in 154 episodes, reporting an incidence greater than 70%. Several studies found that provided chest compressions generate passive ventilations of low inspiratory tidal volumes [33–35]. Deakin et al. [34] found that generated low tidal volumes during ongoing chest compressions were considerably lower than the anatomical dead space (150 ml). Recently, Vanwulpen et al. [35] conducted a similar out-of-hospital study, and their results were in line with the ones reported by Deakin et al., but they found lower inspiratory volumes. Therefore generated gas exchange is insufficient to properly ventilate the patient [36].

**27**

capnography interpretation.

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

*OHCA waveform capnography signal segments. (a) Nondistorted waveform and (b) capnogram distorted by* 

Our first approach was to assess the origin of the artifact, so we performed timedomain and spectral analyses on a large set of out-of-hospital capnograms. Readers are encouraged to consult reference [37] for further details. As an example, **Figure 5** depicts a distorted capnogram interval (top-left panel), the concurrent chest compression depth (CD) signal (bottom-left panel), and the normalized power spectral density (PSD) estimated (right panel) for both the waveform capnography signal (solid blue) and for the CD signal (dotted red). The PSD analysis of the waveform capnography reveals a low-frequency peak that represents the ventilation rate (shadowed in gray) and a high-frequency peak corresponding to the artifact oscillation frequency. The latter exactly overlaps with the fundamental frequency peak of the CD signal. Thus, the induced artifact presents a sinusoidal pattern with a fundamental frequency that matches the frequency of the chest compressions. The appearance of the artifact induced by chest compressions can negatively affect the quality of CPR in three different aspects: first, causing misdetection of ventilations and consequently giving an incorrect feedback in the estimation of ventilation rate; second, impeding reliable and stable ETCO2 measurements as reported by Raimondi et al. [38]; and third, interfering with CPR providers' waveform

*Time-domain and spectral analyses of the oscillations present in a capnogram segment (top left). CD signal (bottom left). Normalized PSD analysis (right) of the distorted capnogram (solid blue) and of the CD signal (dotted red). The high-frequency peak around 2 Hz matches the average chest compression rate of 116* 

**4. Impact of chest compression artifact on ventilation detection**

This section briefly describes the conducted analysis to characterize the morphology of the chest compression-induced oscillations and assess its impact on automated ventilation detection during ongoing CPR. First, we describe the process followed to collect the OHCA episodes used in the study, as well as the steps followed to annotate each ventilation instance. Then, we describe an algorithm designed to automatically detect ventilations in the capnogram. Finally, we assess

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

**Figure 4.**

**Figure 5.**

*compressions per minute.*

*fast oscillations.*

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

**Figure 4.**

*Cardiac Diseases and Interventions in 21st Century*

being less likely to aspirate water and secretions.

**Figure 3.**

ventilation rate, helping to avoid overventilation.

the IR emitter exactly matches the absorption spectrum of the CO2 molecules. This facilitates the sample cell to use a much smaller volume that permits a low flow rate,

*The normal capnogram. Capnography waveform representing the variation of CO2 concentration during the respiratory cycle. Segments and phases follow the nomenclature proposed by Bhavani-Shankar and Philip [27].*

The evolution and morphology of CO2 concentration in the respiratory cycle of a normal capnogram are depicted in **Figure 3**. The initial rapid decrease of CO2 concentration named as phase 0 represents the inspiration segment, where the lungs are filled with CO2-free respiratory gases until a zero level is reached, defining the baseline of the capnogram. The following phases represent the expiration segment: during phase I, the CO2-free gas in the anatomical dead space (between the alveoli and measurement device) is exhaled; in phase II a mixture of gases from the anatomical dead space and the alveoli quickly rises the level of CO2 concentration; finally in phase III, CO2-rich gases coming from the alveoli slowly raise the CO2 concentration until a peak level is reached, corresponding to the ETCO2 value [28].

The initial use of capnographs during resuscitation was initially proposed by the International Liaison Committee on Resuscitation (ILCOR) in 2010, and since 2015 it is becoming a standard of care in advanced high-quality CPR [24, 29, 30]. Among the several advantages of waveform capnography during CPR emphasized in current resuscitation guidelines, but one of its most important roles is to monitor

For a reliable clinical analysis, either visual or automated, of the waveform capnography, its morphology is essential. All phases of the respiratory cycle must by identifiable during CPR, and the measurement of ETCO2 should be possible. However, issues related to the capnograph as well as to the ongoing resuscitation efforts may distort the waveform capnography [29, 31, 32]. Moreover, the appearance of fast oscillations induced in the waveform capnography at different rates and with varying amplitude has been reported in several studies [33–35], often completely distorting the real tracing of the respiratory cycle as shown in **Figure 4b**. To the best of our knowledge, studies assessing the incidence and origin of this artifact are sparse. A preliminary abstract published by Idris et al. [33] in 2010 analyzed a dataset of 210 patients and detected the presence of this artifact in 154 episodes, reporting an incidence greater than 70%. Several studies found that provided chest compressions generate passive ventilations of low inspiratory tidal volumes [33–35]. Deakin et al. [34] found that generated low tidal volumes during ongoing chest compressions were considerably lower than the anatomical dead space (150 ml). Recently, Vanwulpen et al. [35] conducted a similar out-of-hospital study, and their results were in line with the ones reported by Deakin et al., but they found lower inspiratory volumes. Therefore

generated gas exchange is insufficient to properly ventilate the patient [36].

**3. Capnography signal during ongoing chest compressions**

**26**

*OHCA waveform capnography signal segments. (a) Nondistorted waveform and (b) capnogram distorted by fast oscillations.*

#### **Figure 5.**

*Time-domain and spectral analyses of the oscillations present in a capnogram segment (top left). CD signal (bottom left). Normalized PSD analysis (right) of the distorted capnogram (solid blue) and of the CD signal (dotted red). The high-frequency peak around 2 Hz matches the average chest compression rate of 116 compressions per minute.*

Our first approach was to assess the origin of the artifact, so we performed timedomain and spectral analyses on a large set of out-of-hospital capnograms. Readers are encouraged to consult reference [37] for further details. As an example, **Figure 5** depicts a distorted capnogram interval (top-left panel), the concurrent chest compression depth (CD) signal (bottom-left panel), and the normalized power spectral density (PSD) estimated (right panel) for both the waveform capnography signal (solid blue) and for the CD signal (dotted red). The PSD analysis of the waveform capnography reveals a low-frequency peak that represents the ventilation rate (shadowed in gray) and a high-frequency peak corresponding to the artifact oscillation frequency. The latter exactly overlaps with the fundamental frequency peak of the CD signal. Thus, the induced artifact presents a sinusoidal pattern with a fundamental frequency that matches the frequency of the chest compressions.

The appearance of the artifact induced by chest compressions can negatively affect the quality of CPR in three different aspects: first, causing misdetection of ventilations and consequently giving an incorrect feedback in the estimation of ventilation rate; second, impeding reliable and stable ETCO2 measurements as reported by Raimondi et al. [38]; and third, interfering with CPR providers' waveform capnography interpretation.

## **4. Impact of chest compression artifact on ventilation detection**

This section briefly describes the conducted analysis to characterize the morphology of the chest compression-induced oscillations and assess its impact on automated ventilation detection during ongoing CPR. First, we describe the process followed to collect the OHCA episodes used in the study, as well as the steps followed to annotate each ventilation instance. Then, we describe an algorithm designed to automatically detect ventilations in the capnogram. Finally, we assess

the impact of the artifact on the reliability of ventilation detection by testing the performance of the detection algorithm. For a more detailed description of the database used and the methods followed, see Ref. [37].

### **4.1 Data collection and annotation**

In order to perform the analysis, a dataset of 301 episodes was selected from a large database collected between 2011 and 2016 as part of the Resuscitation Outcomes Consortium (ROC), collected by the Portland Regional Clinical Centre (Oregon, USA). The data collection was approved by the Oregon Health and Science University (OHSU) Institutional Review Board (IRB00001736). No patient private data was required for this study. Episodes were recorded using Heartstart MRx monitor-defibrillators (Philips, USA), equipped with real-time CPR feedback technology (Q-CPR) and sidestream waveform capnography (Microstream, Oridion Systems Ltd., Israel). Ventilation was provided with a bag valve mask (BVM), endotracheal tube (ETT), or the King LT-D supraglottic airway (SGA).

Three biomedical engineers participating in the study visually reviewed and manually annotated each OHCA episode. Episodes were classified as distorted if evident chest compression-induced oscillations were found during more than 1 min of the total chest compression time. In the case of distorted episodes, experts annotated the location of the artifact with respect to the capnogram segment to characterize its morphology. Otherwise, episodes were grouped as nondistorted. Episodes and intervals with unreliable data caused by excessive noise or disconnections were discarded.

**Figure 6** shows an example of the ventilation annotation process. The compression depth (CD) signal (top panel) was used to determine whether chest compressions were provided or not. The position of each single ventilation was also annotated using the TI signal as a reference. Provided ventilations provoke slow fluctuations in the TI signal [39–41]. The raw TI signal was low-pass filtered to enhance the slow fluctuations caused by ventilations (middle panel, blue line). Each ventilation was annotated at the instant corresponding to a rise in each impedance

#### **Figure 6.**

*CD signal measured with Q-CPR technology (top panel), raw TI signal acquired through defibrillation pads (middle panel, gray line) and waveform capnography (bottom panel). Using the low-pass filtered TI signal (middle panel, blue line), ventilations were annotated at the rise of a TI fluctuation, corresponding with a CO2 rapid decay to zero.*

**29**

**Figure 7.**

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

fluctuation (vertical red line). The capnogram depicted in the bottom panel allows visual confirmation of the presence of ventilations. Resulting annotations were used as the gold standard to evaluate the performance of an automated capnogram-based

There is remarkably little knowledge about how the proprietary algorithm of a commercial capnometer works. In 2010, Edelson et al. [41] proposed the first algorithm to automatically detect ventilations in the capnogram during CPR. For this study, we designed a new algorithm for ventilation detection, based upon certain

A simplified scheme of the algorithm performance is shown in **Figure 7**. The algorithm searches for series of consecutive upstrokes (*t*up) and downstrokes (*t*dw) in the capnogram. These abrupt changes are detected when the amplitude of the capnogram exceeds or goes below a fixed threshold, *Th*amp (mmHg). Then the algorithm extracts two features, the duration between consecutive abrupt changes, considered as an estimation of expiration and inspiration intervals, *D*ex and *D*in. Classification of potential true ventilations is done according to a simple decision tree based on *Th*ex and *Th*in thresholds. If both duration features are greater than these thresholds,

the ventilation is annotated at the instant when the downstroke occurs (*t*dw).

The performance of the algorithm was evaluated in terms of its sensitivity (Se) and positive predictive value (PPV). Se was defined as the proportion of annotated ventilations that were identified by the algorithm and PPV as the proportion of detected ventilations that were true ventilations. Ventilation detection instances were matched with the gold standard annotations if they were within ±0.5 s of one another. The algorithm was first trained with a subset of 30 nondistorted episodes obtaining the maximizing Se while assuring a PPV >98%. Ventilation detection performance was reported for the remaining episodes (test set), consisting of a

In order to assess how the ventilation rate estimation is influenced by the chest compression artifact, we computed, for each episode in the whole set, the number of ventilations given during every minute, using a 1-minute sliding window with an

*The ventilation detection scheme is described in the top panel. Applying a fixed amplitude threshold Thamp, the algorithm searches consecutive upstrokes (tup) and downstrokes (tdw) in the waveform capnography signal (bottom-left panel). Then, it extracts the duration of the intervals Dex and Din. Finally, features greater than the fixed duration thresholds Thex and Thin are classified as true ventilations. Detected ventilations are depicted* 

**4.2 Method for an automated capnogram-based ventilation detection**

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

assumptions about the nature of the CO2 waveform.

mixture of distorted and nondistorted episodes.

*with vertical red dotted lines (bottom-right panel).*

ventilation detection algorithm.

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

fluctuation (vertical red line). The capnogram depicted in the bottom panel allows visual confirmation of the presence of ventilations. Resulting annotations were used as the gold standard to evaluate the performance of an automated capnogram-based ventilation detection algorithm.

#### **4.2 Method for an automated capnogram-based ventilation detection**

There is remarkably little knowledge about how the proprietary algorithm of a commercial capnometer works. In 2010, Edelson et al. [41] proposed the first algorithm to automatically detect ventilations in the capnogram during CPR. For this study, we designed a new algorithm for ventilation detection, based upon certain assumptions about the nature of the CO2 waveform.

A simplified scheme of the algorithm performance is shown in **Figure 7**. The algorithm searches for series of consecutive upstrokes (*t*up) and downstrokes (*t*dw) in the capnogram. These abrupt changes are detected when the amplitude of the capnogram exceeds or goes below a fixed threshold, *Th*amp (mmHg). Then the algorithm extracts two features, the duration between consecutive abrupt changes, considered as an estimation of expiration and inspiration intervals, *D*ex and *D*in. Classification of potential true ventilations is done according to a simple decision tree based on *Th*ex and *Th*in thresholds. If both duration features are greater than these thresholds, the ventilation is annotated at the instant when the downstroke occurs (*t*dw).

The performance of the algorithm was evaluated in terms of its sensitivity (Se) and positive predictive value (PPV). Se was defined as the proportion of annotated ventilations that were identified by the algorithm and PPV as the proportion of detected ventilations that were true ventilations. Ventilation detection instances were matched with the gold standard annotations if they were within ±0.5 s of one another. The algorithm was first trained with a subset of 30 nondistorted episodes obtaining the maximizing Se while assuring a PPV >98%. Ventilation detection performance was reported for the remaining episodes (test set), consisting of a mixture of distorted and nondistorted episodes.

In order to assess how the ventilation rate estimation is influenced by the chest compression artifact, we computed, for each episode in the whole set, the number of ventilations given during every minute, using a 1-minute sliding window with an

#### **Figure 7.**

*Cardiac Diseases and Interventions in 21st Century*

**4.1 Data collection and annotation**

tions were discarded.

database used and the methods followed, see Ref. [37].

the impact of the artifact on the reliability of ventilation detection by testing the performance of the detection algorithm. For a more detailed description of the

In order to perform the analysis, a dataset of 301 episodes was selected from a large database collected between 2011 and 2016 as part of the Resuscitation Outcomes Consortium (ROC), collected by the Portland Regional Clinical Centre (Oregon, USA). The data collection was approved by the Oregon Health and Science University (OHSU) Institutional Review Board (IRB00001736). No patient private data was required for this study. Episodes were recorded using Heartstart MRx monitor-defibrillators (Philips, USA), equipped with real-time CPR feedback technology (Q-CPR) and sidestream waveform capnography (Microstream, Oridion Systems Ltd., Israel). Ventilation was provided with a bag valve mask (BVM), endotracheal tube (ETT), or the King LT-D supraglottic airway (SGA).

Three biomedical engineers participating in the study visually reviewed and manually annotated each OHCA episode. Episodes were classified as distorted if evident chest compression-induced oscillations were found during more than 1 min of the total chest compression time. In the case of distorted episodes, experts annotated the location of the artifact with respect to the capnogram segment to characterize its morphology. Otherwise, episodes were grouped as nondistorted. Episodes and intervals with unreliable data caused by excessive noise or disconnec-

**Figure 6** shows an example of the ventilation annotation process. The compression depth (CD) signal (top panel) was used to determine whether chest compressions were provided or not. The position of each single ventilation was also annotated using the TI signal as a reference. Provided ventilations provoke slow fluctuations in the TI signal [39–41]. The raw TI signal was low-pass filtered to enhance the slow fluctuations caused by ventilations (middle panel, blue line). Each ventilation was annotated at the instant corresponding to a rise in each impedance

*CD signal measured with Q-CPR technology (top panel), raw TI signal acquired through defibrillation pads (middle panel, gray line) and waveform capnography (bottom panel). Using the low-pass filtered TI signal (middle panel, blue line), ventilations were annotated at the rise of a TI fluctuation, corresponding with a CO2*

**28**

**Figure 6.**

*rapid decay to zero.*

*The ventilation detection scheme is described in the top panel. Applying a fixed amplitude threshold Thamp, the algorithm searches consecutive upstrokes (tup) and downstrokes (tdw) in the waveform capnography signal (bottom-left panel). Then, it extracts the duration of the intervals Dex and Din. Finally, features greater than the fixed duration thresholds Thex and Thin are classified as true ventilations. Detected ventilations are depicted with vertical red dotted lines (bottom-right panel).*

overlap factor of 1/6. Hence, ventilation rate value was updated every 10 s. Then, we compared the ventilation rate measurements estimated from the ventilation detections with those computed from the gold standard annotations. Using the computed ventilation rate per minute measurements, we also calculated the overventilation alarms obtained for a 10 min<sup>−</sup><sup>1</sup> threshold. Then, we tested the ability of our algorithm to correctly detect overventilation.

Results were reported as mean (±SD) if they passed Lilliefors normality test and as median (IQR) otherwise. Distribution of Se and PPV per record and distributions of the percent error in the estimation of ventilation rate were depicted with box plots, which graphically report median, IQR, and possible outlier values.

## **4.3 Characterization of chest compression artifact and ventilation detection performance**

From the original dataset of 301 episodes, 23% were discarded (69 records) due to unreliable waveform capnography or TI signals. Permanent signal disconnection or saturation, capnograms without respiratory cycle variations or under 5 mmHg during the whole episode (32 records), and inability to observe ventilation fluctuations in the filtered TI signal (20 records) were some of the reasons for elimination. Remaining 232 episodes had a mean duration of 30 (±9.5) min per episode.

A total of 98 episodes (42%) were annotated as distorted. The artifact was classified into three types: type I, observed primarily during the expiratory plateau; type II, in the capnogram baseline; and type III, spanning from the plateau to the baseline. No induced chest compression oscillations were found in the slopes of phases 0 and II. **Figure 8** depicts, for each artifact type, examples of capnogram intervals observed during ongoing chest compressions. The ventilation annotation process yielded a total of 52,654 ventilation instances, with a mean of 227 (±118) ventilations per episode. Nondistorted episodes comprised 30,814 ventilations and distorted episodes 21,840 ventilations (type I, 10,119; type II, 5228; and type III, 6493).

Global Se was 96.4% and PPV was 95.0% for the whole test subset. Reported performance for nondistorted episodes was higher, Se was 99.6%, and PPV was 99.0%. However, performance decreased for the distorted subset, with values of Se and PPV of 91.9 and 89.5%, respectively. This phenomenon is highly noticeable in the case of type III episodes, where performance was drastically affected by the artifact, reporting values of Se and PPV of 77.6 and 73.5%, respectively. **Figure 9** (left panels) shows the performance results of the automated ventilation detection. **Figure 9** (right panel) shows the distribution of the unsigned percent error in the estimation of ventilation rate per episode. For the nondistorted episodes, median error was 0.9 (0–1.9)%. For the distorted subset, error was 6.3 (1.7–16.9)%. For type III episodes, error increased to 19.6 (7.7–40.3)%.

**Table 1** shows the relation between the artifact type and the airway system, and the algorithm performance in the detection of overventilation alarms. Overall, type I artifact appeared in 48% of the distorted cases, type II in 21%, and type III in 31%

#### **Figure 8.**

*Intervals of chest compression oscillations observed in OHCA capnograms during ongoing CPR: type I, located in the plateau; type II, located in the baseline; type III, spanning from the plateau to the baseline.*

**31**

with respect to PPV.

*overventilation alarms.*

*overventilation alarms.*

**Table 1.**

**4.4 Discussion**

**Figure 9.**

classification.

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

*Automated ventilation detection performance and error in the estimation of ventilation rate. Results are* 

**Group Total Ventilation type Gold standard Alarm detection**

Total 232 7 149 73 3 31,760 17,901 99.1 92.6 Non-D 134 7 90 35 2 17,413 10,511 99.7 98.0 Distorted 98 0 59 38 1 14,347 7390 98.2 85.8 Type I 47 0 19 28 0 7167 3398 98.9 90.8 Type II 21 0 15 6 0 2826 1837 99.8 96.6 Type III 30 0 25 4 1 4354 2155 95.5 72.1 *Non-D, nondistorted; BVM, bag valve mask; ETT, endotracheal tube; SGA, supraglottic airway; NA, not available; nvr , number of ventilation rate per minute measurements annotated in the gold standard; nov , number of annotated* 

**BVM ETT SGA NA** *n***vr** *n***ov Se (%) PPV (%)**

*provided for all categories: non-D, nondistorted; I, type I; II, type II; and III, type III.*

of them. Artifact was not present where BVM ventilation was used, although the sample was small. However, all types of artifact appeared in both advanced airways, with a higher incidence for SGA cases. In ETT cases, incidence of type III artifact

*Distribution of episodes according to artifact and airway type and algorithm performance in the detection of* 

There was a 56.4% (17,901/31,760) of 1-minute overventilation annotated intervals. Overventilation was accurately detected in the case of nondistorted episodes, but performance decreased in the distorted group (type III), particularly

There is a lack of evidence about the incidence and origin of the chest compression artifact. One prior study has reported the impact of these induced oscillations on the capnogram during OHCA CPR. In this work, published as a conference abstract, Idris et al. [33] reported the appearance of oscillations in 154 episodes from a total of 210 OHCA records (73.3%). In our study, with a similar number of OHCA episodes (232 vs. 210), we found a lower incidence of distorted capnograms (42%). This could be explained by a different criterion for distorted episode

Ventilation rate guidance is one of the emphasized advantages of capnography during OCHA episodes. However, the presence of fast oscillations in the capnogram during ongoing CPR may limit rescuers since distorted capnograms are difficult to interpret. Performed analyses demonstrated the negative impact of this artifact

was more prevalent, whereas in SGA cases, type I was more prevalent.

