**Part 1**

**Cardiac Arrhythmias** 

**1** 

*Greece* 

**The Prognostic Role of ECG** 

**in Arterial Hypertension** 

Stavros Dimopoulos1, Christos Manetos1,

*33rd Cardiology Department, "Laiko" Hospital,* 

*University of Athens, Athens,* 

Eleni Koroboki2, John Terrovitis3 and Serafim Nanas1 *11st Critical Care Medicine Department, "Evangelismos" Hospital, 2Hypertensive Center, Clinical Therapeutics, "Alexandra" Hospital,* 

Hypertension is very common and affects around 50 million Americans of which about 30% are not yet diagnosed. Hypertension is an under-diagnosed syndrome causing damage to the various target organs with no symptoms or only mild symptoms, also called a "silent killer" for this reason. Early diagnosis of arterial hypertension remains an important element in evaluating cardiovascular risk factors in general population. It is well established that isolated arterial hypertension increases the risk of left ventricular hypertrophy (LVH) and sudden death and prognostic indexes are necessary for risk stratification of these patients. The electrocardiogram (ECG) is a simple, non-invasive, low-cost method that can detect LVH, ventricular arrhythmias and ventricular repolarization abnormalities and confers

It is well known that left ventricular hypertrophy (LVH) is associated with an increased incidence of ventricular arrhythmia and sudden cardiac death. Systemic arterial hypertension is one of the most important causes of pathological LVH and there is evidence indicating that hypertensive patients with LVH are at increased risk of sudden cardiac death (Haider 1998, Korren 1991). These patients are characterized by a high incidence of ventricular arrhythmias (Gallinier 1997, Messerli 1999, Dimopoulos 2009) and this observation has led to the logical hypothesis that these arrhythmias may be the cause of sudden cardiac death in these patients. However, the exact mechanism by which LVH, ventricular arrhythmias and sudden cardiac death are linked to each other has not been fully clarified yet. Premature ventricular beats, multiform beats, couplets and non-sustained ventricular tachycardia are commonly found in ECGs of hypertensive patients, are associated with LVH, and have a negative prognostic value (Gallinier 1997, Messerli 1999). Furthermore, ventricular repolarization abnormalities in the 12-lead surface ECG such as QT-interval prolongation, increased QT dispersion (Dimopoulos 2008,2009), T wave axis

useful prognostic information for patients with arterial hypertension

**1. Introduction** 

**2. Prognostic data** 

### **The Prognostic Role of ECG in Arterial Hypertension**

Stavros Dimopoulos1, Christos Manetos1, Eleni Koroboki2, John Terrovitis3 and Serafim Nanas1 *11st Critical Care Medicine Department, "Evangelismos" Hospital, 2Hypertensive Center, Clinical Therapeutics, "Alexandra" Hospital, 33rd Cardiology Department, "Laiko" Hospital, University of Athens, Athens, Greece* 

### **1. Introduction**

Hypertension is very common and affects around 50 million Americans of which about 30% are not yet diagnosed. Hypertension is an under-diagnosed syndrome causing damage to the various target organs with no symptoms or only mild symptoms, also called a "silent killer" for this reason. Early diagnosis of arterial hypertension remains an important element in evaluating cardiovascular risk factors in general population. It is well established that isolated arterial hypertension increases the risk of left ventricular hypertrophy (LVH) and sudden death and prognostic indexes are necessary for risk stratification of these patients. The electrocardiogram (ECG) is a simple, non-invasive, low-cost method that can detect LVH, ventricular arrhythmias and ventricular repolarization abnormalities and confers useful prognostic information for patients with arterial hypertension

### **2. Prognostic data**

It is well known that left ventricular hypertrophy (LVH) is associated with an increased incidence of ventricular arrhythmia and sudden cardiac death. Systemic arterial hypertension is one of the most important causes of pathological LVH and there is evidence indicating that hypertensive patients with LVH are at increased risk of sudden cardiac death (Haider 1998, Korren 1991). These patients are characterized by a high incidence of ventricular arrhythmias (Gallinier 1997, Messerli 1999, Dimopoulos 2009) and this observation has led to the logical hypothesis that these arrhythmias may be the cause of sudden cardiac death in these patients. However, the exact mechanism by which LVH, ventricular arrhythmias and sudden cardiac death are linked to each other has not been fully clarified yet. Premature ventricular beats, multiform beats, couplets and non-sustained ventricular tachycardia are commonly found in ECGs of hypertensive patients, are associated with LVH, and have a negative prognostic value (Gallinier 1997, Messerli 1999). Furthermore, ventricular repolarization abnormalities in the 12-lead surface ECG such as QT-interval prolongation, increased QT dispersion (Dimopoulos 2008,2009), T wave axis