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

#### **Figure 9.**

*Cardiac Diseases and Interventions in 21st Century*

rithm to correctly detect overventilation.

III episodes, error increased to 19.6 (7.7–40.3)%.

alarms obtained for a 10 min<sup>−</sup><sup>1</sup>

**performance**

overlap factor of 1/6. Hence, ventilation rate value was updated every 10 s. Then, we compared the ventilation rate measurements estimated from the ventilation detections with those computed from the gold standard annotations. Using the computed ventilation rate per minute measurements, we also calculated the overventilation

Results were reported as mean (±SD) if they passed Lilliefors normality test and as median (IQR) otherwise. Distribution of Se and PPV per record and distributions of the percent error in the estimation of ventilation rate were depicted with box plots, which graphically report median, IQR, and possible outlier values.

From the original dataset of 301 episodes, 23% were discarded (69 records) due to unreliable waveform capnography or TI signals. Permanent signal disconnection or saturation, capnograms without respiratory cycle variations or under 5 mmHg during the whole episode (32 records), and inability to observe ventilation fluctuations in the filtered TI signal (20 records) were some of the reasons for elimination.

Global Se was 96.4% and PPV was 95.0% for the whole test subset. Reported performance for nondistorted episodes was higher, Se was 99.6%, and PPV was 99.0%. However, performance decreased for the distorted subset, with values of Se and PPV of 91.9 and 89.5%, respectively. This phenomenon is highly noticeable in the case of type III episodes, where performance was drastically affected by the artifact, reporting values of Se and PPV of 77.6 and 73.5%, respectively. **Figure 9** (left panels) shows the performance results of the automated ventilation detection. **Figure 9** (right panel) shows the distribution of the unsigned percent error in the estimation of ventilation rate per episode. For the nondistorted episodes, median error was 0.9 (0–1.9)%. For the distorted subset, error was 6.3 (1.7–16.9)%. For type

**Table 1** shows the relation between the artifact type and the airway system, and the algorithm performance in the detection of overventilation alarms. Overall, type I artifact appeared in 48% of the distorted cases, type II in 21%, and type III in 31%

*Intervals of chest compression oscillations observed in OHCA capnograms during ongoing CPR: type I, located* 

*in the plateau; type II, located in the baseline; type III, spanning from the plateau to the baseline.*

**4.3 Characterization of chest compression artifact and ventilation detection** 

Remaining 232 episodes had a mean duration of 30 (±9.5) min per episode. A total of 98 episodes (42%) were annotated as distorted. The artifact was classified into three types: type I, observed primarily during the expiratory plateau; type II, in the capnogram baseline; and type III, spanning from the plateau to the baseline. No induced chest compression oscillations were found in the slopes of phases 0 and II. **Figure 8** depicts, for each artifact type, examples of capnogram intervals observed during ongoing chest compressions. The ventilation annotation process yielded a total of 52,654 ventilation instances, with a mean of 227 (±118) ventilations per episode. Nondistorted episodes comprised 30,814 ventilations and distorted episodes 21,840 ventilations (type I, 10,119; type II, 5228; and type III,

threshold. Then, we tested the ability of our algo-

**30**

**Figure 8.**

6493).

*Automated ventilation detection performance and error in the estimation of ventilation rate. Results are provided for all categories: non-D, nondistorted; I, type I; II, type II; and III, type III.*


*Non-D, nondistorted; BVM, bag valve mask; ETT, endotracheal tube; SGA, supraglottic airway; NA, not available; nvr , number of ventilation rate per minute measurements annotated in the gold standard; nov , number of annotated overventilation alarms.*

#### **Table 1.**

*Distribution of episodes according to artifact and airway type and algorithm performance in the detection of overventilation alarms.*

of them. Artifact was not present where BVM ventilation was used, although the sample was small. However, all types of artifact appeared in both advanced airways, with a higher incidence for SGA cases. In ETT cases, incidence of type III artifact was more prevalent, whereas in SGA cases, type I was more prevalent.

There was a 56.4% (17,901/31,760) of 1-minute overventilation annotated intervals. Overventilation was accurately detected in the case of nondistorted episodes, but performance decreased in the distorted group (type III), particularly with respect to PPV.

#### **4.4 Discussion**

There is a lack of evidence about the incidence and origin of the chest compression artifact. One prior study has reported the impact of these induced oscillations on the capnogram during OHCA CPR. In this work, published as a conference abstract, Idris et al. [33] reported the appearance of oscillations in 154 episodes from a total of 210 OHCA records (73.3%). In our study, with a similar number of OHCA episodes (232 vs. 210), we found a lower incidence of distorted capnograms (42%). This could be explained by a different criterion for distorted episode classification.

Ventilation rate guidance is one of the emphasized advantages of capnography during OCHA episodes. However, the presence of fast oscillations in the capnogram during ongoing CPR may limit rescuers since distorted capnograms are difficult to interpret. Performed analyses demonstrated the negative impact of this artifact

in the detection of ventilations. Se and PPV were above 95%, and ventilation rate estimation errors were minimal for all the nondistorted episodes, but detection performance significantly decreased in the presence of oscillations. Thus, a reliable ventilation guidance would not be feasible for those OHCA patients.

Overventilation was common in our database: 56.4% of the annotated ventilation rates were above the recommended 10 breaths per minute. Sensitivity for alarm detection was high for all episodes (nondistorted and distorted). However, the algorithm showed a tendency to overestimate ventilation rate in the presence of chest compression oscillations, where PPV values were low. Induced oscillations spanning from the plateau to the baseline impeded a reliable detection of true ventilation CO2 concentration changes. Hence, the presence of artifact in the waveform capnography caused many false ventilation detections.

## **5. Suppression of chest compression artifact during CPR**

In Section 2 we quantitatively confirmed the nature of the oscillations, with a single frequency matching the chest compression rate, suggesting that the artifact is directly caused by ongoing chest compressions during CPR. In this context, we hypothesized that automatic ventilation detection would improve if the oscillations induced by chest compressions could be successfully removed from the capnogram. Our next step was designing chest compression artifact suppression techniques, exploring different alternatives.

#### **5.1 Frequency domain filtering techniques**

The following section describes the filtering techniques used for the suppression of the chest compression oscillations induced in the capnogram. We studied three different alternatives: a simple fixed-coefficient filter and two computationally intensive adaptive filtering techniques. To assess the goodness of the filter, we computed the performance using an automated capnogram-based ventilation detector after filtering OHCA capnograms. We also evaluated the improvement in ventilation rate measurement and in overventilation alarm detection. Then, we compared these results with those obtained before filtering, described previously in Section 4.4.

### *5.1.1 Fixed-coefficient filter*

The spectral analysis performed on OHCA capnograms (see Section 3, **Figure 5**) suggests that a sensible strategy to suppress the oscillations induced by chest compressions in the capnogram would be to use a simple fixed-coefficient filter that suppresses the spectral content of the capnogram above 1 Hz (compression rate above 60 cpm). To that end, after analyzing the spectral characteristics of several waveform capnography and CD signals, we developed a digital infinite impulse response low-pass Butterworth filter (8th order, cutoff frequency of 1.5 Hz).

#### *5.1.2 Adaptive filtering*

Efficacy of the fixed-coefficient filter may be affected by the variability of chest compression and ventilation rates during CPR [17, 18, 30, 42]. In the literature, filters adjusted in time, according to the varying characteristics of the artifact, have been extensively used to suppress oscillations in the electrocardiogram induced by chest compressions [43–46]. In this study, we designed two adaptive filtering

**33**

**Figure 10.**

*artifact; and III, type III artifact.*

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

configurations, an open-loop and a closed-loop adaptive filter [47]. Both techniques used the annotated chest compression instances, obtained from the CD signal as a reference to adjust the parameters of the adaptive filters. To do so, chest compression instances were annotated at the local minima as shown in **Figure 6** (top panel), corresponding to the maximum depth achieved for each chest compression. For

*Open-loop adaptive filter*. This technique is based on the adaptive adjustment of a stop-band Butterworth filter whose central frequency is adaptively adjusted to the chest compression rate. Average chest compression rate was estimated in 2-s nonoverlapped windows, using the annotated chest compression instances. Thus,

*Closed-loop adaptive filter*. In our approach, the required reference signal was modeled as a pure cosine wave of time-varying amplitude and phase, estimating the instantaneous chest compression rate from the chest compression instances. In this configuration, the artifact is adaptively estimated and subtracted from the capnogram, resulting in an equivalent notch filter capable of adaptively tracking the chest

The three proposed filter schemes performed similarly, reporting favorable global Se and PPV values well above 97 and 96%, respectively, for the distorted episodes, and maintaining the performance for nondistorted episodes. For this reason, and trying to keep this section as simple as possible, results for the closed-loop filter are reported. These results are representative of the three approaches. Readers are

Globally, Se/PPV improved from 96.4/95.0% before filtering to 98.2/98.3%. Performance improvement for type III episodes was remarkably higher, with Se/ PPV improving from a low 77.6/73.5% to 95.5/95.5%. **Figure 10** (left panels) shows, for each artifact type, the distribution of Se and PPV per episode, before and after filtering. In the case of type III episodes, the high dispersion in performance was drastically reduced after artifact suppression. Box plots in **Figure 10** (right panel) show the distribution of error in the estimation of ventilation rate before and after filtering. In the same way, estimation error for type III episodes noticeably

**Table 2** shows the performance improvement in the detection of overventilation. Globally, Se/PPV improved from 99.1/92.6% before filtering to 97.9/98.0%

*Se and PPV distribution per episode before and after filtering (left). Distribution of the unsigned error in the ventilation rate estimation (right). Results are provided for each artifact category: I, type I artifact; II, type II* 

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

more details of the adaptive filters, see Ref. [48].

filter parameters were updated every 2 s.

compression oscillation frequency.

encouraged to see full results in Ref. [48].

decreased after filtering.

*5.1.3 Results*

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

configurations, an open-loop and a closed-loop adaptive filter [47]. Both techniques used the annotated chest compression instances, obtained from the CD signal as a reference to adjust the parameters of the adaptive filters. To do so, chest compression instances were annotated at the local minima as shown in **Figure 6** (top panel), corresponding to the maximum depth achieved for each chest compression. For more details of the adaptive filters, see Ref. [48].

*Open-loop adaptive filter*. This technique is based on the adaptive adjustment of a stop-band Butterworth filter whose central frequency is adaptively adjusted to the chest compression rate. Average chest compression rate was estimated in 2-s nonoverlapped windows, using the annotated chest compression instances. Thus, filter parameters were updated every 2 s.

*Closed-loop adaptive filter*. In our approach, the required reference signal was modeled as a pure cosine wave of time-varying amplitude and phase, estimating the instantaneous chest compression rate from the chest compression instances. In this configuration, the artifact is adaptively estimated and subtracted from the capnogram, resulting in an equivalent notch filter capable of adaptively tracking the chest compression oscillation frequency.

### *5.1.3 Results*

*Cardiac Diseases and Interventions in 21st Century*

phy caused many false ventilation detections.

exploring different alternatives.

**5.1 Frequency domain filtering techniques**

in the detection of ventilations. Se and PPV were above 95%, and ventilation rate estimation errors were minimal for all the nondistorted episodes, but detection performance significantly decreased in the presence of oscillations. Thus, a reliable

Overventilation was common in our database: 56.4% of the annotated ventilation rates were above the recommended 10 breaths per minute. Sensitivity for alarm detection was high for all episodes (nondistorted and distorted). However, the algorithm showed a tendency to overestimate ventilation rate in the presence of chest compression oscillations, where PPV values were low. Induced oscillations spanning from the plateau to the baseline impeded a reliable detection of true ventilation CO2 concentration changes. Hence, the presence of artifact in the waveform capnogra-

In Section 2 we quantitatively confirmed the nature of the oscillations, with a single frequency matching the chest compression rate, suggesting that the artifact is directly caused by ongoing chest compressions during CPR. In this context, we hypothesized that automatic ventilation detection would improve if the oscillations induced by chest compressions could be successfully removed from the capnogram. Our next step was designing chest compression artifact suppression techniques,

The following section describes the filtering techniques used for the suppression of the chest compression oscillations induced in the capnogram. We studied three different alternatives: a simple fixed-coefficient filter and two computationally intensive adaptive filtering techniques. To assess the goodness of the filter, we computed the performance using an automated capnogram-based ventilation detector after filtering OHCA capnograms. We also evaluated the improvement in ventilation rate measurement and in overventilation alarm detection. Then, we compared these results with those obtained before filtering, described previously in

The spectral analysis performed on OHCA capnograms (see Section 3, **Figure 5**)

compressions in the capnogram would be to use a simple fixed-coefficient filter that suppresses the spectral content of the capnogram above 1 Hz (compression rate above 60 cpm). To that end, after analyzing the spectral characteristics of several waveform capnography and CD signals, we developed a digital infinite impulse response low-pass Butterworth filter (8th order, cutoff frequency of 1.5 Hz).

Efficacy of the fixed-coefficient filter may be affected by the variability of chest

compression and ventilation rates during CPR [17, 18, 30, 42]. In the literature, filters adjusted in time, according to the varying characteristics of the artifact, have been extensively used to suppress oscillations in the electrocardiogram induced by chest compressions [43–46]. In this study, we designed two adaptive filtering

suggests that a sensible strategy to suppress the oscillations induced by chest

ventilation guidance would not be feasible for those OHCA patients.

**5. Suppression of chest compression artifact during CPR**

**32**

Section 4.4.

*5.1.1 Fixed-coefficient filter*

*5.1.2 Adaptive filtering*

The three proposed filter schemes performed similarly, reporting favorable global Se and PPV values well above 97 and 96%, respectively, for the distorted episodes, and maintaining the performance for nondistorted episodes. For this reason, and trying to keep this section as simple as possible, results for the closed-loop filter are reported. These results are representative of the three approaches. Readers are encouraged to see full results in Ref. [48].

Globally, Se/PPV improved from 96.4/95.0% before filtering to 98.2/98.3%. Performance improvement for type III episodes was remarkably higher, with Se/ PPV improving from a low 77.6/73.5% to 95.5/95.5%. **Figure 10** (left panels) shows, for each artifact type, the distribution of Se and PPV per episode, before and after filtering. In the case of type III episodes, the high dispersion in performance was drastically reduced after artifact suppression. Box plots in **Figure 10** (right panel) show the distribution of error in the estimation of ventilation rate before and after filtering. In the same way, estimation error for type III episodes noticeably decreased after filtering.

**Table 2** shows the performance improvement in the detection of overventilation. Globally, Se/PPV improved from 99.1/92.6% before filtering to 97.9/98.0%

#### **Figure 10.**

*Se and PPV distribution per episode before and after filtering (left). Distribution of the unsigned error in the ventilation rate estimation (right). Results are provided for each artifact category: I, type I artifact; II, type II artifact; and III, type III artifact.*


*nvr , number of ventilation rate per minute measurements annotated in the gold standard; nov , number of annotated overventilation alarms.*

#### **Table 2.**

*Performance in the detection of overventilation alarms before and after filtering.*

after filtering. Although the improvement for the distorted group was noticeable in all cases, improvement was remarkably higher for type III episodes, with Se/PPV of 95.5/72.1% before and 94.8/94.2% after filtering.

A graphical example of the closed-loop filtering approach is illustrated in **Figure 11**. The raw capnogram is depicted by the solid gray line and the resulting waveform capnography after filtering by the solid blue superimposed to the raw capnogram. Each vertical dashed red line indicates a detection of ventilation given by the automated ventilation detector.

### *5.1.4 Discussion*

The presented filtering techniques were designed to preprocess the raw capnogram before applying the ventilation detection algorithm with the aim of improving automated ventilation detection. Although the closed-loop approach showed a great balance in Se and PPV improvement, none of the techniques showed a distinctive superiority in terms of performance. Since chest compression rates tend to vary during CPR, one could expect that adaptive filters would present better results than a simple fixed-coefficient filter, but this was not the case. This could be explained in part because chest compression rate is usually ten times greater than ventilation rate; thus spectral information is far away from one another. The selection of the filtering strategy could be analyzed in terms of simplicity and computational burden. Consequently, applying a simple fixedcoefficient filter to remove the chest compression artifact seems to be adequate.

As illustrated in **Figure 11**, resulting waveform capnography obtained after filtering approximates the mean peak-to-peak amplitude of the artifact. After

#### **Figure 11.**

*Example of filtering performance. Original capnogram with clean and distorted respiration cycles is depicted by the solid gray line. Filtered capnogram (in blue) superimposed to the original capnogram. Detected ventilations are depicted with vertical dashed red lines.*

**35**

**Figure 12.**

*ventilations are depicted with vertical red arrows.*

*5.2.2 Results*

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

filtering, output capnogram waveform hinders the ETCO2 measurement and a reliable analysis of ETCO2 trends. Thus, clinicians may still find the capnogram difficult to interpret. Removing the artifact to improve ventilation detection and at the same time preserving the capnogram tracing, which favors clinical interpretation,

In the previous section, we proposed a solution to suppress chest compression artifact from the waveform capnography using different filtering approaches. Although the automated detection of ventilations was improved, filtered capno-

This section explores an alternative method to remove chest compression oscillations from the waveform capnography signal. This technique was designed to improve ventilation detection focusing on the real tracing preservation. Again, performance metrics previously described in the chapter were used for quantitatively assessing the goodness of the method. This study was conducted using the

The principle of this artifact suppression technique relies on the hypothesis that the envelope of the waveform capnography signal could be a clinically reliable estimation of the CO2 concentration tracing produced by ventilations. Due to artifact morphology and location variability reported in Section 4, the algorithm determines how to extract the envelope of the waveform capnography dividing its

A graphical explanation of the method's performance is given in **Figure 12**. To extract the upper envelope of the capnogram (dashed blue line), the algorithm detects the local maxima values (downward arrowheads) during the plateau phase and applies a smoothing filter. Then, in order to extract the lower envelope (dotted blue line), local minima values (upward arrowheads) are detected during the capnogram baseline. A detailed explanation of the algorithm is provided in Ref. [49].

Globally, performance of the automated ventilation detection in terms of Se/PPV improved from 96.4/95.0% to 98.5/98.3% after artifact suppression.

*Chest compression artifact suppression example. A distorted capnogram interval is depicted by the gray line. The blue line illustrates the waveform capnography envelope extraction process. Upper envelope (dashed blue line) is extracted through the detection of each local maxima (downward arrowheads), and lower envelope (dotted blue line) is extracted through the detection of each local minima (upward arrowheads). Detected* 

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

require the development of new suppressing techniques.

operation into low and high CO2 concentration intervals.

**5.2 Time-domain artifact suppression technique**

grams were far from being clinically reliable.

test subset described in Section 4.4.

*5.2.1 Envelope detection algorithm*

## *Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

filtering, output capnogram waveform hinders the ETCO2 measurement and a reliable analysis of ETCO2 trends. Thus, clinicians may still find the capnogram difficult to interpret. Removing the artifact to improve ventilation detection and at the same time preserving the capnogram tracing, which favors clinical interpretation, require the development of new suppressing techniques.

### **5.2 Time-domain artifact suppression technique**

In the previous section, we proposed a solution to suppress chest compression artifact from the waveform capnography using different filtering approaches. Although the automated detection of ventilations was improved, filtered capnograms were far from being clinically reliable.

This section explores an alternative method to remove chest compression oscillations from the waveform capnography signal. This technique was designed to improve ventilation detection focusing on the real tracing preservation. Again, performance metrics previously described in the chapter were used for quantitatively assessing the goodness of the method. This study was conducted using the test subset described in Section 4.4.

### *5.2.1 Envelope detection algorithm*

The principle of this artifact suppression technique relies on the hypothesis that the envelope of the waveform capnography signal could be a clinically reliable estimation of the CO2 concentration tracing produced by ventilations. Due to artifact morphology and location variability reported in Section 4, the algorithm determines how to extract the envelope of the waveform capnography dividing its operation into low and high CO2 concentration intervals.

A graphical explanation of the method's performance is given in **Figure 12**. To extract the upper envelope of the capnogram (dashed blue line), the algorithm detects the local maxima values (downward arrowheads) during the plateau phase and applies a smoothing filter. Then, in order to extract the lower envelope (dotted blue line), local minima values (upward arrowheads) are detected during the capnogram baseline. A detailed explanation of the algorithm is provided in Ref. [49].

## *5.2.2 Results*

*Cardiac Diseases and Interventions in 21st Century*

95.5/72.1% before and 94.8/94.2% after filtering.

*Performance in the detection of overventilation alarms before and after filtering.*

automated ventilation detector.

*5.1.4 Discussion*

*overventilation alarms.*

**Table 2.**

after filtering. Although the improvement for the distorted group was noticeable in all cases, improvement was remarkably higher for type III episodes, with Se/PPV of

**Group Gold standard Before After**

Total 31,760 17,901 99.1 92.6 97.9 98.0 Nondistorted 17,413 10,511 99.7 98.0 98.9 98.9 Distorted 14,347 7390 98.2 85.8 96.3 96.6 Type I 7167 3398 98.9 90.8 98.0 97.0 Type II 2826 1837 99.8 96.6 95.2 98.3 Type III 4354 2155 95.5 72.1 94.8 94.2 *nvr , number of ventilation rate per minute measurements annotated in the gold standard; nov , number of annotated* 

*n***vr** *n***ov Se (%) PPV (%) Se (%) PPV (%)**

A graphical example of the closed-loop filtering approach is illustrated in **Figure 11**. The raw capnogram is depicted by the solid gray line and the resulting waveform capnography after filtering by the solid blue superimposed to the raw capnogram. Each vertical dashed red line indicates a detection of ventilation given by the

The presented filtering techniques were designed to preprocess the raw capnogram before applying the ventilation detection algorithm with the aim of improving automated ventilation detection. Although the closed-loop approach showed a great balance in Se and PPV improvement, none of the techniques showed a distinctive superiority in terms of performance. Since chest compression rates tend to vary during CPR, one could expect that adaptive filters would present better results than a simple fixed-coefficient filter, but this was not the case. This could be explained in part because chest compression rate is usually ten times greater than ventilation rate; thus spectral information is far away from one another. The selection of the filtering strategy could be analyzed in terms of simplicity and computational burden. Consequently, applying a simple fixedcoefficient filter to remove the chest compression artifact seems to be adequate. As illustrated in **Figure 11**, resulting waveform capnography obtained after filtering approximates the mean peak-to-peak amplitude of the artifact. After

*Example of filtering performance. Original capnogram with clean and distorted respiration cycles is depicted by the solid gray line. Filtered capnogram (in blue) superimposed to the original capnogram. Detected* 

**34**

**Figure 11.**

*ventilations are depicted with vertical dashed red lines.*

Globally, performance of the automated ventilation detection in terms of Se/PPV improved from 96.4/95.0% to 98.5/98.3% after artifact suppression.

#### **Figure 12.**

*Chest compression artifact suppression example. A distorted capnogram interval is depicted by the gray line. The blue line illustrates the waveform capnography envelope extraction process. Upper envelope (dashed blue line) is extracted through the detection of each local maxima (downward arrowheads), and lower envelope (dotted blue line) is extracted through the detection of each local minima (upward arrowheads). Detected ventilations are depicted with vertical red arrows.*

Performance for nondistorted episodes stayed stable, whereas Se/PPV for distorted episodes increased noticeably, from 91.9/89.5% to 98.0/97.3%. As it happens with previous filtering methods, performance improved more in type III episodes, with Se/PPV increasing from 77.6/73.5% to 97.1/96.1%. **Figure 13** (left panels) depicts trough box plots, for each artifact type, the distribution of Se and PPV per episode given by the automated ventilation detector. In general, median values of both performance metrics increased after artifact suppression, and dispersion was reduced for all groups. These improvements were more noticeable for type III episodes. Performance regarding ventilation rate estimation is shown in **Figure 13** (right panel), in which box plots depict the distribution of the error before and after applying the suppression method. Errors were reduced in all groups, but again, improvements in case of type III episodes were noticeably higher.

Results after artifact suppression in the detection of excessive ventilation rates are reported in **Table 3**. In this case, Se stayed almost stable with a higher increase for PPV values. For the distorted subset, Se/PPV was 98.8/86.7% before and 98.4/96.3% after suppressing the artifact, implying a reduction in false overventilation alarms. Once again, most remarkable results were obtained for type III episodes, with a slight increase in Se, but with PPV increasing from 73.9 to 93.6% after artifact suppression.

Finally, performance of the suppression method is illustrated in **Figure 14**. The raw capnogram is depicted by a solid gray line and the resulting waveform capnography by a solid blue line superimposed to the raw capnogram.

#### *5.2.3 Discussion*

Filtering methods to remove the oscillations from the capnogram, described in Section 4, improved ventilation detection accuracy. However, filtered capnograms do not accurately represent the CO2 concentration in intervals where the artifact appeared. In this section, a method that tries to preserve the waveform capnography has been proposed. Automated detection of ventilation instances, as well as estimation of ventilation rate and detection of overventilation, improved after artifact suppression. Results obtained with this method were similar or even better than the result reported for several filtering methods (Section 4).

The idea of "preserving the capnogram waveform" refers to the extraction of a clinically useful capnogram. We visually analyzed several capnogram segments in our database showing consecutive intervals with nondistorted and distorted ventilations (**Figure 14**). In most cases, the envelope of the distorted capnogram

#### **Figure 13.**

*Se and PPV distribution per episode before and after artifact suppression method (left). Distribution of the unsigned error in the ventilation rate estimation (right). Results are provided for each artifact category: I, type I artifact; II, type II artifact; and III, type III artifact.*

**37**

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

**Group Total Gold standard Before After**

Total 202 25,833 15,237 99.3 93.1 98.9 97.8 Nondistorted 119 14,889 8873 99.7 98.2 99.3 98.9 Distorted 83 10,944 6364 98.8 86.7 98.4 96.3 Type I 42 5823 2961 99.1 90.7 98.7 97.2 Type II 16 2160 1570 99.8 97.8 97.5 97.7 Type III 25 2961 1833 97.2 73.9 98.7 93.6 *nvr , number of ventilation rate per minute measurements annotated in the gold standard; nov , number of annotated* 

*n***vr** *n***ov Se (%) PPV (%) Se (%) PPV (%)**

resembled the CO2 tracing observed in the preceding and following undistorted respiratory cycles. Therefore, this method could enhance capnographs not account-

*Examples of artifact suppression method performance. Original capnogram with clean and distorted respiration cycles is depicted by the solid gray line. Filtered capnogram (in blue) superimposed to the original capnogram.*

*Overventilation alarm detection performance before and after applying the artifact suppression method.*

Current resuscitation guidelines emphasize the use of waveform capnography

This work received financial support from the Basque Government (Basque Country, Spain) through the project IT1087-16 and the predoctoral research grant

during CPR in order to enhance CPR quality and improve patient outcomes. However, the first study presented in this chapter showed that ventilation rate and overventilation prevention were compromised by the high incidence of chest compression artifact. The appearance of artifact during ongoing CPR is unpredictable, and thus suppression algorithms that continuously process the raw capnogram could be a great approach for waveform capnography enhancement. All artifact suppression approaches yielded good performance in terms of sensitivity and positive predictive value figures of merit. However, the time-domain alternative was the only one that enhanced the capnogram tracing, favoring its interpretation during CPR. The implementation of artifact suppression techniques in current capnographs could increase the use of capnography in OHCA episodes, which could

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

ing for the chest compression artifact effect.

in turn contribute to improving CPR quality.

**Acknowledgements**

PRE-2017-2-0201.

**6. Conclusions**

*overventilation alarms.*

**Table 3.**

**Figure 14.**

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*


*nvr , number of ventilation rate per minute measurements annotated in the gold standard; nov , number of annotated overventilation alarms.*

#### **Table 3.**

*Cardiac Diseases and Interventions in 21st Century*

artifact suppression.

*5.2.3 Discussion*

Performance for nondistorted episodes stayed stable, whereas Se/PPV for distorted episodes increased noticeably, from 91.9/89.5% to 98.0/97.3%. As it happens with previous filtering methods, performance improved more in type III episodes, with Se/PPV increasing from 77.6/73.5% to 97.1/96.1%. **Figure 13** (left panels) depicts trough box plots, for each artifact type, the distribution of Se and PPV per episode given by the automated ventilation detector. In general, median values of both performance metrics increased after artifact suppression, and dispersion was reduced for all groups. These improvements were more noticeable for type III episodes. Performance regarding ventilation rate estimation is shown in **Figure 13** (right panel), in which box plots depict the distribution of the error before and after applying the suppression method. Errors were reduced in all groups, but again,

Results after artifact suppression in the detection of excessive ventilation rates are reported in **Table 3**. In this case, Se stayed almost stable with a higher increase for PPV values. For the distorted subset, Se/PPV was 98.8/86.7% before and 98.4/96.3% after suppressing the artifact, implying a reduction in false overventilation alarms. Once again, most remarkable results were obtained for type III episodes, with a slight increase in Se, but with PPV increasing from 73.9 to 93.6% after

Finally, performance of the suppression method is illustrated in **Figure 14**. The raw capnogram is depicted by a solid gray line and the resulting waveform capnog-

Filtering methods to remove the oscillations from the capnogram, described in Section 4, improved ventilation detection accuracy. However, filtered capnograms do not accurately represent the CO2 concentration in intervals where the artifact appeared. In this section, a method that tries to preserve the waveform capnography has been proposed. Automated detection of ventilation instances, as well as estimation of ventilation rate and detection of overventilation, improved after artifact suppression. Results obtained with this method were similar or even better than the

The idea of "preserving the capnogram waveform" refers to the extraction of a clinically useful capnogram. We visually analyzed several capnogram segments in our database showing consecutive intervals with nondistorted and distorted ventilations (**Figure 14**). In most cases, the envelope of the distorted capnogram

*Se and PPV distribution per episode before and after artifact suppression method (left). Distribution of the unsigned error in the ventilation rate estimation (right). Results are provided for each artifact category: I, type I* 

improvements in case of type III episodes were noticeably higher.

raphy by a solid blue line superimposed to the raw capnogram.

result reported for several filtering methods (Section 4).

**36**

**Figure 13.**

*artifact; II, type II artifact; and III, type III artifact.*

*Overventilation alarm detection performance before and after applying the artifact suppression method.*

**Figure 14.**

*Examples of artifact suppression method performance. Original capnogram with clean and distorted respiration cycles is depicted by the solid gray line. Filtered capnogram (in blue) superimposed to the original capnogram.*

resembled the CO2 tracing observed in the preceding and following undistorted respiratory cycles. Therefore, this method could enhance capnographs not accounting for the chest compression artifact effect.

## **6. Conclusions**

Current resuscitation guidelines emphasize the use of waveform capnography during CPR in order to enhance CPR quality and improve patient outcomes. However, the first study presented in this chapter showed that ventilation rate and overventilation prevention were compromised by the high incidence of chest compression artifact. The appearance of artifact during ongoing CPR is unpredictable, and thus suppression algorithms that continuously process the raw capnogram could be a great approach for waveform capnography enhancement. All artifact suppression approaches yielded good performance in terms of sensitivity and positive predictive value figures of merit. However, the time-domain alternative was the only one that enhanced the capnogram tracing, favoring its interpretation during CPR. The implementation of artifact suppression techniques in current capnographs could increase the use of capnography in OHCA episodes, which could in turn contribute to improving CPR quality.

## **Acknowledgements**

This work received financial support from the Basque Government (Basque Country, Spain) through the project IT1087-16 and the predoctoral research grant PRE-2017-2-0201.

The authors thank the TVF&R emergency medical service providers for collecting the out-of-hospital cardiac arrest episodes used in this study.

## **Conflict of interest**

The authors declare no conflicts of interest.

## **Author details**

Mikel Leturiondo\*, Sofía Ruiz de Gauna, José Julio Gutiérrez, Digna M. González-Otero, Jesus M. Ruiz, Luis A. Leturiondo and Purificación Saiz University of the Basque Country (UPV/EHU), Bilbao, Spain

\*Address all correspondence to: mikel.leturiondo@ehu.eus

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**39**

2015;**95**:100-147

(18 suppl 2):S444-S464

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation…*

[9] Idris AH, Guffey D, Aufderheide TP, Brown S, Morrison LJ, Nichols P, et al. Relationship between chest compression rates and outcomes from cardiac arrest. Circulation. 2012;**125**:3004-3012

[10] Edelson DP, Abella BS, Kramer-Johansen J, Wik L, Myklebust H, Barry AM, et al. Effects of compression depth and pre-shock pauses predict defibrillation failure during cardiac arrest. Resuscitation.

[11] Kramer-Johansen J, Myklebust H, Wik L, Fellows B, Svensson L, Sørebø H, et al. Quality of out-of-hospital cardiopulmonary resuscitation with real time automated feedback: A prospective interventional study. Resuscitation.

[12] Monsieurs KG, Nolan JP, Bossaert LL, Greif R, Maconochie IK, Nikolaou NI, et al. European resuscitation council guidelines for resuscitation 2015: Section 1. Executive summary.

[13] Berg RA, Hemphill R, Abella BS, Aufderheide TP, Cave DM, Hazinski MF, et al. Part 5: Adult basic life support: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2010;**122**(18 Suppl 3):S685-S705

2006;**71**(2):137-145

2006;**71**(3):283-292

Resuscitation. 2015;**95**:1-80

[14] Pitts S, Kellermann AL.

[15] Benoit JL, Prince DK, Wang HE. Mechanisms linking advanced airway management and cardiac arrest outcomes. Resuscitation.

[16] Aufderheide TP, Sigurdsson G, Pirrallo RG, Yannopoulos D, McKnite S, von Briesen C, et al.

2015;**93**:124-127

Hyperventilation during cardiac arrest. Lancet. 2004;**364**(9431):313-315

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

[1] Mehra R. Global public health problem of sudden cardiac death. Journal of Electrocardiology. 2007;**40**(6):S118-S122

Review. 2016;**5**(3):177

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[8] Link MS, Berkow LC, Kudenchuk PJ, Halperin HR, Hess EP, Moitra VK, et al. 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Part 7: Adult advanced cardiovascular life support. Circulation. 2015;**132**

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*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

## **References**

*Cardiac Diseases and Interventions in 21st Century*

The authors declare no conflicts of interest.

**Conflict of interest**

The authors thank the TVF&R emergency medical service providers for collect-

ing the out-of-hospital cardiac arrest episodes used in this study.

**38**

**Author details**

provided the original work is properly cited.

Jesus M. Ruiz, Luis A. Leturiondo and Purificación Saiz University of the Basque Country (UPV/EHU), Bilbao, Spain

\*Address all correspondence to: mikel.leturiondo@ehu.eus

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Mikel Leturiondo\*, Sofía Ruiz de Gauna, José Julio Gutiérrez, Digna M. González-Otero,

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[7] Soar J, Nolan JP, Böttiger BW, Perkins GD, Lott C, Carli P, et al. European Resuscitation Council guidelines for resuscitation 2015. Section 3. Adult advanced life support. Resuscitation. 2015;**95**:100-147

[8] Link MS, Berkow LC, Kudenchuk PJ, Halperin HR, Hess EP, Moitra VK, et al. 2015 American Heart Association Guidelines Update for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Part 7: Adult advanced cardiovascular life support. Circulation. 2015;**132** (18 suppl 2):S444-S464

[9] Idris AH, Guffey D, Aufderheide TP, Brown S, Morrison LJ, Nichols P, et al. Relationship between chest compression rates and outcomes from cardiac arrest. Circulation. 2012;**125**:3004-3012

[10] Edelson DP, Abella BS, Kramer-Johansen J, Wik L, Myklebust H, Barry AM, et al. Effects of compression depth and pre-shock pauses predict defibrillation failure during cardiac arrest. Resuscitation. 2006;**71**(2):137-145

[11] Kramer-Johansen J, Myklebust H, Wik L, Fellows B, Svensson L, Sørebø H, et al. Quality of out-of-hospital cardiopulmonary resuscitation with real time automated feedback: A prospective interventional study. Resuscitation. 2006;**71**(3):283-292

[12] Monsieurs KG, Nolan JP, Bossaert LL, Greif R, Maconochie IK, Nikolaou NI, et al. European resuscitation council guidelines for resuscitation 2015: Section 1. Executive summary. Resuscitation. 2015;**95**:1-80

[13] Berg RA, Hemphill R, Abella BS, Aufderheide TP, Cave DM, Hazinski MF, et al. Part 5: Adult basic life support: 2010 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care. Circulation. 2010;**122**(18 Suppl 3):S685-S705

[14] Pitts S, Kellermann AL. Hyperventilation during cardiac arrest. Lancet. 2004;**364**(9431):313-315

[15] Benoit JL, Prince DK, Wang HE. Mechanisms linking advanced airway management and cardiac arrest outcomes. Resuscitation. 2015;**93**:124-127

[16] Aufderheide TP, Sigurdsson G, Pirrallo RG, Yannopoulos D, McKnite S, von Briesen C, et al.

Hyperventilation-induced hypotension during cardiopulmonary resuscitation. Circulation. 2004;**109**(16):1960-1965

[17] O'Neill JF, Deakin CD. Do we hyperventilate cardiac arrest patients? Resuscitation. 2007;**73**(1):82-85

[18] Maertens VL, De Smedt LE, Lemoyne S, Huybrechts SA, Wouters K, Kalmar AF, et al. Patients with cardiac arrest are ventilated two times faster than guidelines recommend: an observational prehospital study using tracheal pressure measurement. Resuscitation. 2013;**84**(7):921-926

[19] Yannopoulos D, McKnite S, Aufderheide TP, Sigurdsson G, Pirrallo RG, Benditt D, et al. Effects of incomplete chest wall decompression during cardiopulmonary resuscitation on coronary and cerebral perfusion pressures in a porcine model of cardiac arrest. Resuscitation. 2005;**64**(3):363-372

[20] Aufderheide TP, Lurie KG. Death by hyperventilation: A common and life-threatening problem during cardiopulmonary resuscitation. Critical Care Medicine. 2004;**32**(9):S345-S351

[21] Silvestri S, Ralls GA, Krauss B, Thundiyil J, Rothrock SG, Senn A, et al. The effectiveness of out-of-hospital use of continuous end-tidal carbon dioxide monitoring on the rate of unrecognized misplaced intubation within a regional emergency medical services system. Annals of Emergency Medicine. 2005;**45**(5):497-503

[22] Qvigstad E, Kramer-Johansen J, Tømte Ø, Skålhegg T, Sørensen Ø, Sunde K, et al. Clinical pilot study of different hand positions during manual chest compressions monitored with capnography. Resuscitation. 2013;**84**(9):1203-1207

[23] Pokorná M, Nečas E, Kratochvíl J, Skřipský R, Andrlík M, Franěk O. A sudden increase in partial pressure end-tidal carbon dioxide (PETCO2) at the moment of return of spontaneous circulation. The Journal of Emergency Medicine. 2010;**38**(5):614-621

[24] Kodali BS, Urman RD, et al. Capnography during cardiopulmonary resuscitation: Current evidence and future directions. Journal of Emergencies, Trauma, and Shock. 2014;**7**(4):332

[25] Touma O, Davies M. The prognostic value of end tidal carbon dioxide during cardiac arrest: A systematic review. Resuscitation. 2013;**84**(11):1470-1479

[26] Jaffe MB. Infrared measurement of carbon dioxide in the human breath: "Breathe-through" devices from Tyndall to the present day. Anesthesia and Analgesia. 2008;**107**(3):890-904

[27] Bhavani-Shankar K, Philip JH. Defining segments and phases of a time capnogram. Anesthesia and Analgesia. 2000;**91**(4):973-977

[28] Gravenstein JS, Jaffe MB, Gravenstein N, Paulus DA. Capnography. In: Gravenstein JS, editor. Clinical Perspectives. Cambridge: Cambridge University Press; 2011. pp. 6-9

[29] Pantazopoulos C, Xanthos T, Pantazopoulos I, Papalois A, Kouskouni E, Iacovidou N. A review of carbon dioxide monitoring during adult cardiopulmonary resuscitation. Heart, Lung & Circulation. 2015;**24**(11):1053-1061

[30] Meaney PA, Bobrow BJ, Mancini ME, et al. Cardiopulmonary resuscitation quality: Improving cardiac resuscitation outcomes both inside and outside the hospital: A consensus statement from the American Heart Association. Circulation. 2013;**128**(4):417-435

**41**

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[38] Raimondi M, Savastano S, Pamploni

G, Molinari S, Degani A, Belliato M. End-tidal carbon dioxide monitoring and load band device for mechanical cardio-pulmonary resuscitation: Never trust the numbers, believe at the curves.

Resuscitation. 2016;**103**:e9-e10

[39] Pellis T, Bisera J, Tang W, Weil MH. Expanding automatic external defibrillators to include automated detection of cardiac, respiratory, and cardiorespiratory arrest. Critical Care Medicine. 2002;**30**(4):S176-S178

[40] Losert H, Risdal M, Sterz F, Nysaether J, KoÈhler K, Eftestøl T, et al. Thoracic impedance changes measured via

defibrillator pads can monitor ventilation in critically ill patients and during cardiopulmonary resuscitation. Critical Care Medicine. 2006;**34**(9):2399-2405

[41] Edelson DP, Eilevstjønn J, Weidman

[42] Abella BS, Sandbo N, Vassilatos P, Alvarado JP, O'hearn N, Wigder HN, et al. Chest compression rates during cardiopulmonary resuscitation are suboptimal: A prospective study during in-hospital cardiac arrest. Circulation.

EK, Retzer E, Hoek TLV, Abella BS. Capnography and chest-wall impedance algorithms for ventilation detection during cardiopulmonary resuscitation. Resuscitation.