The Prognostic Role of ECG in Arterial Hypertension 5

2) any increase in voltage of the QRS complex (R or S in any limb lead ≥20mm or S waves

**1 point each** (duration of the QRS complex ≥0.09 seconds or intrinsicoid deflection ≥0.05

Cornell voltage criteria x QRS duration in ms ≥ 2440 (in women 6 mm is added to Cornell

Various epidemiological studies have previously demonstrated the importance of LVH for predicting cardiovascular events and sudden cardiac death. In the Framingham Heart Study the 3.4% of 5581 participants with ECG LVH and ST depression and T-wave flattening or inversion had a 3-fold increased risk for developing coronary heart disease after adjusting for age, gender, and blood pressure, (Kannel 1969, 1970). Similar results were found in the Copenhagen City Heart Study with enrollment of 11,634 participants with no evidence of ischemic heart disease at initial evaluation. In this study it was shown that ST depression and/or T-wave inversion (as defined by the Minnesota code) remained strongly predictive for cardiac events in multivariate analyses (Larsen 2002). The left ventricular (LV) strainpattern of ST segment depression and T-wave inversion on the left precordial leads of the standard resting ECG is now considered a valid marker of the presence of anatomic LV hypertrophy (LVH) (Salles 2006). The presence of typical ECG strain pattern is also independently associated with increased LV wall thickness and mass and independently associated with other adverse factors: increased 24-hour systolic blood pressure, prolonged maximum QTc-interval duration, higher serum creatinine and fasting glycemia, physical inactivity, as well as with the presence of two types of important target organ damage (CHD and peripheral arterial disease). In the Losartan Intervention For Endpoint reduction in hypertension (LIFE) study, it was demonstrated that therapy with losartan was more

S waves in V1 + R waves in V5 or V6 > 35mm

1) P wave from left atrial abnormality

**2 points** (left axis deviation ≥-30o)

Table 5. Romhilt and Estes score

Table 6. Cornell voltage criteria

Total QRS voltage from all 12 leads ≥175mm

Table 7. Cornell product

Table 8. Total QRS voltage

in V1 or V2 ≥ 30mm or R waves in V5 or V6 ≥ 30mm)

seconds in V5 or V6 or ST-T abnormalities with digitalis)

Score of ≥ 5 points predicts LVH, score of 4 points = probable LVH

3) ST-T abnormalities any shift in the ST segment but without digitalis

R waves in aVL+ S waves in V3 > 28mm (men) and 20 mm (women)

R waves in aVL > 11mm Table 4. Sokolow-Lyon Index

**3 points each** 

voltage)

(Salles 2008) and T wave alternans (Hennersdorf 2001) have all been associated with ventricular arrhythmias and sudden cardiac death in arterial hypertension; however further research is required to establish their prognostic significance.

### **2.1 LVH**

In arterial hypertension, LVH is a physiologic and expected response to pressure or volume overload and it is a well known marker of increased cardiovascular morbidity. Previous studies have extensively shown that antihypertensive agents can partially reverse LVH and their efficacy is well documented (Haider 1998, Kannel 1969,1970, Sokolow & Lyon 1949, Deverreux & Reichek 1982, Okin 2004,2009). Development of the typical strain pattern in response to LVH may reflect true subendocardial ischemia in the absence of coronary artery disease, since the increases in coronary artery blood flow (through coronary artery dilatation and capillary recruitment) are inadequate to compensate the demand posed by increased LV mass and wall thickness, in the setting of LVH. The increased mass of the left ventricle (representing both myocardial interstitial fibrosis and/or myocyte hypertrophy) can cause abnormalities of the QRS complex and QT-interval. Although the ECG has a low sensitivity (less than 50%) for detecting LVH, when LVH is identified by ECG, the specificity is higher than 90%. From these observations several criteria have been proposed so far, however none is considered to have optimal accuracy (Sokolow & Lyon 1949, Hancock 2009). A variety of ECG abnormalities have been described, as shown in Tables 1,2,3:


Table 1. Abnormalities in the QRS complex

### Terminal negativity of the P wave in V1 1mm X 1mm

Table 2. Left atrial abnormality

ST depression and T inversion in leads with tall R waves (left ventricular strain)

Table 3. Abnormalities in the ST segment and T wave

The following are the most frequently used criteria for the diagnosis of LVH, including the Sokolow-Lyon Index, Romhilt and Estes score, Cornell voltage criteria, Cornell product, total QRS voltage (Table 4,5,6,7,8):

S waves in V1 + R waves in V5 or V6 > 35mm

R waves in aVL > 11mm

Table 4. Sokolow-Lyon Index

Score of ≥ 5 points predicts LVH, score of 4 points = probable LVH

### **3 points each**

4 Advances in Electrocardiograms – Clinical Applications

(Salles 2008) and T wave alternans (Hennersdorf 2001) have all been associated with ventricular arrhythmias and sudden cardiac death in arterial hypertension; however further

In arterial hypertension, LVH is a physiologic and expected response to pressure or volume overload and it is a well known marker of increased cardiovascular morbidity. Previous studies have extensively shown that antihypertensive agents can partially reverse LVH and their efficacy is well documented (Haider 1998, Kannel 1969,1970, Sokolow & Lyon 1949, Deverreux & Reichek 1982, Okin 2004,2009). Development of the typical strain pattern in response to LVH may reflect true subendocardial ischemia in the absence of coronary artery disease, since the increases in coronary artery blood flow (through coronary artery dilatation and capillary recruitment) are inadequate to compensate the demand posed by increased LV mass and wall thickness, in the setting of LVH. The increased mass of the left ventricle (representing both myocardial interstitial fibrosis and/or myocyte hypertrophy) can cause abnormalities of the QRS complex and QT-interval. Although the ECG has a low sensitivity (less than 50%) for detecting LVH, when LVH is identified by ECG, the specificity is higher than 90%. From these observations several criteria have been proposed so far, however none is considered to have optimal accuracy (Sokolow & Lyon 1949, Hancock 2009). A variety of

research is required to establish their prognostic significance.

ECG abnormalities have been described, as shown in Tables 1,2,3:

R waves in aVL+ S waves in V3 > 28mm (men) and 20 mm (women)

ST depression and T inversion in leads with tall R waves (left ventricular strain)

The following are the most frequently used criteria for the diagnosis of LVH, including the Sokolow-Lyon Index, Romhilt and Estes score, Cornell voltage criteria, Cornell product,

Onset of intrinsicoid deflection >0.05 seconds in V5 or V6 Increased duration of the QRS complex ≥0.09 seconds

Terminal negativity of the P wave in V1 1mm X 1mm

Table 3. Abnormalities in the ST segment and T wave

R or S in any limb lead ≥20mm R waves in aVL > 11mm R in lead I + S in III > 25mm S waves in V1 or V2 ≥ 30mm R waves in V5 or V6 ≥ 30mm

Left axis deviation ≥-30o

Table 2. Left atrial abnormality

total QRS voltage (Table 4,5,6,7,8):

S waves in V1 + R waves in V5 or V6 > 35mm

Total QRS voltage from all 12 leads ≥175mm

Table 1. Abnormalities in the QRS complex

**2.1 LVH** 

1) P wave from left atrial abnormality

2) any increase in voltage of the QRS complex (R or S in any limb lead ≥20mm or S waves in V1 or V2 ≥ 30mm or R waves in V5 or V6 ≥ 30mm)

3) ST-T abnormalities any shift in the ST segment but without digitalis

**2 points** (left axis deviation ≥-30o)

**1 point each** (duration of the QRS complex ≥0.09 seconds or intrinsicoid deflection ≥0.05 seconds in V5 or V6 or ST-T abnormalities with digitalis)