2010;**81**(3):317-322

2005;**111**(4):428-434

[43] Aase SO, Eftestøl T, Husøy JH, Sunde K, Steen PA. CPR artifact removal from human ECG using optimal multichannel filtering. IEEE Transactions on Biomedical Engineering. 2000;**47**(11):1440-1449

[44] Eilevstjønn J, Eftestøl T, Aase SO, Myklebust H, Husøy JH, Steen PA. Feasibility of shock advice analysis during CPR through removal of CPR artefacts from the human ECG. Resuscitation. 2004;**61**(2):131-141

*DOI: http://dx.doi.org/10.5772/intechopen.84430*

[31] Takla G, Petre JH, Doyle DJ, Horibe M, Gopakumaran B. The problem of artifactsin patient monitor data during surgery: A clinical and methodological review. Anesthesia and Analgesia.

[32] Herry CL, Townsend D, Green GC, Bravi A, Seely AJE. Segmentation and classi-fication of capnograms: Application in respiratory variability analysis. Physiological Measurement.

[33] Idris AH, Daya M, Owens P, et al. High incidence of chest compression

capnography during out-of-hospital cardiopulmonary resuscitation. Circulation. 2010;**122**:A83

oscillations associated with

[34] Deakin CD, O'Neill JF, Tabor T. Does compression-only cardiopulmonary resuscitation generate adequate passive ventilation during cardiac arrest? Resuscitation.

[35] Vanwulpen M, Wolfskeil M, Duchatelet C, Monsieurs K, Idrissi SH. Quantifying inspiratory volumes generated by manual chest compressions during resuscitation in the prehospital setting. Resuscitation.

[36] Idris AH, Banner MJ, Wenzel V, Fuerst RS, Becker LB, Melker RJ. Ventilation caused by external chest compression is unable to sustain effective gas exchange during CPR: A comparison with mechanical ventilation. Resuscitation.

[37] Leturiondo M, de Gauna SR, Ruiz JM, Gutiérrez JJ, Leturiondo LA, González-Otero DM, et al. Influence of chest compression artefact on capnogram-based ventilation detection during out-of-hospital cardiopulmonary

resuscitation. Resuscitation.

2007;**75**(1):53-59

2017;**118**:e18

1994;**28**(2):143-150

2018;**124**:63-68

2006;**103**(5):1196-1204

2014;**35**(12):2343

*Waveform Capnography for Monitoring Ventilation during Cardiopulmonary Resuscitation… DOI: http://dx.doi.org/10.5772/intechopen.84430*

[31] Takla G, Petre JH, Doyle DJ, Horibe M, Gopakumaran B. The problem of artifactsin patient monitor data during surgery: A clinical and methodological review. Anesthesia and Analgesia. 2006;**103**(5):1196-1204

[32] Herry CL, Townsend D, Green GC, Bravi A, Seely AJE. Segmentation and classi-fication of capnograms: Application in respiratory variability analysis. Physiological Measurement. 2014;**35**(12):2343

[33] Idris AH, Daya M, Owens P, et al. High incidence of chest compression oscillations associated with capnography during out-of-hospital cardiopulmonary resuscitation. Circulation. 2010;**122**:A83

[34] Deakin CD, O'Neill JF, Tabor T. Does compression-only cardiopulmonary resuscitation generate adequate passive ventilation during cardiac arrest? Resuscitation. 2007;**75**(1):53-59

[35] Vanwulpen M, Wolfskeil M, Duchatelet C, Monsieurs K, Idrissi SH. Quantifying inspiratory volumes generated by manual chest compressions during resuscitation in the prehospital setting. Resuscitation. 2017;**118**:e18

[36] Idris AH, Banner MJ, Wenzel V, Fuerst RS, Becker LB, Melker RJ. Ventilation caused by external chest compression is unable to sustain effective gas exchange during CPR: A comparison with mechanical ventilation. Resuscitation. 1994;**28**(2):143-150

[37] Leturiondo M, de Gauna SR, Ruiz JM, Gutiérrez JJ, Leturiondo LA, González-Otero DM, et al. Influence of chest compression artefact on capnogram-based ventilation detection during out-of-hospital cardiopulmonary resuscitation. Resuscitation. 2018;**124**:63-68

[38] Raimondi M, Savastano S, Pamploni G, Molinari S, Degani A, Belliato M. End-tidal carbon dioxide monitoring and load band device for mechanical cardio-pulmonary resuscitation: Never trust the numbers, believe at the curves. Resuscitation. 2016;**103**:e9-e10

[39] Pellis T, Bisera J, Tang W, Weil MH. Expanding automatic external defibrillators to include automated detection of cardiac, respiratory, and cardiorespiratory arrest. Critical Care Medicine. 2002;**30**(4):S176-S178

[40] Losert H, Risdal M, Sterz F, Nysaether J, KoÈhler K, Eftestøl T, et al. Thoracic impedance changes measured via defibrillator pads can monitor ventilation in critically ill patients and during cardiopulmonary resuscitation. Critical Care Medicine. 2006;**34**(9):2399-2405

[41] Edelson DP, Eilevstjønn J, Weidman EK, Retzer E, Hoek TLV, Abella BS. Capnography and chest-wall impedance algorithms for ventilation detection during cardiopulmonary resuscitation. Resuscitation. 2010;**81**(3):317-322

[42] Abella BS, Sandbo N, Vassilatos P, Alvarado JP, O'hearn N, Wigder HN, et al. Chest compression rates during cardiopulmonary resuscitation are suboptimal: A prospective study during in-hospital cardiac arrest. Circulation. 2005;**111**(4):428-434

[43] Aase SO, Eftestøl T, Husøy JH, Sunde K, Steen PA. CPR artifact removal from human ECG using optimal multichannel filtering. IEEE Transactions on Biomedical Engineering. 2000;**47**(11):1440-1449

[44] Eilevstjønn J, Eftestøl T, Aase SO, Myklebust H, Husøy JH, Steen PA. Feasibility of shock advice analysis during CPR through removal of CPR artefacts from the human ECG. Resuscitation. 2004;**61**(2):131-141

**40**

*Cardiac Diseases and Interventions in 21st Century*

Hyperventilation-induced hypotension during cardiopulmonary resuscitation. Circulation. 2004;**109**(16):1960-1965

sudden increase in partial pressure end-tidal carbon dioxide (PETCO2) at the moment of return of spontaneous circulation. The Journal of Emergency

Medicine. 2010;**38**(5):614-621

[24] Kodali BS, Urman RD, et al. Capnography during cardiopulmonary

resuscitation: Current evidence and future directions. Journal of Emergencies, Trauma, and Shock.

[25] Touma O, Davies M. The prognostic value of end tidal carbon dioxide during cardiac arrest: A systematic review. Resuscitation. 2013;**84**(11):1470-1479

[26] Jaffe MB. Infrared measurement of carbon dioxide in the human breath: "Breathe-through" devices from Tyndall to the present day. Anesthesia and Analgesia. 2008;**107**(3):890-904

[27] Bhavani-Shankar K, Philip JH. Defining segments and phases of a time capnogram. Anesthesia and Analgesia. 2000;**91**(4):973-977

[28] Gravenstein JS, Jaffe MB, Gravenstein N, Paulus DA.

[29] Pantazopoulos C, Xanthos T, Pantazopoulos I, Papalois A, Kouskouni E, Iacovidou N. A review of carbon dioxide monitoring during adult cardiopulmonary resuscitation.

Heart, Lung & Circulation. 2015;**24**(11):1053-1061

ME, et al. Cardiopulmonary resuscitation quality: Improving cardiac resuscitation outcomes both inside and outside the hospital: A consensus statement from the American

Heart Association. Circulation.

2013;**128**(4):417-435

[30] Meaney PA, Bobrow BJ, Mancini

pp. 6-9

Capnography. In: Gravenstein JS, editor. Clinical Perspectives. Cambridge: Cambridge University Press; 2011.

2014;**7**(4):332

[17] O'Neill JF, Deakin CD. Do we hyperventilate cardiac arrest patients? Resuscitation. 2007;**73**(1):82-85

[18] Maertens VL, De Smedt LE, Lemoyne S, Huybrechts SA, Wouters K, Kalmar AF, et al. Patients with cardiac arrest are ventilated two times faster than guidelines recommend: an observational prehospital study using tracheal pressure measurement. Resuscitation. 2013;**84**(7):921-926

[19] Yannopoulos D, McKnite S, Aufderheide TP, Sigurdsson G,

2005;**64**(3):363-372

2005;**45**(5):497-503

2013;**84**(9):1203-1207

Pirrallo RG, Benditt D, et al. Effects of incomplete chest wall decompression during cardiopulmonary resuscitation on coronary and cerebral perfusion pressures in a porcine model of cardiac arrest. Resuscitation.

[20] Aufderheide TP, Lurie KG. Death by hyperventilation: A common and life-threatening problem during

cardiopulmonary resuscitation. Critical Care Medicine. 2004;**32**(9):S345-S351

[21] Silvestri S, Ralls GA, Krauss B, Thundiyil J, Rothrock SG, Senn A, et al. The effectiveness of out-of-hospital use of continuous end-tidal carbon dioxide monitoring on the rate of unrecognized misplaced intubation within a regional emergency medical services system. Annals of Emergency Medicine.

[22] Qvigstad E, Kramer-Johansen J, Tømte Ø, Skålhegg T, Sørensen Ø, Sunde K, et al. Clinical pilot study of different hand positions during manual chest compressions monitored with capnography. Resuscitation.

[23] Pokorná M, Nečas E, Kratochvíl J, Skřipský R, Andrlík M, Franěk O. A

Chapter 4

Abstract

1. Introduction

43

Nagaeva Gulnora

Characteristics of Acute

Data Register "RACSMI-Uz"

into account their gender and other uncompensated risk factors.

risk factors, men, women, arterial hypertension, obesity, diabetes

as the role of these diseases in mortality rates [2, 3].

disease or receiving a specific treatment [1, 4].

Keywords: acute coronary syndrome, acute myocardial infarction, register,

The emergence of epidemiology as a science made it possible to formulate the basic principles of conducting research, which make it possible to obtain real information about the prevalence of diseases, the characteristics of their occurrence and course, outcomes, etc. [1]. One of the first major epidemiological studies in the field of cardiology was the well-known Framingham study, which revealed the main factors contributing to the development of cardiovascular diseases (CVD), as well

At the same time, epidemiological studies are not the most successful way to study a particular disease, especially when it comes to studying the characteristics of its course, outcomes, the treatment used, and its effectiveness. In the midtwentieth century, it became clear that the most accurate method for obtaining information about the real clinical course of the disease, its outcomes, etc. in certain regions or even in individual medical institutions is the so-called registers, which are an organized system for collecting information about patients having a specific

For several decades, the registers of acute myocardial infarction (AMI) and, more recently, the registers of acute coronary syndrome (ACS) are regularly held in different countries of the world, and their scale varies from individual clinics (and even departments in clinics) to large regions, whole countries, and even groups of

Myocardial Damage in Uzbekistan:

In 2015, a register of acute coronary events (acute coronary syndrome and acute myocardial infarction) was carried out in one of the districts of the city of Tashkent. The study included 782 patients, of which 491 (63.7%) were analyzed (hereinafter 100%) and the remaining 291 (36.3%) were dead (according to the civil registry office). The average age of patients was 58.3 7.9 years. The features of the patient's nosological structures were established separately for men and women when admitted to hospital and discharged from hospital, which will make it possible to further adjust the tactics of management of these categories of patients, taking

[45] Gong Y, Chen B, Li Y. A review of the performance of artifact filtering algorithms for cardiopulmonary resuscitation. Journal of Healthcare Engineering. 2013;**4**(2):185-202

[46] Ruiz de Gauna S, Irusta U, Ruiz J, Ayala U, Aramendi E, Eftestøl T. Rhythm analysis during cardiopulmonary resuscitation: Past, present, and future. BioMed Research International. 2014;**2014**:1-13

[47] Widrow B, Stearns SD. Adaptive Signal Processing. Vol. 1. Englewood Cliffs, NJ: Prentice-Hall, Inc; 1985. p. 491

[48] Gutiérrez JJ, Leturiondo M, Ruiz de Gauna S, Ruiz JM, Leturiondo LA, et al. Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform. PLoS One. 2018;**13**(8):e0201565

[49] Ruiz de Gauna S, Leturiondo M, Gutiérrez JJ, Ruiz JM, González-Otero DM, Russell JK, et al. Enhancement of capnogram waveform in the presence of chest compression artefact during cardiopulmonary resuscitation. Resuscitation. 2018;**133**:53-58

## Chapter 4

*Cardiac Diseases and Interventions in 21st Century*

[45] Gong Y, Chen B, Li Y. A review of the performance of artifact filtering algorithms for cardiopulmonary resuscitation. Journal of Healthcare Engineering. 2013;**4**(2):185-202

[46] Ruiz de Gauna S, Irusta U, Ruiz J, Ayala U, Aramendi E, Eftestøl T. Rhythm analysis during cardiopulmonary resuscitation: Past, present, and future. BioMed Research

International. 2014;**2014**:1-13

p. 491

[47] Widrow B, Stearns SD. Adaptive Signal Processing. Vol. 1. Englewood Cliffs, NJ: Prentice-Hall, Inc; 1985.

[48] Gutiérrez JJ, Leturiondo M, Ruiz de Gauna S, Ruiz JM, Leturiondo LA, et al. Enhancing ventilation detection during cardiopulmonary resuscitation by filtering chest compression artifact from the capnography waveform. PLoS

[49] Ruiz de Gauna S, Leturiondo M, Gutiérrez JJ, Ruiz JM, González-Otero DM, Russell JK, et al. Enhancement of capnogram waveform in the presence of chest compression artefact during cardiopulmonary resuscitation. Resuscitation. 2018;**133**:53-58

One. 2018;**13**(8):e0201565

**42**

## Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

Nagaeva Gulnora

## Abstract

In 2015, a register of acute coronary events (acute coronary syndrome and acute myocardial infarction) was carried out in one of the districts of the city of Tashkent. The study included 782 patients, of which 491 (63.7%) were analyzed (hereinafter 100%) and the remaining 291 (36.3%) were dead (according to the civil registry office). The average age of patients was 58.3 7.9 years. The features of the patient's nosological structures were established separately for men and women when admitted to hospital and discharged from hospital, which will make it possible to further adjust the tactics of management of these categories of patients, taking into account their gender and other uncompensated risk factors.

Keywords: acute coronary syndrome, acute myocardial infarction, register, risk factors, men, women, arterial hypertension, obesity, diabetes

## 1. Introduction

The emergence of epidemiology as a science made it possible to formulate the basic principles of conducting research, which make it possible to obtain real information about the prevalence of diseases, the characteristics of their occurrence and course, outcomes, etc. [1]. One of the first major epidemiological studies in the field of cardiology was the well-known Framingham study, which revealed the main factors contributing to the development of cardiovascular diseases (CVD), as well as the role of these diseases in mortality rates [2, 3].

At the same time, epidemiological studies are not the most successful way to study a particular disease, especially when it comes to studying the characteristics of its course, outcomes, the treatment used, and its effectiveness. In the midtwentieth century, it became clear that the most accurate method for obtaining information about the real clinical course of the disease, its outcomes, etc. in certain regions or even in individual medical institutions is the so-called registers, which are an organized system for collecting information about patients having a specific disease or receiving a specific treatment [1, 4].

For several decades, the registers of acute myocardial infarction (AMI) and, more recently, the registers of acute coronary syndrome (ACS) are regularly held in different countries of the world, and their scale varies from individual clinics (and even departments in clinics) to large regions, whole countries, and even groups of

countries (international registries). Perhaps the most famous are registers such as Global Registry of Acute Coronary Events Project (GRACE), registers of the European Society of Cardiology (EHS-ACS-I, EHS-ACS-II), and CRUSADE register [5]. However, these registries do not allow a comprehensive assessment of the quality of diagnosis and treatment of arterial hypertension (AH), coronary heart disease (CHD), chronic heart failure, diabetes mellitus (DM), their combinations, etc. in actual clinical practice, to determine the structure of risk factors (RFs) and comorbidities in this category of patients.

• Patients must meet inclusion criteria.

DOI: http://dx.doi.org/10.5772/intechopen.88134

2.1.1 Inclusion criteria

2.1.1.1 Clinical characteristic

• Frequent heartbeat.

• Dyspnea on exertion.

trunk.

2.1.1.2 ECG diagnosis

posterior location.

• Acute blockade of the left bundle of His.

significant:

45

• Patient involvement should not affect the approaches to his treatment.

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

• The inclusion of the patient in the register must be accompanied by his

emergency medical service, hospitalized in relevant hospitals for ACS/AMI.

• ACS and AMI were diagnosed based on generally accepted criteria.

rear upper region of the body and to the left arm.

• Nausea, due to stimulation of the vagus nerve.

• Severe fatigue, even with minimal exertion.

registration in the database with filling in the "register card" for each patient.

The register included patients aged from 18 to 70 years old who applied to the

Complaints of patients with acute coronary insufficiency include the following:

• The pain, which is usually described as pressure, squeezing or burning in the entire left half of the chest and can be transmitted to the neck, shoulder, jaw,

• Diaphoresis (excessive sweating), due to the irritant effect of the sympathetic

WHO experts have suggested distinguishing between certain ECG changes, indicating a myocardial infarction, and ambiguous ECG changes that allow to suspect myocardial infarction [11]. The following ECG changes are diagnostically

• New or repeated lifting of the ST segment by 1 mm or more in at least two adjacent chest leads or by 2 mm or more in two leads from the extremities. Depression of the ST segment in leads V1–V3 is considered as the equivalent of

• The emergence of new or deepening of existing pathological teeth Q (duration ≥30 ms and depth ≥ 1 mm in two adjacent chest leads or in two leads from the extremities). An increase in the amplitude of the R-wave in leads V1–V3 is considered as the equivalent of the Q wave in cases of suspected IM of

• Interpretation of the ECG dynamics against the background of changes caused by previous MI, especially in its acute period, can be very difficult. Therefore,

ST-elevation with suspected IM posterior localization.

Individual attention is required for patients with concomitant disorders of carbohydrate metabolism, in particular with the presence of DM. According to the International Diabetes Federation (IDF, 2014), at present, diabetes affects 400 million people in the world, and by 2035, their number will increase to 600 million people. It is known that DM increases the risk of developing CVD by a factor of 2–4, and mortality with a combination of CVD and DM increases four to five times [6, 7]. The paradox of DM is the increase in CVD in women and the lack of reduction in the growth of these diseases in men, in countries that have achieved significant success in the treatment of coronary artery disease (CHD) [8]. The combination of a whole cluster of rapid development and progression of atherosclerosis based on insulin resistance—hyperglycemia, dyslipidemia, AH—allowed the expert committee of the US National Cholesterol Education Program (NCEP) to equate type 2 diabetes to CHD. Today, DM is considered as equivalent to the presence of clinically significant CVD [9]. Nevertheless, against the background of modern technologies and rapidly developing interventional treatment methods for acute forms of CHD, the attention of clinicians is more focused on the treatment of the disease itself than on the causes of it or RF of its development. Used in modern clinical practice, standards for the treatment of CHD (β-blockers, BAB; angiotensin-converting enzyme inhibitors, inhibitors ACE inhibitors; aspirin, statins, antiplatelet agents, etc.), according to numerous randomized clinical trials [10], have proven to be effective, safe, and positive prognosis in this category of patients. However, there remains the question of how these drugs are regularly and consistently used by the patients themselves and what impact this has on the further course of the disease and the condition of the patients. From this perspective, the real evaluation of therapy received by patients in such practical health conditions is of great scientific and practical interest.

The foregoing implies the relevance and practical significance of the creation of the register for acute coronary syndrome and acute myocardial infarction in Uzbekistan (RACSMI-Uz), with the inclusion of patients with similar diagnoses, as well as an assessment of the interdependence of these RFs and gender characteristics. On the territory of Uzbekistan, such registers were not previously conducted; therefore, this study is not only practically interesting and relevant but also in demand.

## 2. Own results of research

### 2.1 Material and methods of research

The research material was created and processed, in accordance with the developed register protocol (map-register), a database of personal data of patients hospitalized with a diagnosis of ACS/AMI for 1 calendar (2015) year.

Data analysis of all patients with ACS/AMI during the register implied that the following conditions were:

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134


## 2.1.1 Inclusion criteria

countries (international registries). Perhaps the most famous are registers such as Global Registry of Acute Coronary Events Project (GRACE), registers of the European Society of Cardiology (EHS-ACS-I, EHS-ACS-II), and CRUSADE register [5]. However, these registries do not allow a comprehensive assessment of the quality of diagnosis and treatment of arterial hypertension (AH), coronary heart disease (CHD), chronic heart failure, diabetes mellitus (DM), their combinations, etc. in actual clinical practice, to determine the structure of risk factors (RFs) and

Individual attention is required for patients with concomitant disorders of carbohydrate metabolism, in particular with the presence of DM. According to the International Diabetes Federation (IDF, 2014), at present, diabetes affects 400 million people in the world, and by 2035, their number will increase to 600 million people. It is known that DM increases the risk of developing CVD by a factor of 2–4, and mortality with a combination of CVD and DM increases four to five times [6, 7]. The paradox of DM is the increase in CVD in women and the lack of reduction in the growth of these diseases in men, in countries that have achieved significant success in the treatment of coronary artery disease (CHD) [8]. The combination of a whole cluster of rapid development and progression of atherosclerosis based on insulin resistance—hyperglycemia, dyslipidemia, AH—allowed the expert committee of the US National Cholesterol Education Program (NCEP) to equate type 2 diabetes to CHD. Today, DM is considered as equivalent to the presence of clinically significant CVD [9]. Nevertheless, against the background of modern technologies and rapidly developing interventional treatment methods for acute forms of CHD, the attention of clinicians is more focused on the treatment of the disease itself than on the causes of it or RF of its development. Used in modern clinical practice, standards for the treatment of CHD (β-blockers, BAB; angiotensin-converting enzyme inhibitors, inhibitors ACE inhibitors; aspirin, statins, antiplatelet agents, etc.), according to numerous randomized clinical trials [10], have proven to be effective, safe, and positive prognosis in this category of patients. However, there remains the question of how these drugs are regularly and consistently used by the patients themselves and what impact this has on the further course of the disease and the condition of the patients. From this perspective, the real evaluation of therapy received by patients in such practical health conditions is of great scientific and practical

The foregoing implies the relevance and practical significance of the creation of

the register for acute coronary syndrome and acute myocardial infarction in Uzbekistan (RACSMI-Uz), with the inclusion of patients with similar diagnoses, as well as an assessment of the interdependence of these RFs and gender characteristics. On the territory of Uzbekistan, such registers were not previously conducted; therefore, this study is not only practically interesting and relevant but also

The research material was created and processed, in accordance with the developed register protocol (map-register), a database of personal data of patients

Data analysis of all patients with ACS/AMI during the register implied that the

hospitalized with a diagnosis of ACS/AMI for 1 calendar (2015) year.

comorbidities in this category of patients.

Cardiac Diseases and Interventions in 21st Century

interest.

in demand.

44

2. Own results of research

following conditions were:

2.1 Material and methods of research

The register included patients aged from 18 to 70 years old who applied to the emergency medical service, hospitalized in relevant hospitals for ACS/AMI.

• ACS and AMI were diagnosed based on generally accepted criteria.

## 2.1.1.1 Clinical characteristic

Complaints of patients with acute coronary insufficiency include the following:


## 2.1.1.2 ECG diagnosis

WHO experts have suggested distinguishing between certain ECG changes, indicating a myocardial infarction, and ambiguous ECG changes that allow to suspect myocardial infarction [11]. The following ECG changes are diagnostically significant:


crucial in the diagnosis of recurrent MI acquire serum markers of necrosis. Among them, the leading role belongs to CPK, CPK-MB, and myoglobin but not troponins, since elevated levels of the latter in the blood persist for a long time and may mask their possible new rises. Re-elevation of CPK-MB above normal or 50% higher than the previous peak, or repeated elevation of total CPK or myoglobin twice as high as the upper limit of normal, allows a sufficient degree of confidence to diagnose a relapse of a heart attack.

The study found that men with ACS/AMI were younger than women. The age difference was due to the fact that among men, patients younger than 50 years prevailed. Namely, the age category up to 40 years in the group of men was 3.3% and in the group of women = 0.5% (р = 0.076 и χ2 = 2157); the number of men aged

On the contrary, age categories 51–60 years and 61+ were priority for females. In

number of women = 40.3%; the number of men in the 60+ category turned out to be 39.9% and for women = 47.1%. This was confirmed during the correlation

The calculation of body mass index (BMI) was carried out in a total of 225 patients, of whom 125 were men and 100 women. Analysis of BMI by sex found that normal weight in men was observed in 17.6% and in women—in 15.0% of cases. However, being overweight, i.e., BMI values from 25 to 30 kg/m<sup>2</sup> were recorded in men much more often than in women (52.8 and 37.0%, respectively, men and women, р = 0.001 и χ2 = 10,573). Obesity of varying severity, in contrast, was more often observed in women than in men (Table 1). This was confirmed during the

Thus, depending on gender, it was found that ACS/AMI was more often recorded in men, amounting to 54.1%; the incidence of ACS/AMI among women was 45.9%. In the age aspect, men with ACS/AMI were younger than women, and in terms of weight characteristics, obesity of various severity prevailed among

According to anamnestic data, postponed cardiovascular catastrophes were more often observed in males. Namely, the transferred myocardial infarction (TMI) was noted by men 1.8 times more often than women (р = 0.0000 и χ2 = 14,282); the

Graph of correlation between gender and age of patients. p = 0.001; r = 0.154; t = 3.277. On the X-axis, the numeral "1"—male gender, and the numeral "2"—female gender; Y-axis, age of patients in years.

women (48.0% in women vs. 29.6% in men, p = 0.007).

41–50 years was 20.6% and among women = 12.1% (р = 0.024 и χ2 = 5116).

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

particular, the number of men aged 51–60 turned out to be 36.2%, and the

analysis (Figure 1).

Figure 1.

47

correlation analysis (Figure 2).

DOI: http://dx.doi.org/10.5772/intechopen.88134

## 2.1.1.3 Dynamics of myocardial damage markers

In our study, we evaluated troponin-T in a qualitative manner using a special indicator. Troponin-T is a myocardic protein, and its elevated blood level is diagnosed 2–3 h after a heart attack. The maximum amount of protein is detected 10 h after the onset of the attack. Troponin-T is preserved in the blood during a heart attack at a very high level for quite a long time—up to 7 days. Troponin-T refers to cardiospecific markers, which make it possible to determine undiagnosed infarction that has passed in a patient without clearly expressed symptoms and who does not have pronounced signs according to the ECG results. The test is very simple to use. Two to three drops of the patient's blood are applied to a special indicator. You can evaluate the result of the study in 10–15 min. When staining two bands on the indicator, we can conclude that the patient suffered a heart attack. If only one lane turned out to be colored, then health problems are caused by other causes and pathologies [12].

## 2.1.2 Exclusion criteria

• Age under 18 and over 70 years

## 2.1.2.1 Statistical analysis

Statistical processing of the results was carried out on a Pentium-IV personal computer using the STATISTICA 6 software package. Calculate the arithmetic mean (M) and root-mean-square (standard) deviation (SD).

In our study to avoid statistical inaccuracy, the analysis was accompanied by a check on the normal distribution of clinical signs.

To compare the arithmetic means of the two groups, the t-student test was used. To assess the presence of relationships between indicators, a correlation analysis was performed with the calculation of the Pearson correlation coefficient. To analyze the reliability of differences between qualitative signs, the χ2 criterion was used.

## 2.1.2.2 Ethical aspects

The study was conducted in accordance with the principles of the Helsinki Declaration.

## 2.2 Comparative analysis of the results of the register "RACSMI-Uz"depending on gender

For a comparative analysis of clinical and anamnestic data, as well as determining patient adherence to medical recommendations, depending on gender two groups of patients were allocated: 1g = 243 male patients and 2g = 206 female patients.

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

The study found that men with ACS/AMI were younger than women. The age difference was due to the fact that among men, patients younger than 50 years prevailed. Namely, the age category up to 40 years in the group of men was 3.3% and in the group of women = 0.5% (р = 0.076 и χ2 = 2157); the number of men aged 41–50 years was 20.6% and among women = 12.1% (р = 0.024 и χ2 = 5116).

On the contrary, age categories 51–60 years and 61+ were priority for females. In particular, the number of men aged 51–60 turned out to be 36.2%, and the number of women = 40.3%; the number of men in the 60+ category turned out to be 39.9% and for women = 47.1%. This was confirmed during the correlation analysis (Figure 1).

The calculation of body mass index (BMI) was carried out in a total of 225 patients, of whom 125 were men and 100 women. Analysis of BMI by sex found that normal weight in men was observed in 17.6% and in women—in 15.0% of cases. However, being overweight, i.e., BMI values from 25 to 30 kg/m<sup>2</sup> were recorded in men much more often than in women (52.8 and 37.0%, respectively, men and women, р = 0.001 и χ2 = 10,573). Obesity of varying severity, in contrast, was more often observed in women than in men (Table 1). This was confirmed during the correlation analysis (Figure 2).

Thus, depending on gender, it was found that ACS/AMI was more often recorded in men, amounting to 54.1%; the incidence of ACS/AMI among women was 45.9%. In the age aspect, men with ACS/AMI were younger than women, and in terms of weight characteristics, obesity of various severity prevailed among women (48.0% in women vs. 29.6% in men, p = 0.007).

According to anamnestic data, postponed cardiovascular catastrophes were more often observed in males. Namely, the transferred myocardial infarction (TMI) was noted by men 1.8 times more often than women (р = 0.0000 и χ2 = 14,282); the

#### Figure 1.

Graph of correlation between gender and age of patients. p = 0.001; r = 0.154; t = 3.277. On the X-axis, the numeral "1"—male gender, and the numeral "2"—female gender; Y-axis, age of patients in years.

crucial in the diagnosis of recurrent MI acquire serum markers of necrosis. Among them, the leading role belongs to CPK, CPK-MB, and myoglobin but not troponins, since elevated levels of the latter in the blood persist for a long time and may mask their possible new rises. Re-elevation of CPK-MB above normal or 50% higher than the previous peak, or repeated elevation of total CPK or myoglobin twice as high as the upper limit of normal, allows a sufficient degree of confidence to diagnose a relapse of a heart attack.

In our study, we evaluated troponin-T in a qualitative manner using a special indicator. Troponin-T is a myocardic protein, and its elevated blood level is diagnosed 2–3 h after a heart attack. The maximum amount of protein is detected 10 h after the onset of the attack. Troponin-T is preserved in the blood during a heart attack at a very high level for quite a long time—up to 7 days. Troponin-T refers to cardiospecific markers, which make it possible to determine undiagnosed infarction that has passed in a patient without clearly expressed symptoms and who does not have pronounced signs according to the ECG results. The test is very simple to use. Two to three drops of the patient's blood are applied to a special indicator. You can evaluate the result of the study in 10–15 min. When staining two bands on the indicator, we can conclude that the patient suffered a heart attack. If only one lane turned out to be colored, then health problems are caused by other causes and

Statistical processing of the results was carried out on a Pentium-IV personal computer using the STATISTICA 6 software package. Calculate the arithmetic

In our study to avoid statistical inaccuracy, the analysis was accompanied by a

To compare the arithmetic means of the two groups, the t-student test was used. To assess the presence of relationships between indicators, a correlation analysis was performed with the calculation of the Pearson correlation coefficient. To analyze the reliability of differences between qualitative signs, the χ2 criterion

The study was conducted in accordance with the principles of the Helsinki

2.2 Comparative analysis of the results of the register "RACSMI-Uz"depending

determining patient adherence to medical recommendations, depending on gender two groups of patients were allocated: 1g = 243 male patients and 2g = 206 female

For a comparative analysis of clinical and anamnestic data, as well as

mean (M) and root-mean-square (standard) deviation (SD).

check on the normal distribution of clinical signs.

2.1.1.3 Dynamics of myocardial damage markers

Cardiac Diseases and Interventions in 21st Century

pathologies [12].

was used.

Declaration.

patients.

46

on gender

2.1.2.2 Ethical aspects

2.1.2 Exclusion criteria

2.1.2.1 Statistical analysis

• Age under 18 and over 70 years


The age of persons with cardiac surgery did not depend on any gender dependency: for women = 57.7 7.1 years and for men = 58.6 5.6 years (p = 0.246). Despite the fact that men with a history of TMI were younger, nevertheless, they were more likely to have stenotic contractions of ≥50%; however, revealed differ-

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

The analysis of RFs is presented in Figure 3, from which it is clear that smoking, hypertension, and hypercholesterolemia (HChE) prevailed among men. In women, the main RFs were disorders of both carbohydrate and lipid metabolisms, hypertension, and obesity. The difference in RFs—smoking, impaired carbohydrate metabolism, and obesity—reached a statistically significant level (Figure 4). However, the total component of the RFs for the averaged value in women was less than in men: the average number of RFs in men = 3.6 1.2 and in women =

Thus, with ACS/AMI, gender-independent RFs turned out to be AH and HChE and gender-related—smoking (for men) and carbohydrate metabolism disorders and obesity (for women). The transferred of cardiovascular accidents was

prerogative of males, while age was a controversial point in the development of this or that damage (TMI occurred in younger men and stroke in older men, compared

The next stage of the study was an assessment of the patients' adherence to therapy, depending on gender. From these positions, there were no statistically significant differences between the groups. The average number of medications taken per day among men was 2.2 1.7 per person and among women = 2.2 1.6,

However, when calculating quantitative values, it was found that, in general, the female population turned out to be more committed to pharmacotherapy than the male population (the number of committed women was 80.6% against men = 75.7%, p = 0.261, and χ2 = 1.264). At the same time, the women's group prevailed in taking

Anamnestic patient characteristics. Note: \*significance of differences between groups at p < 0.05; \*\*significance of differences between groups at p < 0.001; TMI, transferred myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; presence of stenosis ≥ 50%; patient awareness of

the presence of a cardiovascular pathology; data are presented in percentage.

respectively (p = 1.000). The substantive aspect of conservative therapy is presented in Figure 5, from which it can be seen that both men and women had approximately the same proportions for the main groups of drugs taken, but the

ences did not reach significance level.

DOI: http://dx.doi.org/10.5772/intechopen.88134

difference did not reach significance level.

2.4 1.1 (p = 0.0000).

to women).

Figure 3.

49

#### Table 1.

Anthropometric characteristics of patients.

Figure 2. Graph of correlation between gender and BMI of patients. p = 0.015; r = 0.161; t = 2.434. On the X-axis: The numeral "1"—male gender and the numeral "2"—female gender; Y-axis: BMI, body mass index in kg/m<sup>2</sup> .

presence of a stroke in men was 1.7% more than in the female group; percutaneous coronary interventions (PCI) or coronary artery bypass surgery (CABG) in men was 11.5%, which was 3.3% more than in women (Figure 3).

The average age of women with TMI was 61.5 7.8 years and of men = 58.4 8.4 years (p = 0.041); on the contrary, the age of women with stroke was 59.6 9.5 years and for men = 61.3 7.1 years (p = 0.204).

### Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

The age of persons with cardiac surgery did not depend on any gender dependency: for women = 57.7 7.1 years and for men = 58.6 5.6 years (p = 0.246). Despite the fact that men with a history of TMI were younger, nevertheless, they were more likely to have stenotic contractions of ≥50%; however, revealed differences did not reach significance level.

The analysis of RFs is presented in Figure 3, from which it is clear that smoking, hypertension, and hypercholesterolemia (HChE) prevailed among men. In women, the main RFs were disorders of both carbohydrate and lipid metabolisms, hypertension, and obesity. The difference in RFs—smoking, impaired carbohydrate metabolism, and obesity—reached a statistically significant level (Figure 4). However, the total component of the RFs for the averaged value in women was less than in men: the average number of RFs in men = 3.6 1.2 and in women = 2.4 1.1 (p = 0.0000).

Thus, with ACS/AMI, gender-independent RFs turned out to be AH and HChE and gender-related—smoking (for men) and carbohydrate metabolism disorders and obesity (for women). The transferred of cardiovascular accidents was prerogative of males, while age was a controversial point in the development of this or that damage (TMI occurred in younger men and stroke in older men, compared to women).

The next stage of the study was an assessment of the patients' adherence to therapy, depending on gender. From these positions, there were no statistically significant differences between the groups. The average number of medications taken per day among men was 2.2 1.7 per person and among women = 2.2 1.6, respectively (p = 1.000). The substantive aspect of conservative therapy is presented in Figure 5, from which it can be seen that both men and women had approximately the same proportions for the main groups of drugs taken, but the difference did not reach significance level.

However, when calculating quantitative values, it was found that, in general, the female population turned out to be more committed to pharmacotherapy than the male population (the number of committed women was 80.6% against men = 75.7%, p = 0.261, and χ2 = 1.264). At the same time, the women's group prevailed in taking

#### Figure 3.

presence of a stroke in men was 1.7% more than in the female group; percutaneous coronary interventions (PCI) or coronary artery bypass surgery (CABG) in men

Graph of correlation between gender and BMI of patients. p = 0.015; r = 0.161; t = 2.434. On the X-axis: The numeral "1"—male gender and the numeral "2"—female gender; Y-axis: BMI, body mass index in kg/m<sup>2</sup>

.

Indicator Men (n = 243) Women (n = 206) р χ2 Age, years 57.3 8.6 59.8 7.3 0.001 Weight, kg 83.5 11.2 79.2 14.2 0.012 Height, cm 171.7 5.2 162.9 6.1 0.000 BMI, kg/m<sup>2</sup> 28.4 3.7 29.7 4.6 0.020

Normal weight, % 17.6 15.0 0.732 0.117

Notes: n, the number of patients; p and χ2, significance of differences between groups; BMI, body mass index

), % 52.8 37.0 0.026 4.969

), % 26.4 35.0 0.211 1.562

), % 0.8 3.0 0.458 0.550

), % 2.4 10.0 0.032 4.581

BMI measurement carried out, n (%) 125 (51.4%) 100 (48.5%)

Excess weight (BMI = 25.1–30.0 kg/m2

Obesity 1 degree (BMI = 30.1–35.0 kg/m<sup>2</sup>

Obesity of 3 degrees, (BMI ≥ 40.1 kg/m<sup>2</sup>

Anthropometric characteristics of patients.

Table 1.

Figure 2.

48

Obesity of 2 degrees, (BMI = 35.1–40.0 kg/m<sup>2</sup>

Cardiac Diseases and Interventions in 21st Century

The average age of women with TMI was 61.5 7.8 years and of men = 58.4 8.4 years (p = 0.041); on the contrary, the age of women with stroke was

was 11.5%, which was 3.3% more than in women (Figure 3).

59.6 9.5 years and for men = 61.3 7.1 years (p = 0.204).

Anamnestic patient characteristics. Note: \*significance of differences between groups at p < 0.05; \*\*significance of differences between groups at p < 0.001; TMI, transferred myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; presence of stenosis ≥ 50%; patient awareness of the presence of a cardiovascular pathology; data are presented in percentage.

#### Figure 4.

Risk factors by gender. Note: Data are presented in percentage; \*\*significance of differences between groups with p < 0.001 and \*a tendency to significance of differences between groups (p = 0.057).

Figure 5.

The main groups of medications taken, depending on gender. Note: the data are presented in percentage, all p > 0.05; ARA-II, angiotensin receptor antagonists-II; CA, calcium antagonists; BB, beta blockers.

different between men and women. However, a direct correlation was found between the age of the respondents and the number of medications taken per day.

Graph of correlation between age of patients and adherence to therapy. р = 0.000; r = 0.214; t = 4619. On the

The number of medications taken per day Men (n = 184) Women (n = 166) р χ2 1 drug, % 17.4 20.5 0.548 0.362 2 drugs, % 20.6 22.3 0.809 0.059 3 drugs, % 26.1 29.5 0.551 0.356 4 drugs, % 23.9 19.9 0.435 0.609 5 drugs, % 9.8 6.6 0.381 0.766 6 and more drugs, % 2.2 1.2 0.776 0.081

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

DOI: http://dx.doi.org/10.5772/intechopen.88134

Distribution of patients according to the daily ration of medications among men and women.

2.3 The relationship of arterial hypertension with acute coronary events

To assess the effect of hypertension, two groups were formed: group 1—47 respondents without hypertension (control group) and group 2—385 people with the presence of hypertension with varying severity. The groups were comparable in age and sex, as well as height-weight parameters. The distribution of individuals according to BMI established that the number of patients with overweight in the group with AH was significantly higher than in the control group (p = 0.019; χ2 = 5.520), and the number of patients with AH and normal weight was almost two

(a fragment of the study "RACSMI-Uz")

X-axis, age in years; on the Y-axis, the number of drugs taken per day.

Table 2.

Figure 6.

51

times less than in the control group (Table 3).

from 1 to 3 medicines per day, men's—from 4 or more pharmaceuticals per day, but the difference did not reach the level of confidence (Table 2).

A correlation analysis revealed that adherence to therapy increases with age, regardless of gender (Figure 6).

Thus, this fragment of the study showed that women's adherence to therapy was slightly higher than that of men; men were prone to taking more drugs, although the proportional ratio between the groups of drugs taken was not significantly


Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

#### Table 2.

Distribution of patients according to the daily ration of medications among men and women.

Figure 6. Graph of correlation between age of patients and adherence to therapy. р = 0.000; r = 0.214; t = 4619. On the X-axis, age in years; on the Y-axis, the number of drugs taken per day.

different between men and women. However, a direct correlation was found between the age of the respondents and the number of medications taken per day.

## 2.3 The relationship of arterial hypertension with acute coronary events (a fragment of the study "RACSMI-Uz")

To assess the effect of hypertension, two groups were formed: group 1—47 respondents without hypertension (control group) and group 2—385 people with the presence of hypertension with varying severity. The groups were comparable in age and sex, as well as height-weight parameters. The distribution of individuals according to BMI established that the number of patients with overweight in the group with AH was significantly higher than in the control group (p = 0.019; χ2 = 5.520), and the number of patients with AH and normal weight was almost two times less than in the control group (Table 3).

from 1 to 3 medicines per day, men's—from 4 or more pharmaceuticals per day, but

The main groups of medications taken, depending on gender. Note: the data are presented in percentage, all p > 0.05; ARA-II, angiotensin receptor antagonists-II; CA, calcium antagonists; BB, beta blockers.