Table 5. Romhilt and Estes score

R waves in aVL+ S waves in V3 > 28mm (men) and 20 mm (women)

Table 6. Cornell voltage criteria

Cornell voltage criteria x QRS duration in ms ≥ 2440 (in women 6 mm is added to Cornell voltage)

Table 7. Cornell product

### Total QRS voltage from all 12 leads ≥175mm

Table 8. Total QRS voltage

Various epidemiological studies have previously demonstrated the importance of LVH for predicting cardiovascular events and sudden cardiac death. In the Framingham Heart Study the 3.4% of 5581 participants with ECG LVH and ST depression and T-wave flattening or inversion had a 3-fold increased risk for developing coronary heart disease after adjusting for age, gender, and blood pressure, (Kannel 1969, 1970). Similar results were found in the Copenhagen City Heart Study with enrollment of 11,634 participants with no evidence of ischemic heart disease at initial evaluation. In this study it was shown that ST depression and/or T-wave inversion (as defined by the Minnesota code) remained strongly predictive for cardiac events in multivariate analyses (Larsen 2002). The left ventricular (LV) strainpattern of ST segment depression and T-wave inversion on the left precordial leads of the standard resting ECG is now considered a valid marker of the presence of anatomic LV hypertrophy (LVH) (Salles 2006). The presence of typical ECG strain pattern is also independently associated with increased LV wall thickness and mass and independently associated with other adverse factors: increased 24-hour systolic blood pressure, prolonged maximum QTc-interval duration, higher serum creatinine and fasting glycemia, physical inactivity, as well as with the presence of two types of important target organ damage (CHD and peripheral arterial disease). In the Losartan Intervention For Endpoint reduction in hypertension (LIFE) study, it was demonstrated that therapy with losartan was more

The Prognostic Role of ECG in Arterial Hypertension 7

increases (Uen 2006, Sigurdson 1996, Szlachcic 1992). It is also well known that there is a circadian rhythm of transient ST depressions with a distinct peak in the early hours of the morning, similar to the peak period of myocardial infarction or sudden cardiac death (Asmar 1996). The ST depression is significantly more frequent in patients with known coronary heart disease, positive Sokolow index, as well as in patients who complain of dyspnea, have a smoking history or are on diuretics. The ST-segment depression is also observed more frequently in men than in women. The prevalence of ST-segment depression in patients with coronary heart disease is influenced by the severity of coronary heart disease (Uen 2006, Sigurdson 1996, Szlachcic 1992). This phenomenon is characterized by a significant rise of blood pressure, heart rate and double product before the appearance of ST depression. These parameters reach their peak during the ST depression. After ST depression resolution, they return to lower values. Apart from the systolic blood pressure, however, the other two parameters will remain significantly elevated compared with the mean 24-hour values. Other clinical predictors for the occurrence of cardiac ischemia during daily life are risk factors for silent ST-depression, indicating the presence of ischemic heart disease. A study showed that 24-hours ambulatory blood pressure monitoring (ABPM) has high predictive value, in contrast to office-based blood pressure measurements, for silent ST-segment depression. Interestingly, the office-based measurements, apart from the pulse pressure, are not predictive of the occurrence of ST-segment depression either in treated or in untreated hypertensive and normotensive hypertensive individuals. To what extent the 24-hours mean values of the ST segment depression analysis reflect the severity of ischemic heart disease or have prognostic significance regarding the development of an ischemic event remains so far unknown, although a study has previously reported that silent ischemia may be predictive of adverse outcome in arterial hypertension (Schillaci 2004).