Risk factors by gender. Note: Data are presented in percentage; \*\*significance of differences between groups with

p < 0.001 and \*a tendency to significance of differences between groups (p = 0.057).

Cardiac Diseases and Interventions in 21st Century

A correlation analysis revealed that adherence to therapy increases with age,

Thus, this fragment of the study showed that women's adherence to therapy was slightly higher than that of men; men were prone to taking more drugs, although the proportional ratio between the groups of drugs taken was not significantly

the difference did not reach the level of confidence (Table 2).

regardless of gender (Figure 6).

Figure 5.

50

Figure 4.


Notes: n, number of patients; АН, arterial hypertension; %, percentage of patients with this symptom; BMI, body mass index

#### Table 3.

Comparative characteristics of patient growth and weight indicators depending on the presence of arterial hypertension.

Analysis of anamnestic data showed that in group 2, individuals with myocardial infarction prevailed (33.2 and 8.5%, respectively, in groups 2 and 1; p = 0.000 and χ2 = 10.941). Also, hypertension was significantly more frequently accompanied by the development of chronic heart failure (53.3 and 23.4%, respectively, in groups 2 and 1; p = 0.000 and χ2 = 13.751). Patients with hypertension who underwent PCI or CABG were noted in 7.3 and 3.1% of cases, while in the control group, the corresponding figures were 8.5 and 0% (р = 0,110 и χ2 = 2554). The presence of stenoses >50% in the coronary vessels in the first group was detected in 4.3% of patients and in the second group—in 7.8% of the respondents (р = 0.562 и χ2 = 0.225). An individual conversation awareness of patients of acute coronary disease has been established in 53.5% of patients with hypertension and 38.3%—in the control group (p = 0.069; χ2 = 3.295). Analysis of bad habits did not reveal significant differences between groups. The number of nonsmokers among patients with hypertension was 59.5% and in the comparison group—55.3%. The number of smokers in group 2 was 24.1 and 25.5% in group 1; the number of people who stop smoking in group 2 was 16.4% and in 1 group—19.2%.

Analysis of the main ECG changes in ACS/AMI in the studied patient groups revealed that for individuals with hypertension, the most characteristic are STsegment depression (35.6% in 2 g and 25.5% in 1 g; p = 0.228; χ2 = 1.455) and inversion of the T-wave without ST-displacement (16.3% in 2g and 8.5% in 1g; p = 0.234; χ2 = 1.418), while ST-elevation was observed <15% of cases (Figure 7). The distribution of patients of group 2 according to the level of BP showed that

An in-depth comparative assessment of the clinical and functional parameters of

However, the correlation analysis did not reveal the relationship between HR and BP values (p = 0.564; t = 0.576). In addition, patients with hypertension had elevated levels of blood triglycerides, especially those with hypertension first and third degree (p = 0.0000); however, indicators of total cholesterol were lower than

optimal, normal, and high-normal BP occurred in 37.7% of cases, and in the remaining 62.3%, there was AH of varying severity (Figure 8). The distribution of optimal, normal, and high-normal levels of BP in the second group is probably due

patients depending on the degree of AH showed that in patients with grade 2 hypertension, the number of patients with DM was much higher than among patients with grade 1 hypertension or grade 3 hypertension, but the average blood glucose level was not a significant difference. It was also found that over 50% of patients with AH, regardless of its degree, were characterized by elevated

to received antihypertensive therapy.

Lipid blood spectrum of the compared patient groups.

Indicators Group 1

DOI: http://dx.doi.org/10.5772/intechopen.88134

Indicators Group 1

The average HR in patients with

The number of patients with normal levels of TCh

The average level of TCh in patients with HChE

The number of patients with

HR > 80 beats/min

Table 4.

hypertension.

HChE

Table 5.

(control) n = 47

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

Average HR, beats/min 80.8 16.7 84.6 18.2 0.174

Mean SBP, mm Hg 115.9 10.1 143.8 27.7 0.000 Mean DBP, mm Hg 75.1 9.3 87.1 13.8 0.000

Comparative characteristics of hemodynamic parameters of patients depending on the presence of arterial

(control) n = 47

The average level of TCh, mg/dL 205.6 46.6 156.7 92.9 0.000

The average level of TG, mg/dL 143.1 80.7 211.7 186.2 0.013 Notes: n, the number of patients; TCh, total cholesterol; HChE, hypercholesterolemia; TG, triglycerides

Notes: n, the number of patients; SBP and DBP, systolic and diastolic blood pressure

HR > 80 beats/min 34.1 49.9 0.058 3.593

Group 2 (with the presence of АН) n = 385

97.7 15.8 96.3 17.4 0.599

Group 2 (with the presence of АН) n = 385

19.2% 46.2% 0.000 11.438

80.8% 53.8% 0.000 11.438

221.25 36.1 205.1 82.6 0.186

р χ2

р χ2

heart rate.

53

The clinical characteristics included in this fragment of patients showed that the average values of heart rate (HR) in both compared groups practically did not differ; however, HR > 80 beats/min among patients with AH was observed in 49.9% of cases, which is 1, five times more than in people without AH (Table 4).

Of the concomitant nosologies, the presence of type 2 diabetes mellitus (DM) among control group patients occurred in 4.3% and among patients with hypertension—in 34.8% of patients, while the average blood glucose level in group 1 was 5.8 2.6 mmol/L and in group 2 = 6.3 2.9 mmol/L (р = 0.260). Evaluation of the blood glucose level only in patients with DM showed that in the group with hypertension, this indicator was equal to 8.4 3.5 mmol/L, which was 0.7 mmol/L higher than in the first group (р = 0.194).

Significant, but somewhat paradoxical, differences were found in the evaluation of blood lipid spectrum. Namely, the number of patients with hypercholesterolemia was 1.5 times higher among patients of the first group, i.e., without hypertension, which was confirmed by digital indicators in blood tests. However, the average level of triglycerides among respondents without AH was 1.5 times lower than in the comparison group (Table 5).

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134


#### Table 4.

Analysis of anamnestic data showed that in group 2, individuals with myocardial infarction prevailed (33.2 and 8.5%, respectively, in groups 2 and 1; p = 0.000 and χ2 = 10.941). Also, hypertension was significantly more frequently accompanied by the development of chronic heart failure (53.3 and 23.4%, respectively, in groups 2 and 1; p = 0.000 and χ2 = 13.751). Patients with hypertension who underwent PCI or CABG were noted in 7.3 and 3.1% of cases, while in the control group, the corresponding figures were 8.5 and 0% (р = 0,110 и χ2 = 2554). The presence of stenoses >50% in the coronary vessels in the first group was detected in 4.3% of patients and in the second group—in 7.8% of the respondents (р = 0.562 и χ2 = 0.225). An individual conversation awareness of patients of acute coronary disease has been established in 53.5% of patients with hypertension and 38.3%—in the control group (p = 0.069; χ2 = 3.295). Analysis of bad habits did not reveal significant differences between groups. The number of nonsmokers among patients with hypertension was 59.5% and in the comparison group—55.3%. The number of smokers in group 2 was 24.1 and 25.5% in group 1; the number of people who stop

Comparative characteristics of patient growth and weight indicators depending on the presence of arterial

Group 2 (with the presence of АН) n = 385

р χ2

The clinical characteristics included in this fragment of patients showed that the

Of the concomitant nosologies, the presence of type 2 diabetes mellitus (DM)

hypertension—in 34.8% of patients, while the average blood glucose level in group 1 was 5.8 2.6 mmol/L and in group 2 = 6.3 2.9 mmol/L (р = 0.260). Evaluation of the blood glucose level only in patients with DM showed that in the group with hypertension, this indicator was equal to 8.4 3.5 mmol/L, which was 0.7 mmol/L

Significant, but somewhat paradoxical, differences were found in the evaluation of blood lipid spectrum. Namely, the number of patients with hypercholesterolemia was 1.5 times higher among patients of the first group, i.e., without hypertension, which was confirmed by digital indicators in blood tests. However, the average level of triglycerides among respondents without AH was 1.5 times lower than in the

average values of heart rate (HR) in both compared groups practically did not differ; however, HR > 80 beats/min among patients with AH was observed in 49.9%

of cases, which is 1, five times more than in people without AH (Table 4).

among control group patients occurred in 4.3% and among patients with

smoking in group 2 was 16.4% and in 1 group—19.2%.

higher than in the first group (р = 0.194).

Indicators Group 1 (control)

Cardiac Diseases and Interventions in 21st Century

index

Table 3.

hypertension.

n = 47

Number of men, % 59.6% 53.9%

Age, years 57.1 9.8 58.6 7.9 0.233

Weight, kg 80.7 16.6 81.7 12.4 0.617 Height, cm 168.4 4.8 167.8 7.3 0.583 BMI, kg/m<sup>2</sup> 28.5 5.9 29.1 4.1 0.370 Normal weight, % 29.8 15.1 0.019 5.520 Obesity 1 degree, % 36.2 46.2 0.249 1.331 Obesity 2 degree, % 25.5 37.1 0.160 1.976 Obesity 3 degree, % 8.5 1.6 0.013 6.143 Notes: n, number of patients; АН, arterial hypertension; %, percentage of patients with this symptom; BMI, body mass

comparison group (Table 5).

52

Comparative characteristics of hemodynamic parameters of patients depending on the presence of arterial hypertension.


#### Table 5.

Lipid blood spectrum of the compared patient groups.

Analysis of the main ECG changes in ACS/AMI in the studied patient groups revealed that for individuals with hypertension, the most characteristic are STsegment depression (35.6% in 2 g and 25.5% in 1 g; p = 0.228; χ2 = 1.455) and inversion of the T-wave without ST-displacement (16.3% in 2g and 8.5% in 1g; p = 0.234; χ2 = 1.418), while ST-elevation was observed <15% of cases (Figure 7).

The distribution of patients of group 2 according to the level of BP showed that optimal, normal, and high-normal BP occurred in 37.7% of cases, and in the remaining 62.3%, there was AH of varying severity (Figure 8). The distribution of optimal, normal, and high-normal levels of BP in the second group is probably due to received antihypertensive therapy.

An in-depth comparative assessment of the clinical and functional parameters of patients depending on the degree of AH showed that in patients with grade 2 hypertension, the number of patients with DM was much higher than among patients with grade 1 hypertension or grade 3 hypertension, but the average blood glucose level was not a significant difference. It was also found that over 50% of patients with AH, regardless of its degree, were characterized by elevated heart rate.

However, the correlation analysis did not reveal the relationship between HR and BP values (p = 0.564; t = 0.576). In addition, patients with hypertension had elevated levels of blood triglycerides, especially those with hypertension first and third degree (p = 0.0000); however, indicators of total cholesterol were lower than

Figure 7. The occurrence of ECG changes in the compared groups of patients (%).

Figure 8. Distribution of patients in group 2 by blood pressure levels.

in the control group. When comparing the lipid spectrum with the blood pressure numbers, no correlation dependence was found. According to ECG parameters, there were no significant differences in the analyzed patients; with the exception of ST-elevation, the phenomenon of which was less frequently observed among respondents with AH 2 degree (Table 6).

The evaluation of the treatment of patients with AH, regardless of its severity, showed that all patients in this category took on average 2.4 1.6 drugs, which was two times higher than in the control group; however, antihypertensive drugs such as BB and ACE inhibitors among people with AH were used much more often than in the control group (Table 7).

## 2.4 Comparative analysis of patients with acute coronary events depending on the presence/absence of diabetes mellitus (data from the "RACSMI-Uz" registry)

To assess the impact of DM, two groups were identified: group 1, 207 respondents without diabetes (control group), and group 2, 159 people with DM, of which 41.5% had diabetes compensated by diet, 38.4% had diabetes compensated by taking hypoglycemic drug tablets, 6.9% had diabetes compensated by insulin

Comparative analysis of clinical and functional parameters depending on the degree of hypertension.

\*\*the significance of differences in comparison with the control group at p < 0.001Notes: OH, total cholesterol; TG, triglycerides; BB, β-blockers; ACE inhibitors, angiotensin-converting enzyme inhibitors; ARA, angiotensin II receptor

intake, and 13.2% had newly diagnosed type 2 diabetes.

\*significance of differences in comparison with the control group at p < 0.05

Indicators Control

DOI: http://dx.doi.org/10.5772/intechopen.88134

Number of men, % of

The average level of blood glucose, mmol/L

HR > 80 beats/min, % of

Characteristics of BP

The average level of TCh,

The average level of TG,

Inversion of the T-wave without ST, %

Commitment to therapy The average number of medications taken

patients

patients

mg/dL

mg/dL

antagonists.

Table 6.

55

ECG changes

group (n = 47)

Patients with AH 1 (n = 105)

Age, years 57.1 9.8 58.8 7.9 60.2 6.6\* 58.9 7.3

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

BMI, kg/m2 28.5 5.9 29.1 3.8 27.9 3.1 30.3 4.9 DM, % of patients 4.3 29.5\* 38\*\* 30.9\*

Average HR, beats/min 80.8 16.7 84.3 13.7 85.5 16.4 84.8 12.7

Mean SBP, mm Hg 115.9 10.1 143.7 4.9\*\* 162.2 4.1\*\* 193.8 21.3\*\* Average DBP, mm Hg 75.1 9.3 89.1 7.0\*\* 95.9 7.3\*\* 101.7 17.3\*\*

Normal ECG, % 27.7 27.6 24.1 28.6 Elevation ST, % 25.5 12.4 6.3\* 14.3 Depression ST, % 25.5 42.9 40.5 40.5

Other changes 12.8 2.8%\* 8.9 7.1

BB, % 27.7 53.3\* 48.1\* 54.8\* ACE inhibitors/ARA, % 17.0/0 46.7/8.6\*\* 51.9/3.8\* 57.1/7.1\*\* Calcium antagonists 6.4 11.4 6.3 14.3 Aspirin 40.4 74.3\*\* 74.7\*\* 80.9\*\* Nitrates 17.0 22.9 31.6 47.6\* Statins 10.6 19.1 20.2 11.9

Patients with AH 2 (n = 79)

59.6 55.2 41.8 57.1

5.8 2.6 6.1 2.4 6.1 2.2 6.0 2.4

34.1 58.1\* 55.7\* 59.5\*

205.6 46.6 177.3 82.5\* 1977 45.2 167.3 31.5\*\*

146.1 80.7 222.5 188.7\* 160.1 53.4 232.3 45.3\*\*

8.5 14.3 20.2 9.5

1.2 1.5 2.4 1.5\*\* 2.4 1.6\*\* 2.7 1.7\*\*

Patients with AH 3 (n = 42)


## Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

\*significance of differences in comparison with the control group at p < 0.05

\*\*the significance of differences in comparison with the control group at p < 0.001Notes: OH, total cholesterol; TG, triglycerides; BB, β-blockers; ACE inhibitors, angiotensin-converting enzyme inhibitors; ARA, angiotensin II receptor antagonists.

#### Table 6.

in the control group. When comparing the lipid spectrum with the blood pressure numbers, no correlation dependence was found. According to ECG parameters, there were no significant differences in the analyzed patients; with the exception of ST-elevation, the phenomenon of which was less frequently observed among

The evaluation of the treatment of patients with AH, regardless of its severity, showed that all patients in this category took on average 2.4 1.6 drugs, which was two times higher than in the control group; however, antihypertensive drugs such as BB and ACE inhibitors among people with AH were used much more often than

2.4 Comparative analysis of patients with acute coronary events depending on the presence/absence of diabetes mellitus (data from the "RACSMI-Uz"

To assess the impact of DM, two groups were identified: group 1, 207 respondents without diabetes (control group), and group 2, 159 people with DM, of which

respondents with AH 2 degree (Table 6).

Distribution of patients in group 2 by blood pressure levels.

The occurrence of ECG changes in the compared groups of patients (%).

Cardiac Diseases and Interventions in 21st Century

in the control group (Table 7).

registry)

54

Figure 7.

Figure 8.

Comparative analysis of clinical and functional parameters depending on the degree of hypertension.

41.5% had diabetes compensated by diet, 38.4% had diabetes compensated by taking hypoglycemic drug tablets, 6.9% had diabetes compensated by insulin intake, and 13.2% had newly diagnosed type 2 diabetes.


\* p = 0.036 in the intragroup comparison of the average age of men and women

Note: BMI, body mass index; n, the number of patients; DM, diabetes mellitus

#### Table 7.

Gender-anthropometric characteristics of compared groups of patients.

The study found that patients in group 2 were much older than patients in group 1 (mean age of patients in group 1 = 57.7 8.5 years and in group 2 = 60.1 7.6 years; p = 0.005). The age difference was due to the predominance of young people in group 1. Namely, the category of ≤45 years in group 1 was 9.7%, and in group 2– 3.8% (р = 0.049 и χ2 = 3874) of respondents (Figure 9).

Figure 9.

Figure 10.

57

The distribution of patients by age. \*significance of differences between groups at p < 0.05.

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

DOI: http://dx.doi.org/10.5772/intechopen.88134

Graph of correlation between the presence of diabetes and age of patients. p = 0.005; t = 2781; r = 0.145. On the X-axis, "0" is the absence of SD and "1" is the presence of SD; on the Y-axis, age of patients in years.

On the contrary, in group 2 the number of patients older than 66 years was greater than in the comparison group (28.9 vs. 21.7%, р = 0.170 и χ2 = 1880).

The average age of men was younger than the average age of women, regardless of the presence or absence of diabetes. This difference in group 2 reaches the level of confidence (Table 7). Correlation analysis between age and the presence of DM has established a direct relationship (Figure 10).

Gender-anthropometric characteristics of patients are presented in Table 7, from which it can be seen that in group 2, the number of women was greater than in group 1, and the weight characteristics of patients in group 2 exceeded those in patients of the comparison group. A more detailed analysis found that among persons with DM, obesity of varying severity was more common (52.2 vs. 28.5%, respectively, in groups 2 and 1; р = 0.000 и χ2 = 20,284).

A correlation analysis revealed a direct relationship between blood glucose and BMI, as well as between gender and diabetes, while in the latter case, the correlation reached a statistically significant level (Figures 11 and 12).

BP was measured in 94.7% of respondents in the first group and in 95.6% in the second group: a total of 348 patients. The mean figures of both systolic and diastolic BP were comparable between the groups, as well as the quantitative components of the main gradations of BP. The presence of AH in individuals of group 1 was in 48.3% of cases, and in group 2—in 49.7% of cases (Table 8). When carrying out the correlation analysis, we did not reveal any relationship between the blood glucose level and the BP values.

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

Figure 9.

The study found that patients in group 2 were much older than patients in group 1 (mean age of patients in group 1 = 57.7 8.5 years and in group 2 = 60.1 7.6 years; p = 0.005). The age difference was due to the predominance of young people in group 1. Namely, the category of ≤45 years in group 1 was 9.7%, and in group 2–

DM (n = 207)

), % 19.3 12.6

), % 52.2 35.2 0.002 9.777

), % 25.6 39.6 0.006 7.529

), % 2.9 8.8 0.020 5.450

), % 0 3.8 0.016 5.774

Number of men, % 62.3 46.5 0.004 8.435

The average age of men, years 57.1 8.9 58.7 8.1 0.208 The average age of women, years 58.6 7.7 61.2 6.9\* 0.024 Weight, kg 80.1 11.2 83.8 13.5 0.004 Height, cm 168.4 6.6 168.7 6.9 0.673 BMI, kg/m<sup>2</sup> 28.2 3.5 30.2 4.6 0.000 Obesity, n (%) 80.7 87.4 0.113 2.513

Group 2 with DM (n = 159)

р χ2

On the contrary, in group 2 the number of patients older than 66 years was greater than in the comparison group (28.9 vs. 21.7%, р = 0.170 и χ2 = 1880).

Gender-anthropometric characteristics of patients are presented in Table 7, from which it can be seen that in group 2, the number of women was greater than in group 1, and the weight characteristics of patients in group 2 exceeded those in patients of the comparison group. A more detailed analysis found that among persons with DM, obesity of varying severity was more common (52.2 vs. 28.5%,

A correlation analysis revealed a direct relationship between blood glucose and BMI, as well as between gender and diabetes, while in the latter case, the correlation

BP was measured in 94.7% of respondents in the first group and in 95.6% in the second group: a total of 348 patients. The mean figures of both systolic and diastolic BP were comparable between the groups, as well as the quantitative components of the main gradations of BP. The presence of AH in individuals of group 1 was in 48.3% of cases, and in group 2—in 49.7% of cases (Table 8). When carrying out the correlation analysis, we did not reveal any relationship between the blood glucose

The average age of men was younger than the average age of women, regardless of the presence or absence of diabetes. This difference in group 2 reaches the level of confidence (Table 7). Correlation analysis between age and the presence of DM

3.8% (р = 0.049 и χ2 = 3874) of respondents (Figure 9).

p = 0.036 in the intragroup comparison of the average age of men and women Note: BMI, body mass index; n, the number of patients; DM, diabetes mellitus

Gender-anthropometric characteristics of compared groups of patients.

Indicator Group 1 without

Cardiac Diseases and Interventions in 21st Century

Normal weight (BMI = 18–24.9 kg/m<sup>2</sup>

Excess weight (BMI = 25–29.9 kg/m<sup>2</sup>

Obesity grade (BMI = 30–34.9 kg/m<sup>2</sup>

Obesity grade 2 (BMI = 35–39.9 kg/m<sup>2</sup>

Obesity grade 3 (BMI ≥40 kg/m<sup>2</sup>

\*

Table 7.