Increased left ventricular mass, as mentioned before has a high prevalence in patients with arterial hypertension, particularly in the elderly and has been associated with increased cardiovascular risk, including sudden death, (Haider 1998, Koren 1991, Dimopoulos 2009, Kannel 1969,1970, Bednar 2001, Okin 2000). The increased electrical instability due to nonuniform left ventricular mass distribution has been suggested as a possible mechanism linking LVH and cardiovascular death. QT interval prolongation has been implicated in the origin of ventricular arrhythmias, possibly because of less uniform recovery of ventricular excitability in the setting of regional differences in cardiac sympathetic nervous system activity. In addition, the increased inhomogeneity of ventricular repolarization, induced by LVH, can be indirectly detected by QT dispersion, a relatively simple measurement of 12 lead electrocardiogram (ECG) variability, and this index has been recently shown to be related to poor prognosis in large population studies (Okin 2000, Salles 2005, Elming 1998, Bruyne 1998, Sheehana 2004). Interestingly, heterogeneous ventricular repolarization was initially recognized in standard ECGs as early as 1934; however only recently QTc interval dispersion was identified as a marker of arrhythmia risk and sudden cardiac death in patients after myocardial infarction or with heart failure (Barr 1994, Glancy 1995, Anastasiou-Nana 2000). Furthermore, other studies subsequently demonstrated the presence of increased QT-dispersion in patients with systemic arterial hypertension, associated with ventricular arrhythmias and LVH (Dimopoulos 2008, Clarkson 1995, Mayet 1996). QTc dispersion (QT-dispersion corrected for HR) was found to be an independent predictor of echocardiographically determined LVMI in normotensive and hypertensive elderly patients (Dimopoulos 2008). Patients with arterial hypertension had an increased

**2.4 QT-interval and QT-dispersion** 

effective than atenolol in preventing CV morbidity and mortality with nearly identical reductions in systolic and diastolic pressure in both treatment arms. The presence of typical strain on the ECG in hypertensive patients with ECG LVH also identified patients at higher risk of CV morbidity and mortality in the setting of antihypertensive therapy associated with large decreases in systolic and diastolic pressure. The increased CV risk associated with ECG strain was independent of the improved prognosis with losartan therapy in the LIFE trial and persisted after adjusting for the greater baseline severity and prevalence of ECG LVH and the higher prevalence of other CV disorders in patients with strain pattern. The development of new ECG strain between baseline and year 1 during the LIFE study identified patients at increased risk of cardiovascular morbidity and mortality and all-cause mortality in the setting of antihypertensive therapy associated with substantial decreases in both systolic and diastolic pressure. The increased risk associated with new ECG strain was independent of the improved prognosis with losartan therapy and with regression of ECG LVH in LIFE (Okin 2004,2009). These findings suggest that more aggressive therapy may be warranted in hypertensive patients who develop new ECG strain to reduce the risk of cardiovascular morbidity, cardiovascular and all-cause mortality, and sudden death. The relationship of cardiovascular risk to ECG strain pattern on a single ECG has been demonstrated in population-based studies and in patients with hypertension.

### **2.2 Ventricular arrhythmias, Lown score**

Previous studies have shown an increased incidence of number and severity of ventricular arrhythmias in patients with hypertensive LVH (Galinier 1997, Messerli 1999, Dimopoulos 2008, Hennersdorf 2001). The average number of premature ventricular beats (PVB), and their severity according to a modified Lown's score (a: absence of PVB; b: PVB < 30/h; c: PVB ≥ 30/h; d: multiform; e: couplets; f: nonsustained ventricular tachycardia; g: R on T = RR / QT ≤ 0.75) during a 24-hours ECG Holter are independent predictors of echocardiographically determined LVMI in normotensive and hypertensive elderly patients, as demonstrated in a previous study (Dimopoulos 2008). In that study, patients with arterial hypertension had a higher Lown's score compared with normotensive subjects, and this increase was found to be higher in those hypertensives with LVH. The elderly population group had a greater number of PVB in contrast to younger subjects. Elderly patients were also characterized by a higher Lown's score and a greater LVMI. Another study has previously shown that patients with LVH present frequent and complex ventricular premature beats and that the presence of non-sustained ventricular tachycardia has negative prognostic value in this population (Galinier 1997). However, the role of ventricular arrhythmias in arterial hypertension is still under investigation, with no clear evidence for high prognostic significance.

#### **2.3 Silent ischemia**

Transient changes of ST-segment (ST depression) is a phenomenon commonly observed in patients with arterial hypertension; although reported prevalence varied widely (from 15 to 80%-Stramba 1998, Asmar 1996), it seems that the true value is around 20% (Uen 2006). These findings on ECG are interpreted as silent ischemia, due to the absence of angina pectoris or other angina equivalent symptoms and are attributed to transient mismatch of myocardial blood flow to the myocardial demands. It seems that more than 90% of these episodes, identified in the Holter electrocardiogram (ECG) of hypertensive patients, are silent. When transient ST depressions are detected, the risk of cardiovascular events

effective than atenolol in preventing CV morbidity and mortality with nearly identical reductions in systolic and diastolic pressure in both treatment arms. The presence of typical strain on the ECG in hypertensive patients with ECG LVH also identified patients at higher risk of CV morbidity and mortality in the setting of antihypertensive therapy associated with large decreases in systolic and diastolic pressure. The increased CV risk associated with ECG strain was independent of the improved prognosis with losartan therapy in the LIFE trial and persisted after adjusting for the greater baseline severity and prevalence of ECG LVH and the higher prevalence of other CV disorders in patients with strain pattern. The development of new ECG strain between baseline and year 1 during the LIFE study identified patients at increased risk of cardiovascular morbidity and mortality and all-cause mortality in the setting of antihypertensive therapy associated with substantial decreases in both systolic and diastolic pressure. The increased risk associated with new ECG strain was independent of the improved prognosis with losartan therapy and with regression of ECG LVH in LIFE (Okin 2004,2009). These findings suggest that more aggressive therapy may be warranted in hypertensive patients who develop new ECG strain to reduce the risk of cardiovascular morbidity, cardiovascular and all-cause mortality, and sudden death. The relationship of cardiovascular risk to ECG strain pattern on a single ECG has been

demonstrated in population-based studies and in patients with hypertension.

Previous studies have shown an increased incidence of number and severity of ventricular arrhythmias in patients with hypertensive LVH (Galinier 1997, Messerli 1999, Dimopoulos 2008, Hennersdorf 2001). The average number of premature ventricular beats (PVB), and their severity according to a modified Lown's score (a: absence of PVB; b: PVB < 30/h; c: PVB ≥ 30/h; d: multiform; e: couplets; f: nonsustained ventricular tachycardia; g: R on T = RR / QT ≤ 0.75) during a 24-hours ECG Holter are independent predictors of echocardiographically determined LVMI in normotensive and hypertensive elderly patients, as demonstrated in a previous study (Dimopoulos 2008). In that study, patients with arterial hypertension had a higher Lown's score compared with normotensive subjects, and this increase was found to be higher in those hypertensives with LVH. The elderly population group had a greater number of PVB in contrast to younger subjects. Elderly patients were also characterized by a higher Lown's score and a greater LVMI. Another study has previously shown that patients with LVH present frequent and complex ventricular premature beats and that the presence of non-sustained ventricular tachycardia has negative prognostic value in this population (Galinier 1997). However, the role of ventricular arrhythmias in arterial hypertension is still under investigation, with no clear evidence for

Transient changes of ST-segment (ST depression) is a phenomenon commonly observed in patients with arterial hypertension; although reported prevalence varied widely (from 15 to 80%-Stramba 1998, Asmar 1996), it seems that the true value is around 20% (Uen 2006). These findings on ECG are interpreted as silent ischemia, due to the absence of angina pectoris or other angina equivalent symptoms and are attributed to transient mismatch of myocardial blood flow to the myocardial demands. It seems that more than 90% of these episodes, identified in the Holter electrocardiogram (ECG) of hypertensive patients, are silent. When transient ST depressions are detected, the risk of cardiovascular events

**2.2 Ventricular arrhythmias, Lown score** 

high prognostic significance.