Number of women, % 37.7 53.5

respectively, in groups 2 and 1; р = 0.000 и χ2 = 20,284).

reached a statistically significant level (Figures 11 and 12).

has established a direct relationship (Figure 10).

level and the BP values.

56

The distribution of patients by age. \*significance of differences between groups at p < 0.05.

Figure 10.

Graph of correlation between the presence of diabetes and age of patients. p = 0.005; t = 2781; r = 0.145. On the X-axis, "0" is the absence of SD and "1" is the presence of SD; on the Y-axis, age of patients in years.

#### Figure 11.

The graph of the correlation between the level of blood glucose (mmol/L): X-axis and body mass index (kg/m<sup>2</sup> ); Y-axis, p = 0.202; t = 1.279; r = 0.097.

Analysis of blood triglyceride levels revealed a clear prevalence of this indicator

(n = 196)

Mean SBP, mm Hg 138.8 29.4 138.3 28.4 0.873 Mean DBP, mm Hg 85.0 15.9 83.9 14.0 0.501 Optimal BP < 120 mm Hg, % 15.8 19.7 0.417 0.659 Normal BP, 120–129 mm Hg, % 15.3 15.1 0.916 0.011 High-normal BP, 130–139 mm Hg, % 17.9 13.2 0.297 1.090 AH-1 degree, 140–159 mm Hg, % 25.5 21.7 0.485 0.487 AH-2 degrees, 160–179 mm Hg, % 15.8 20.4 0.334 0.933 AH-3 degrees, ≥180 mm.rt.st., % 9.7 9.9 0.898 0.016 Notes: n, the number of patients; SBP/DBP, systolic/diastolic blood pressure; DM, diabetes mellitus; AH, arterial

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

Group 2 with DM (n = 152)

р χ2

261.9 217.85 mg/dL in the second group, p = 0.015). The number of patients with hypertriglyceridemia in group 1 was 17.5% and in group 2—41.7% of patients (p = 0.054; χ2 = 3.701), while the content of blood triglycerides in group 1 = 283.0 90.1 mg/dL for and in group 2 = 426.2 222.6 mg/dL (p = 0.120). When carrying out the correlation analysis, a directly proportional relationship was established; i.e., an increase in blood glucose levels is directly correlated with an increase in blood triglyceride levels, while the dependence was not so pronounced with total cholesterol (Figures 13 and 14). From the above, it follows that the presence of diabetes is associated with dyslipidemia, in particular with hypertriglyceridemia. From anamnestic data, it was found that chronic heart failure was more often observed among patients of group 2 (56.0 vs. 45.4% of cases in groups 2 and 1, respectively, p = 0.058, χ2 = 3.603). 34.8% in the first group and 31.4% of patients in the second group were indicated on the transferred AMI in the anamnesis. Previously, PCI/CABG was observed in 9.2 and 3.4% of respondents in group 1 and 9.4 and 1.9% of persons in group 2. However, stenosis >50% was more common in patients with diabetes than in the comparison group (78.9% in group 1 and 93.3% in

One of the fragments of the study was the analysis of thrombolytic therapy (TLT). From this perspective, it was found that TLT in group 1 was performed in 54.1% and in group 2 in 59.1% of patients. At the same time, the success of the TLT procedure in group 1 was 62.5% and in group 2—60.6%. This was also confirmed during the correlation analysis, which showed that the blood glucose level did not affect the success of the TLT procedure (p = 0.944; t = 0.069; r = 0.005). However, a more detailed analysis found that among patients of group 2, the form of DM is of considerable importance when conducting TLT. Figure 15 presents a graph of the correlation dependence between various forms of DM and the success of TLT. As can be seen in Figure 15, the TLT procedure was more successful in patients who are on glucose-lowering drugs, including insulin therapy, and in patients with

Thus, in patients with ACS/AMI, comorbid with diabetes, the success of thrombolysis was directly dependent on the form of diabetes and ongoing antidiabetic

in patients with diabetes (159.8 83.1 mg/dL in the first group and

Blood pressure indicators depending on the presence or absence of diabetes.

Indicator Group 1 without DM

DOI: http://dx.doi.org/10.5772/intechopen.88134

group 2, p = 0.005, χ2 = 7.905).

therapy.

59

hypertension

Table 8.

newly diagnosed forms of the disease (Figure 15).

#### Figure 12.

Graph of correlation between the presence of DM and gender. p = 0.001; t = 3.156; r = 0.163. On the X-axis, "0" is the absence of DM and "1" is the presence of DM; on the Y-axis, "1" men and "2" women.

Analysis of lipid metabolism was carried out in 1/5 of the subjects, 40 of them from group 1 and 36 from group 2. In this aspect, it was found that the levels of total cholesterol in group 1 = 187.8 51.5 mg/dL and in group 2 = 199.1 47.8 mg/dL (p = 0.326). The number of patients with total cholesterol over 180 mg/dL in the first group was 55.0% and in the second group—61.1% of patients, while the average level of total blood cholesterol in the first group = 222.1 40.2 mg/dL and in group 2 = 224.1 43.6 mg/dL (p = 0.514).


Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

Notes: n, the number of patients; SBP/DBP, systolic/diastolic blood pressure; DM, diabetes mellitus; AH, arterial hypertension

#### Table 8.

Blood pressure indicators depending on the presence or absence of diabetes.

Analysis of blood triglyceride levels revealed a clear prevalence of this indicator in patients with diabetes (159.8 83.1 mg/dL in the first group and 261.9 217.85 mg/dL in the second group, p = 0.015). The number of patients with hypertriglyceridemia in group 1 was 17.5% and in group 2—41.7% of patients (p = 0.054; χ2 = 3.701), while the content of blood triglycerides in group 1 = 283.0 90.1 mg/dL for and in group 2 = 426.2 222.6 mg/dL (p = 0.120). When carrying out the correlation analysis, a directly proportional relationship was established; i.e., an increase in blood glucose levels is directly correlated with an increase in blood triglyceride levels, while the dependence was not so pronounced with total cholesterol (Figures 13 and 14). From the above, it follows that the presence of diabetes is associated with dyslipidemia, in particular with hypertriglyceridemia.

From anamnestic data, it was found that chronic heart failure was more often observed among patients of group 2 (56.0 vs. 45.4% of cases in groups 2 and 1, respectively, p = 0.058, χ2 = 3.603). 34.8% in the first group and 31.4% of patients in the second group were indicated on the transferred AMI in the anamnesis. Previously, PCI/CABG was observed in 9.2 and 3.4% of respondents in group 1 and 9.4 and 1.9% of persons in group 2. However, stenosis >50% was more common in patients with diabetes than in the comparison group (78.9% in group 1 and 93.3% in group 2, p = 0.005, χ2 = 7.905).

One of the fragments of the study was the analysis of thrombolytic therapy (TLT). From this perspective, it was found that TLT in group 1 was performed in 54.1% and in group 2 in 59.1% of patients. At the same time, the success of the TLT procedure in group 1 was 62.5% and in group 2—60.6%. This was also confirmed during the correlation analysis, which showed that the blood glucose level did not affect the success of the TLT procedure (p = 0.944; t = 0.069; r = 0.005). However, a more detailed analysis found that among patients of group 2, the form of DM is of considerable importance when conducting TLT. Figure 15 presents a graph of the correlation dependence between various forms of DM and the success of TLT. As can be seen in Figure 15, the TLT procedure was more successful in patients who are on glucose-lowering drugs, including insulin therapy, and in patients with newly diagnosed forms of the disease (Figure 15).

Thus, in patients with ACS/AMI, comorbid with diabetes, the success of thrombolysis was directly dependent on the form of diabetes and ongoing antidiabetic therapy.

Analysis of lipid metabolism was carried out in 1/5 of the subjects, 40 of them from group 1 and 36 from group 2. In this aspect, it was found that the levels of total cholesterol in group 1 = 187.8 51.5 mg/dL and in group 2 = 199.1 47.8 mg/dL (p = 0.326). The number of patients with total cholesterol over 180 mg/dL in the first group was 55.0% and in the second group—61.1% of patients, while the average level of total blood cholesterol in the first group = 222.1 40.2 mg/dL and in

Graph of correlation between the presence of DM and gender. p = 0.001; t = 3.156; r = 0.163. On the X-axis,

"0" is the absence of DM and "1" is the presence of DM; on the Y-axis, "1" men and "2" women.

The graph of the correlation between the level of blood glucose (mmol/L): X-axis and body mass index (kg/m<sup>2</sup>

);

group 2 = 224.1 43.6 mg/dL (p = 0.514).

Figure 11.

Figure 12.

58

Y-axis, p = 0.202; t = 1.279; r = 0.097.

Cardiac Diseases and Interventions in 21st Century

Figure 13.

The graph of the correlation between the level of blood glucose and total cholesterol (n = 76). p = 0.905; t = 0.119; r = 0.014; X-axis, total cholesterol values; Y-axis, blood glucose values.

2.5 Discussion of the results of the study "RACSMI-Uz"

Figure 15.

61

numeral "2," unsuccessful TLT.

pausal period of women included in this register.

Gender is a very significant factor in the occurrence and course of AMI. Men get sick much more often than women, especially at young and middle ages, but with increasing age, these differences disappear. The cause of female immunity to AMI at a young age is due to the subtleties of the hormonal system of the body of women, with a significant role belongs to an increased amount of estrogen. This is the main hormone of the female reproductive system, taking an active part in the reproductive function. Estrogen in the female body performs a number of functions, one of which is the dilation of the heart's own blood vessels, which contributes to leaching of sclerotic plaques and prolonging the normal functioning of the myocardium [13]. After menopause, the hormonal system of the female body significantly changes its work, and the content of estrogen in the blood decreases. This leads to a violation of the protection of coronary vessels from harmful influences and the development of CHD [13]. The results of our register revealed that women with ACS/AMI were older than men, and, for the most part, age over 50 years prevailed. Unfortunately, in the map-register used by us, there were no questions concerning the state of health of the female body; therefore, we cannot judge the hormonal status of the analyzed female population. However, the identified age limit—over 50 years old—can probably be considered as evidence of the meno-

Graph of correlation between various forms of diabetes and the success of the TLT procedure. p = 0.171; t = 1.3784 r = 0.142; Notes: On the X-axis, the number "2," DM compensated by diet; the number "3," DM compensated by the intake of tablets medications; the number "4," DM compensated by insulin; and the number "5," the first revealed diabetes; on the Y-axis, numeral "1," a successful thrombolytic therapy (TLT), and the

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

DOI: http://dx.doi.org/10.5772/intechopen.88134

Most of the generally recognized RFs of CVD are common for men and women; however, the accumulated scientific data to date indicate the presence of certain features of the manifestation of RF in the female population [14–16]. In our

#### Figure 14.

The graph of the correlation between the level of blood glucose and triglycerides (n = 76). p = 0.021; t = 2.371; r = 0,279. On the X-axis, the values of triglycerides; Y-axis, blood glucose values.

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

Figure 15.

Graph of correlation between various forms of diabetes and the success of the TLT procedure. p = 0.171; t = 1.3784 r = 0.142; Notes: On the X-axis, the number "2," DM compensated by diet; the number "3," DM compensated by the intake of tablets medications; the number "4," DM compensated by insulin; and the number "5," the first revealed diabetes; on the Y-axis, numeral "1," a successful thrombolytic therapy (TLT), and the numeral "2," unsuccessful TLT.

#### 2.5 Discussion of the results of the study "RACSMI-Uz"

Gender is a very significant factor in the occurrence and course of AMI. Men get sick much more often than women, especially at young and middle ages, but with increasing age, these differences disappear. The cause of female immunity to AMI at a young age is due to the subtleties of the hormonal system of the body of women, with a significant role belongs to an increased amount of estrogen. This is the main hormone of the female reproductive system, taking an active part in the reproductive function. Estrogen in the female body performs a number of functions, one of which is the dilation of the heart's own blood vessels, which contributes to leaching of sclerotic plaques and prolonging the normal functioning of the myocardium [13]. After menopause, the hormonal system of the female body significantly changes its work, and the content of estrogen in the blood decreases. This leads to a violation of the protection of coronary vessels from harmful influences and the development of CHD [13]. The results of our register revealed that women with ACS/AMI were older than men, and, for the most part, age over 50 years prevailed. Unfortunately, in the map-register used by us, there were no questions concerning the state of health of the female body; therefore, we cannot judge the hormonal status of the analyzed female population. However, the identified age limit—over 50 years old—can probably be considered as evidence of the menopausal period of women included in this register.

Most of the generally recognized RFs of CVD are common for men and women; however, the accumulated scientific data to date indicate the presence of certain features of the manifestation of RF in the female population [14–16]. In our

Figure 14.

60

Figure 13.

The graph of the correlation between the level of blood glucose and triglycerides (n = 76). p = 0.021; t = 2.371;

The graph of the correlation between the level of blood glucose and total cholesterol (n = 76). p = 0.905;

t = 0.119; r = 0.014; X-axis, total cholesterol values; Y-axis, blood glucose values.

Cardiac Diseases and Interventions in 21st Century

r = 0,279. On the X-axis, the values of triglycerides; Y-axis, blood glucose values.

registry, the majority of patients had combined cardiac pathology, with the most frequent option being a combination of CHD and AH (over 80% of patients).

our study is probably due to the fact that patients receiving hypoglycemic drugs, including insulin therapy, are characterized by relatively intact endothelial functionality and, thus, have a more favorable prognosis than patients, adhering to

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

Numerous studies confirm the positive effect of treatment standards for treating one or the other diseases [27]. The results of our register showed that persons with ACS/AMI comorbid with AH are characterized by high adherence to therapy with β-blockers and ACE inhibitors; however, despite this, they still developed destabilization of CHD, probably due to, as mentioned above, the develop-

In work Wang et al. [28] when analyzing 382 elderly patients from six Macau Medical Centers, China, the best adherence to therapy was observed in people over 65 years of age. A similar trend has occurred in our register. As for the predominance of women in the group of committed patients that we received during the course of work, this fact is confirmed by other researchers who show that women

Thus, the register "RACSMI-Uz" conducted in the clinic conditions of the

obtaining objective data on the demographic, anamnestic, and clinical characteristics of patients with acute coronary pathology in only one of the districts of Tashkent city. The results obtained revealed both positive aspects in the treatment plan of this category of patients and established a number of issues that require

ACS/AMI is more commonly reported in men. In the age aspect, men with ACS/ AMI turned out to be younger than women, and in terms of weight characteristics,

With ACS/AMI, gender-independent risk factors (RF) were hypertension and hypercholesterolemia GHS, and gender-related factors were smoking (for men) and carbohydrate metabolism disorders and obesity (for women). The history of cardiovascular catastrophes was prerogative of males, while age seemed to be a controversial point in the development of this or that damage (TMI occurred in

Adherence to therapy in women was slightly higher than in men, although in a proportional ratio in the accepted groups of drugs, there were no significant differences between men and women. However, a direct correlation was found between the age of the respondents and the number of medications taken per day. Adherence to taking drugs in patients with ACS/AMI with comorbid hypertension was

Arterial hypertension as a risk factor was recorded in 89% of patients with ACS/ AMI, of which in 36.7% of cases hypertension was noted in history and in 52.3% of

In ACS/AMI, concomitant comorbid conditions, such as obesity, previous myocardial infarction, chronic heart failure, and type 2 diabetes, were the prerogative of individuals with hypertension and, accordingly, were characterized by a large

In ACS/AMI, the number of patients with heart rate > 80 beats/min among patients with AH was 1.5 times more than among those without it; however, when considering heart rate depending on the degree of AH, no significant differences

Republican Specialized Scientific Medical Center for Cardiology allowed

dietary recommendations only.

further study.

3. Conclusions

were found.

63

ment of refractoriness to drug therapy.

DOI: http://dx.doi.org/10.5772/intechopen.88134

are more committed to treatment than men [29–31].

obesity of varying severity prevailed among women.

younger men and stroke in older men compared with women).

two times higher than among those without hypertension.

cases—hypertension of various severity occurred at the present time.

number of patients with stenotic >50% of the coronary arteries.

The study found that the average level of triglycerides in respondents with hypertension was 1.5 times higher than in the comparison group. According to scientific information sources, an increased level of triglycerides is observed in those who already have high levels of low-density lipoproteins and low levels of high-density lipoproteins in the blood; are obese; have type 2 diabetes, decreased thyroid function, and neurotic syndrome; and consume excessive amounts of alcohol. It is completely natural to ask whether triglycerides are related to an increased cardiovascular risk in these patients or whether they simply reflect metabolic disturbances. The final answer to the question about the participation of triglycerides in the process of atherosclerotic vascular lesion has not yet been received. It is assumed that an increase in triglycerides reflects an increased content of atherogenic lipid particles, such as intermediate density lipoproteins and very low-density lipoproteins, which, among other things, cause and maintain inflammation of the vascular wall [17]. Probably, this is the explanation of the hypertriglyceridemia detected in our registry specifically in individuals with hypertension.

In the CREATE-ECLA study, 30-day mortality in patients with AMI with STelevation differed significantly depending on the glucose content in the blood and was 6.6% of patients with glycemia within the lower tertile and 14% of patients with glycemia within the upper tertile [18, 19]. According to the register of the RECORD, the presence of diabetes, as the FR of the development of ACS, occurred in 18.1% of respondents [20]. The results of our study "ROXIM-Uz" revealed that 34.8% of patients with hypertension had concomitant diabetes.

It is well-known that people who are overweight and obese have a higher risk of developing metabolic syndrome, type 2 diabetes, AH, and CHD [21]. Findings from epidemiological studies have repeatedly confirmed the strong positive relationship between obesity and the risk of developing DM. In our study, a similar trend is observed. In the group of patients with DM, the number of people with obesity of varying severity was 1.8 times greater than in the group of patients without diabetes.

One of the most important causes of high cardiovascular morbidity and mortality in patients with diabetes is the accelerated development of the atherosclerotic process [22]. In our study, it was found that stenosis >50% was more often detected in patients with diabetes (93.3%) than in the comparison group (78.9%). Endothelial dysfunction is the earliest stage in the development of adverse cardiovascular complications in patients with diabetes [23]. However, the relationship between diabetes and vascular pathology remains unclear.

Under conditions of hyperglycemia, there is an increased formation of the end products of glycation and their precursors, which leads to a change in the structure of blood proteins and the extracellular matrix, disrupting the function of nerve fibers. Levels of all previous glycolysis intermediates become elevated, which triggers alternative paths; glyceraldehyde-3-phosphate, glycerol, and methylglyoxal enter the pathway of protein kinase C and the end products of glycation, fructose-6 phosphate enters the hexosamine pathway, and glucose itself enters the polyol pathway. All the above pathological pathways for utilization of glucose and its metabolites cause diabetic complications and damage to the nervous tissue and the vascular wall (neuropathy and angiopathy) [24].

To verify the positive effect of insulin on the course of AMI in patients with diabetes, a diabetes, insulin, glucose infusion in acute myocardial infarction (DIGAMI) study was conducted, which showed that with the development of AMI in patients with diabetes, adding to therapy the glucose-insulin-potassium mixture followed by insulin therapy can reduce mortality after 1 and 3 years. [25, 26]. The direct correlation between the forms of diabetes and the success of TLT identified in Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

our study is probably due to the fact that patients receiving hypoglycemic drugs, including insulin therapy, are characterized by relatively intact endothelial functionality and, thus, have a more favorable prognosis than patients, adhering to dietary recommendations only.

Numerous studies confirm the positive effect of treatment standards for treating one or the other diseases [27]. The results of our register showed that persons with ACS/AMI comorbid with AH are characterized by high adherence to therapy with β-blockers and ACE inhibitors; however, despite this, they still developed destabilization of CHD, probably due to, as mentioned above, the development of refractoriness to drug therapy.

In work Wang et al. [28] when analyzing 382 elderly patients from six Macau Medical Centers, China, the best adherence to therapy was observed in people over 65 years of age. A similar trend has occurred in our register. As for the predominance of women in the group of committed patients that we received during the course of work, this fact is confirmed by other researchers who show that women are more committed to treatment than men [29–31].

Thus, the register "RACSMI-Uz" conducted in the clinic conditions of the Republican Specialized Scientific Medical Center for Cardiology allowed obtaining objective data on the demographic, anamnestic, and clinical characteristics of patients with acute coronary pathology in only one of the districts of Tashkent city. The results obtained revealed both positive aspects in the treatment plan of this category of patients and established a number of issues that require further study.

## 3. Conclusions

registry, the majority of patients had combined cardiac pathology, with the most frequent option being a combination of CHD and AH (over 80% of patients). The study found that the average level of triglycerides in respondents with hypertension was 1.5 times higher than in the comparison group. According to scientific information sources, an increased level of triglycerides is observed in those who already have high levels of low-density lipoproteins and low levels of high-density lipoproteins in the blood; are obese; have type 2 diabetes, decreased thyroid function, and neurotic syndrome; and consume excessive amounts of alcohol. It is completely natural to ask whether triglycerides are related to an increased cardiovascular risk in these patients or whether they simply reflect metabolic disturbances. The final answer to the question about the participation of triglycerides in the process of atherosclerotic vascular lesion has not yet been received. It is assumed that an increase in triglycerides reflects an increased content of atherogenic lipid particles, such as intermediate density lipoproteins and very low-density lipoproteins, which, among other things, cause and maintain inflammation of the vascular wall [17]. Probably, this is the explanation of the hypertriglyceridemia

Cardiac Diseases and Interventions in 21st Century

detected in our registry specifically in individuals with hypertension.