**2.3 Silent ischemia** 

increases (Uen 2006, Sigurdson 1996, Szlachcic 1992). It is also well known that there is a circadian rhythm of transient ST depressions with a distinct peak in the early hours of the morning, similar to the peak period of myocardial infarction or sudden cardiac death (Asmar 1996). The ST depression is significantly more frequent in patients with known coronary heart disease, positive Sokolow index, as well as in patients who complain of dyspnea, have a smoking history or are on diuretics. The ST-segment depression is also observed more frequently in men than in women. The prevalence of ST-segment depression in patients with coronary heart disease is influenced by the severity of coronary heart disease (Uen 2006, Sigurdson 1996, Szlachcic 1992). This phenomenon is characterized by a significant rise of blood pressure, heart rate and double product before the appearance of ST depression. These parameters reach their peak during the ST depression. After ST depression resolution, they return to lower values. Apart from the systolic blood pressure, however, the other two parameters will remain significantly elevated compared with the mean 24-hour values. Other clinical predictors for the occurrence of cardiac ischemia during daily life are risk factors for silent ST-depression, indicating the presence of ischemic heart disease. A study showed that 24-hours ambulatory blood pressure monitoring (ABPM) has high predictive value, in contrast to office-based blood pressure measurements, for silent ST-segment depression. Interestingly, the office-based measurements, apart from the pulse pressure, are not predictive of the occurrence of ST-segment depression either in treated or in untreated hypertensive and normotensive hypertensive individuals. To what extent the 24-hours mean values of the ST segment depression analysis reflect the severity of ischemic heart disease or have prognostic significance regarding the development of an ischemic event remains so far unknown, although a study has previously reported that silent ischemia may be predictive of adverse outcome in arterial hypertension (Schillaci 2004).

### **2.4 QT-interval and QT-dispersion**

Increased left ventricular mass, as mentioned before has a high prevalence in patients with arterial hypertension, particularly in the elderly and has been associated with increased cardiovascular risk, including sudden death, (Haider 1998, Koren 1991, Dimopoulos 2009, Kannel 1969,1970, Bednar 2001, Okin 2000). The increased electrical instability due to nonuniform left ventricular mass distribution has been suggested as a possible mechanism linking LVH and cardiovascular death. QT interval prolongation has been implicated in the origin of ventricular arrhythmias, possibly because of less uniform recovery of ventricular excitability in the setting of regional differences in cardiac sympathetic nervous system activity. In addition, the increased inhomogeneity of ventricular repolarization, induced by LVH, can be indirectly detected by QT dispersion, a relatively simple measurement of 12 lead electrocardiogram (ECG) variability, and this index has been recently shown to be related to poor prognosis in large population studies (Okin 2000, Salles 2005, Elming 1998, Bruyne 1998, Sheehana 2004). Interestingly, heterogeneous ventricular repolarization was initially recognized in standard ECGs as early as 1934; however only recently QTc interval dispersion was identified as a marker of arrhythmia risk and sudden cardiac death in patients after myocardial infarction or with heart failure (Barr 1994, Glancy 1995, Anastasiou-Nana 2000). Furthermore, other studies subsequently demonstrated the presence of increased QT-dispersion in patients with systemic arterial hypertension, associated with ventricular arrhythmias and LVH (Dimopoulos 2008, Clarkson 1995, Mayet 1996). QTc dispersion (QT-dispersion corrected for HR) was found to be an independent predictor of echocardiographically determined LVMI in normotensive and hypertensive elderly patients (Dimopoulos 2008). Patients with arterial hypertension had an increased

The Prognostic Role of ECG in Arterial Hypertension 9

interval dispersion has obvious predictive value, but inter- and intraobserver varialbility limits the wider clinical use. In perspective, it is important to decrease measurement errors by improving measurement technique, define precisely normal values and demonstrate

Two electrocardiographic markers of ventricular repolarization abnormalities have been recently proposed: spatial T-wave axis deviation and T (peak)-T (end)-interval duration. In a cross-sectional study (Salles 2008), 810 treatment-resistant hypertensive patients were evaluated. Maximum T(peak)-T(end)-interval duration (Tpe(max)) was considered prolonged if it was beyond the upper quartile value (120 ms), and the spatial T-wave axis on the frontal plane was considered abnormally deviated if >105 degrees or < 15 degrees. Tpe(max)-interval prolongation, as well as QTc-interval prolongation, was found to be associated with body mass index, 24-h systolic blood pressure (SBP), indexed LVM, serum potassium, and heart rate. Abnormal T-axis deviation was associated with male gender, presence of coronary heart disease, serum creatinine, 24-h SBP, LVM, and serum potassium. All three repolarization parameters (T-wave axis deviation, T (peak)-T (end)-interval and QTc-interval) were shown to be associated with increased LVM, after adjustment for possible confounders. However, when included together into the same model, only abnormal T-axis and QTc-interval prolongation remained independently associated with LVM. All three parameters were also increased in patients with concentric hypertrophy. It seems that only abnormal T-wave axis deviation appears to have distinct and additive prognostic value compared with the more classic marker, the QTc interval. More investigational studies are needed to evaluate the prognostic value of T-wave axis

predictive value in studies with large number of patients with arterial hypertension.

abnormalities in arterial hypertension, prior to clinical application of this index.