34.8% of patients with hypertension had concomitant diabetes.

diabetes and vascular pathology remains unclear.

vascular wall (neuropathy and angiopathy) [24].

62

In the CREATE-ECLA study, 30-day mortality in patients with AMI with STelevation differed significantly depending on the glucose content in the blood and was 6.6% of patients with glycemia within the lower tertile and 14% of patients with glycemia within the upper tertile [18, 19]. According to the register of the RECORD, the presence of diabetes, as the FR of the development of ACS, occurred in 18.1% of respondents [20]. The results of our study "ROXIM-Uz" revealed that

It is well-known that people who are overweight and obese have a higher risk of developing metabolic syndrome, type 2 diabetes, AH, and CHD [21]. Findings from epidemiological studies have repeatedly confirmed the strong positive relationship between obesity and the risk of developing DM. In our study, a similar trend is observed. In the group of patients with DM, the number of people with obesity of varying severity was 1.8 times greater than in the group of patients without diabetes. One of the most important causes of high cardiovascular morbidity and mortality in patients with diabetes is the accelerated development of the atherosclerotic process [22]. In our study, it was found that stenosis >50% was more often detected in patients with diabetes (93.3%) than in the comparison group (78.9%). Endothelial dysfunction is the earliest stage in the development of adverse cardiovascular complications in patients with diabetes [23]. However, the relationship between

Under conditions of hyperglycemia, there is an increased formation of the end products of glycation and their precursors, which leads to a change in the structure of blood proteins and the extracellular matrix, disrupting the function of nerve fibers. Levels of all previous glycolysis intermediates become elevated, which triggers alternative paths; glyceraldehyde-3-phosphate, glycerol, and methylglyoxal enter the pathway of protein kinase C and the end products of glycation, fructose-6 phosphate enters the hexosamine pathway, and glucose itself enters the polyol pathway. All the above pathological pathways for utilization of glucose and its metabolites cause diabetic complications and damage to the nervous tissue and the

To verify the positive effect of insulin on the course of AMI in patients with diabetes, a diabetes, insulin, glucose infusion in acute myocardial infarction (DIGAMI) study was conducted, which showed that with the development of AMI in patients with diabetes, adding to therapy the glucose-insulin-potassium mixture followed by insulin therapy can reduce mortality after 1 and 3 years. [25, 26]. The direct correlation between the forms of diabetes and the success of TLT identified in

ACS/AMI is more commonly reported in men. In the age aspect, men with ACS/ AMI turned out to be younger than women, and in terms of weight characteristics, obesity of varying severity prevailed among women.

With ACS/AMI, gender-independent risk factors (RF) were hypertension and hypercholesterolemia GHS, and gender-related factors were smoking (for men) and carbohydrate metabolism disorders and obesity (for women). The history of cardiovascular catastrophes was prerogative of males, while age seemed to be a controversial point in the development of this or that damage (TMI occurred in younger men and stroke in older men compared with women).

Adherence to therapy in women was slightly higher than in men, although in a proportional ratio in the accepted groups of drugs, there were no significant differences between men and women. However, a direct correlation was found between the age of the respondents and the number of medications taken per day. Adherence to taking drugs in patients with ACS/AMI with comorbid hypertension was two times higher than among those without hypertension.

Arterial hypertension as a risk factor was recorded in 89% of patients with ACS/ AMI, of which in 36.7% of cases hypertension was noted in history and in 52.3% of cases—hypertension of various severity occurred at the present time.

In ACS/AMI, concomitant comorbid conditions, such as obesity, previous myocardial infarction, chronic heart failure, and type 2 diabetes, were the prerogative of individuals with hypertension and, accordingly, were characterized by a large number of patients with stenotic >50% of the coronary arteries.

In ACS/AMI, the number of patients with heart rate > 80 beats/min among patients with AH was 1.5 times more than among those without it; however, when considering heart rate depending on the degree of AH, no significant differences were found.

The lipid spectrum of patients with ACS/AMI, comorbid with AH, was characterized by hypertriglyceridemia and relatively intact values of total blood cholesterol, while ST-segment depression and T-wave inversion were more often recorded on the ECG than the ST-elevation.

HChE hypercholesterolemia

DOI: http://dx.doi.org/10.5772/intechopen.88134

mm Hg millimeters of mercury

SBP systolic blood pressure TLT thrombolytic therapy TCh total cholesterol TG triglycerides

Author details

Nagaeva Gulnora

65

Tashkent, Uzbekistan

IDF international diabetes federation

PCI percutaneous coronary intervention TMI transferred myocardial infarction

in Uzbekistan RF risk factors for coronary artery disease

RACSMI-Uz register of acute coronary syndrome and myocardial infarction

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

Republican Specialized Scientific and Practical Medical Center of Cardiology,

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: nagaeva.gulnora@mail.ru

provided the original work is properly cited.

In patients with acute coronary events comorbid with DM, a direct correlation was observed with age, female sex, obesity, and hypertriglyceridemia; on the contrary, no dependence was found between the blood pressure figures and the blood glucose level.

In patients with ACS/AMI, in combination with DM, symptoms of chronic heart failure were observed much more often than in individuals without diabetes, which was probably due to a significantly higher incidence of diagnostically significant stenotic constriction in the coronary arteries.

In patients with ACS/AMI comorbid with DM, the success of thrombolysis was directly dependent on the form of diabetes and hypoglycemic therapy.

## Acknowledgement

I express my gratitude to the employees of the register "RACSMI-Uz" who took part in the collection of information: Aminov A. A., Abidova D. E., Alieva Z. Kh., Mun O. R., Urinov O., and Mamarajapova D. A.

Also, on behalf of the participants of the "RACSMI-Uz" register, the staff of the Republican Specialized Scientific-Practical Medical Center for Cardiology of the Ministry of Health of the Republic of Uzbekistan expresses gratitude to the administrative and medical workers of the city clinical hospitals, family polyclinics and emergency medical care in Mirzo-Ulugbek district of Tashkent for their cooperation and assistance in conducting this project.

## Conflict of interest

In the course of the "ROXIM-Uz" register, no conflicts of interest arose.

## Abbreviations


Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134


## Author details

The lipid spectrum of patients with ACS/AMI, comorbid with AH, was characterized by hypertriglyceridemia and relatively intact values of total blood cholesterol, while ST-segment depression and T-wave inversion were more often

In patients with acute coronary events comorbid with DM, a direct correlation was observed with age, female sex, obesity, and hypertriglyceridemia; on the contrary, no dependence was found between the blood pressure figures and the blood

In patients with ACS/AMI, in combination with DM, symptoms of chronic heart failure were observed much more often than in individuals without diabetes, which was probably due to a significantly higher incidence of diagnostically significant

In patients with ACS/AMI comorbid with DM, the success of thrombolysis was

I express my gratitude to the employees of the register "RACSMI-Uz" who took part in the collection of information: Aminov A. A., Abidova D. E., Alieva Z. Kh.,

Also, on behalf of the participants of the "RACSMI-Uz" register, the staff of the Republican Specialized Scientific-Practical Medical Center for Cardiology of the Ministry of Health of the Republic of Uzbekistan expresses gratitude to the administrative and medical workers of the city clinical hospitals, family polyclinics and emergency medical care in Mirzo-Ulugbek district of Tashkent for their cooperation

In the course of the "ROXIM-Uz" register, no conflicts of interest arose.

directly dependent on the form of diabetes and hypoglycemic therapy.

recorded on the ECG than the ST-elevation.

Cardiac Diseases and Interventions in 21st Century

stenotic constriction in the coronary arteries.

Mun O. R., Urinov O., and Mamarajapova D. A.

and assistance in conducting this project.

ACS acute coronary syndrome AMI acute myocardial infarction

CABG coronary artery bypass grafting

CHD coronary heart disease DM diabetes mellitus DBP diastolic blood pressure ECG electrocardiogram

AH arterial hypertension

BB β-blockers BP blood pressure beats/min beats per minute BMI body mass index CA calcium antagonists CVD cardiovascular diseases

HR heart rate

64

ACE inhibitors angiotensin-converting enzyme inhibitors ARA angiotensin II receptor antagonists

glucose level.

Acknowledgement

Conflict of interest

Abbreviations

Nagaeva Gulnora Republican Specialized Scientific and Practical Medical Center of Cardiology, Tashkent, Uzbekistan

\*Address all correspondence to: nagaeva.gulnora@mail.ru

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

## References

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(DIGAMI study): Effects on mortality at 1 year. Journal of the American College

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mortality. The New England Journal of Medicine. 2015;372(14):1333-1341

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European study. Journal of Hypertension. 2005;23:1261-1266

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of Cardiology. 1995;26(1):57-65

Endocrinology. 2012;1:48-52

460-465

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz"

[16] James MT. Longitudinal studies 4: Matching strategies to evaluate risk. Methods in Molecular Biology. 2015;

[17] 5 and 5 Medicine. Triglycerides are

[18] Deedwania P, Kosiborod M, Barrett E, et al. Hyperglycemia and acute coronary syndrome: A scientific statement from the American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation. 2008;117:

elevated, what does this mean? Available from: http://www.5u5.ru/ articles/articles-drugs/160-triglitsetidy-

[19] Abadal AC, Flores JS. The

Cardiología. 2008;61(5):447-450

[20] Erlich AD, Gratsiansky NA. Independent register of acute coronary syndromes record. Characteristics of patients and treatment before discharge from the hospital. Atherothrombosis.

[21] Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: A systematic review and meta-analysis. JAMA. 2013;309(1):71-82

[22] de Ferranti SD, de Boer IH, Fonseca V, Fox CS, et al. Type 1 diabetes mellitus and cardiovascular disease: A scientific statement from the American Heart Association and American Diabetes Association. Diabetes Care. 2014;37(10):

[23] Kotb NA, Gaber R, Salah W, Elhendy A. Relations among glycemic

2009;1(2):105-119

2843-2863

67

significance of hyperglycemia in acute coronary syndrome. Revista Española de

kak-faktor-riska.html

view/81

1281:133-143

1610-1619

[9] Ametov АS, Kurochkin IO, Zubkov AA. Diabetes mellitus and cardiovascular diseases. Russian Medical Journal. Endocrinology. 2014;13: 943-958

[10] ESC Congress 2018. Focus on Updated Recommendations for the Treatment of Hypertension. Available from: https://www.umj.com. ua/article/130071/esc-congress-2018-a ktsent-na-obnovlennye-rekome ndatsii-po-lecheniyu-arterialnojgipertenzii

[11] Repeated myocardial infarction of unspecified localization (I22.9). Version: Directory of Diseases MedElement. Available from: https://diseases. medelement.com/disease/3787

[12] Methods of diagnosis of myocardial infarction. Available from: https:// serdce.biz/diagnostika/diagnostikainfarkta-miokarda.html

[13] Encyclopedia of Cardiology. Available from: http://cardiobook. ru/priznaki-infarkta-miokarda-uzhenshhin/

[14] Dvoretsky LI, Gibradze NT, Cherkasova NA. Coronary heart disease in women. Russian Medical Journal. 2011;2:79-86. https://www.rmj.ru/ articles/kardiologiya/Ishemicheskaya\_ bolezny\_serdca\_u\_ghenschin/ #ixzz55l0cbLLG

[15] Boytsov SA, Boytsov S, et al. The Cardiovascular Disease Registry (REKVAZA): Diagnostics, combined cardiovascular pathology, comorbidities, and treatment under real outpatient polyclinic practice. Cardiovascular Therapy and Prevention. 2014;13(6):44-50. DOI: 10.15829/1728-8800-2014-6-3-8. ISSN:

Characteristics of Acute Myocardial Damage in Uzbekistan: Data Register "RACSMI-Uz" DOI: http://dx.doi.org/10.5772/intechopen.88134

2619-0125. Available from: https:// cardiovascular.elpub.ru/jour/article/ view/81

References

Wilkins; 2005. 252p

2008;117(6):743-753

3078-3084

Care

urgML.pdf

66

for use in primary care: The

[1] Fletcher RH, Fletcher SW. Clinical Epidemiology: The Essentials. Vol. 4th– xv. London: Lippincott Williams and

Cardiac Diseases and Interventions in 21st Century

Nature Reviews Immunology. 14 Jan

[9] Ametov АS, Kurochkin IO, Zubkov

cardiovascular diseases. Russian Medical

AA. Diabetes mellitus and

Journal. Endocrinology. 2014;13:

[10] ESC Congress 2018. Focus on Updated Recommendations for the Treatment of Hypertension.

Available from: https://www.umj.com. ua/article/130071/esc-congress-2018-a ktsent-na-obnovlennye-rekome ndatsii-po-lecheniyu-arterialnoj-

[11] Repeated myocardial infarction of unspecified localization (I22.9). Version: Directory of Diseases MedElement. Available from: https://diseases. medelement.com/disease/3787

[12] Methods of diagnosis of myocardial infarction. Available from: https:// serdce.biz/diagnostika/diagnostika-

infarkta-miokarda.html

zhenshhin/

#ixzz55l0cbLLG

[13] Encyclopedia of Cardiology. Available from: http://cardiobook. ru/priznaki-infarkta-miokarda-u-

[14] Dvoretsky LI, Gibradze NT,

bolezny\_serdca\_u\_ghenschin/

cardiovascular pathology,

outpatient polyclinic practice. Cardiovascular Therapy and Prevention. 2014;13(6):44-50. DOI: 10.15829/1728-8800-2014-6-3-8. ISSN:

[15] Boytsov SA, Boytsov S, et al. The Cardiovascular Disease Registry (REKVAZA): Diagnostics, combined

comorbidities, and treatment under real

Cherkasova NA. Coronary heart disease in women. Russian Medical Journal. 2011;2:79-86. https://www.rmj.ru/ articles/kardiologiya/Ishemicheskaya\_

2011;11:98-107

943-958

gipertenzii

[2] D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile

Framingham heart study. Circulation.

cardiovascular disease: The Framingham heart study. Circulation. 2009;119(24):

[4] Gliklich RE, Dreyer NA, Leavy MB, editors. Registries for Evaluating Patient Outcomes: A User's Guide [Internet]. 3rd ed. US, Rockville (MD): Agency for Healthcare Research and Quality; Apr 2014. Report No.: 13(14)-EHC111. AHRQ Methods for Effective Health

[5] Ginzburg ML. Evaluation of the immediate and long-term results of treatment of patients with acute myocardial infarction within the

[6] Kronenberg GM, Mel-med S, Polonsky KS, Larsen PR. In: Dedova II, Melnichenko GA, editors. Diabetes and Carbohydrate Metabolism: Translation from English. Moscow, Russia: Reed

Elsiver LLC; 2010. 448p

[7] Lorber D. Importance of

cardiovascular disease risk with type 2 diabetes mellitus. Diabetes, Metabolic Syndrome and Obesity. 2014;7:169-183

[8] Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease.

register [Material doctoral dissertation]. Moscow; 2016. 211p. Available from: https://www.gnicpm.ru/data/files/ dictionary/1547/158/DissertationGinzb

[3] Pencina MJ, D'Agostino RB Sr, Larson MG, Massaro JM, Vasan RS. Predicting the 30-year risk of

[16] James MT. Longitudinal studies 4: Matching strategies to evaluate risk. Methods in Molecular Biology. 2015; 1281:133-143

[17] 5 and 5 Medicine. Triglycerides are elevated, what does this mean? Available from: http://www.5u5.ru/ articles/articles-drugs/160-triglitsetidykak-faktor-riska.html

[18] Deedwania P, Kosiborod M, Barrett E, et al. Hyperglycemia and acute coronary syndrome: A scientific statement from the American Heart Association Diabetes Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation. 2008;117: 1610-1619

[19] Abadal AC, Flores JS. The significance of hyperglycemia in acute coronary syndrome. Revista Española de Cardiología. 2008;61(5):447-450

[20] Erlich AD, Gratsiansky NA. Independent register of acute coronary syndromes record. Characteristics of patients and treatment before discharge from the hospital. Atherothrombosis. 2009;1(2):105-119

[21] Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: A systematic review and meta-analysis. JAMA. 2013;309(1):71-82

[22] de Ferranti SD, de Boer IH, Fonseca V, Fox CS, et al. Type 1 diabetes mellitus and cardiovascular disease: A scientific statement from the American Heart Association and American Diabetes Association. Diabetes Care. 2014;37(10): 2843-2863

[23] Kotb NA, Gaber R, Salah W, Elhendy A. Relations among glycemic control, circulating endothelial cells, nitric oxide, and flow mediated dilation in patients with type 2 diabetes mellitus. Experimental and Clinical Endocrinology & Diabetes. 2012;120(8): 460-465

[24] Podachina SV. From classical therapy of diabetic neuropathy to solving the problem of hyperglycemic memory. Effective pharmacotherapy. Endocrinology. 2012;1:48-52

[25] Malmberg K, Rydén L, Efendic S, Herlitz J, Nicol P, Waldenström A, et al. Randomized trial of insulin glucose infusion followed by subcutaneous insulin treatment in diabetic patients with acute myocardial infarction (DIGAMI study): Effects on mortality at 1 year. Journal of the American College of Cardiology. 1995;26(1):57-65

[26] Malmberg K, Rydén L, Hamsten A, Herlitz J, Waldenström A, Wedel H. Effects of insulin treatment on causespecific one year mortality and morbidity in diabetic patients with acute myocardial infarction. DIGAMI Study Group. Diabetes insulin glucose in acute myocardial infarction. European Heart Journal. 1996;17(9):1337-1344

[27] Roth GA, Forouzanfar MH, Moran AE, Barber R, Nguyen G, Feigin VL, et al. Demographic and epidemiologic drivers of global cardiovascular mortality. The New England Journal of Medicine. 2015;372(14):1333-1341

[28] Wang W, Lau Y, Loo A, et al. Medication adherence and its associated factors among Chinese communitydwelling older adults with hypertension. Heart & Lung. 2014;43(4):278-283

[29] Fodor GJ, Kotrec M, Bacskai K, et al. Is interview a reliable method to verify the compliance with antihypertensive therapy? An international Central-European study. Journal of Hypertension. 2005;23:1261-1266

[30] Rao CR, Veena KG, Shetty A, et al. Treatment compliance among patients with hypertension and type 2 diabetes mellitus in a coastal population of Southern India. International Journal of Preventive Medicine. 2014;5(8):992-998

[31] Raymundo NAC, Pierin AMG. Adherence to anti-hypertensive treatment within a chronic disease management program: A longitudinal, retrospective study. Revista da Escola de Enfermagem da U.S.P. 2014;48(5): 809-881

**69**

expansion.

**Chapter 5**

**Abstract**

gestures.

**1. Introduction**

other respiratory diseases.

Resuscitation

*Jean-Marc Mercantini*

Ontology-Based Modeling for

Newborn Behavior Simulation

This chapter concerns the formulation of a methodology and its implementation to elaborate a training simulator for medical staff who may be confronted with the critical situations of newborn resuscitation. The simulator reproduces the different cardiopulmonary pathological behaviors of newborns, the working environment of resuscitation rooms, and the monitoring and control environment of the learners by a teacher. Conceptual models of newborn behaviors combined with the cardiopulmonary resuscitation gestures have been developed. The methodological process is jointly using cognitive approaches with formal modeling and simulation. Cognitive approaches are mobilized to elaborate application ontologies to be the bases for the development of the conceptual models and the specification of the simulator. Ontologies have been developed on the bases of a corpus of academic documents, return on experience documents, and practitioner interviews, by means of the knowledge-oriented design (KOD) method. A discrete event formalism has been used to formalize the conceptual models of the newborn behaviors. As a result, a simulator has been built to train medical practitioners to face situations, which are reported to potentially cause errors, and thus improve the safety of the resuscitation

**Keywords:** newborn resuscitation, clinician training, ontology engineering,

Approximately 15% of newborns require respiratory support at birth, and 2% require complex resuscitation (intubation, chest compression, and/or epinephrine) [1]. In France, 25% of the causes of neonatal mortality are due to respiratory difficulties: intrauterine hypoxia, asphyxia at birth, respiratory distress syndrome, or

Given these emergencies at birth, specialized technical equipment and skilled personnel are required to carry out all or part of the following procedures [1]: (i) the initial stage of stabilization (airway clearance, neonatal placement, and stimulation), (ii) ventilation, (iii) chest compressions, and (iv) medication or volume

conceptual modeling, discrete event modeling, simulation

during Cardiopulmonary

## **Chapter 5**

[30] Rao CR, Veena KG, Shetty A, et al. Treatment compliance among patients with hypertension and type 2 diabetes mellitus in a coastal population of Southern India. International Journal of Preventive Medicine. 2014;5(8):992-998

Cardiac Diseases and Interventions in 21st Century

[31] Raymundo NAC, Pierin AMG. Adherence to anti-hypertensive treatment within a chronic disease management program: A longitudinal, retrospective study. Revista da Escola de Enfermagem da U.S.P. 2014;48(5):

809-881

68