An association between the occurrence of TWA and inducibility of tachyarrhythmias has been reported in a previous electrophysiological study (Rosenbaum 1994). Another significant study (Hennersdorf 2001) has evaluated the significance of TWA in 51 patients with arterial hypertension. The patient population was divided into a group of 11 consecutive patients who had survived arrhythmic events or cardiogenic syncope and a group of 40 consecutive patients without documented ventricular arrhythmias. Patients exercised with a gradual increase of workload to maintain a heart rate of at least 105/min. Workload was increased in a stepwise fashion to avoid a sudden increase of the heart rate, which could provoke a false positive test result. After recording 254 consecutive heartbeats, ECG signals were digitally processed by a spectral analysis method and analyzed. The beat domain power spectrum of the T wave (J point 160 ms through end of the T wave) was calculated every 16 beats from sequential overlapping 128-beat sequences. The magnitude of the TWA was measured at a frequency of 0.5 cycles per beat. The results of this study showed that the prevalence of TWA was higher in patients with LVH. There was also a significant correlation between patients with a positive TWA and a survived arrhythmic event. None of the patients without documented ventricular arrhythmias had a positive alternans tracing. The underlying mechanism of a positive TWA is not clear yet; it seems that there is an alteration in action potential morphology or dispersion of repolarization. The changes in morphology of the action potentials might lead to spatial inhomogeneity in refractoriness and may result in increased vulnerability to ventricular fibrillation. During

**2.5 T wave axis** 

**2.6 T wave alternans (TWA)** 

QTc dispersion compared with normotensive subjects, and this increase was found to be higher in those hypertensive patients with LVH. In a prospective study, patients with QTcD ≥45 ms had a higher rate of major cardiovascular events, higher LVMI, increased values of systolic and diastolic blood pressure, higher number of PVB and higher Lown's score than patients with QTcD <45 ms (Dimopoulos 2009) . After adjustment for multiple known predictors of adverse outcome, QT interval corrected for heart rate remained associated with both all-cause and cardiovascular mortality. Increased QTc interval dispersion was also a significant predictor of cardiovascular mortality in LIFE trial (Saaden & Jones 2001). This additive risk prediction suggests that increased QTc and QTc interval dispersion on the surface ECG reflect different aspects of abnormal ventricular repolarization. However only few studies have investigated the prognostic role of QT-dispersion and QT-interval in arterial hypertension (Galinier 1997, Dimopoulos 2009, Oikarinen 2004, Saaden & Jones 2001). Antihypertensive therapy with angiotensin-converting enzyme inhibitors, calcium antagonists and beta-blockers has been shown beneficial in terms of QT-dispersion normalization and/or LVH decrease (Tomiyama 1998, Karpanou 1998, Galetta 2005,Lim 1999). Beta blockers are associated with a reduction in both QT and QTc interval dispersion, raising the possibility that a reduction in dispersion of ventricular repolarization may be an important antiarrhythmic mechanism of beta blockade.

The direct linking mechanism between QT-dispersion, ventricular arrhythmias, LVH and adverse outcome has not been fully clarified yet. Increased QTc dispersion has been associated with increased regional heterogeneity of ventricular repolarization and it has been considered as a possible noninvasive surrogate marker of susceptibility to malignant ventricular arrhythmias and cardiovascular mortality in large population studies and, in cardiac patients (Okin 2000, Elming 1998, Bruyne 1998, Sheehana 2004, Barr 1994, Glancy 1995, Anastasiou 2000). The degree of myocardial interstitial fibrosis induced by either systemic arterial hypertension and/or by ageing, as well as the inhomogeneous myocyte hypertrophy caused mainly by arterial hypertension, might play an important role in increasing action potential duration and amplitude in different myocardial regions (Dimopoulos 2008,2009). These myocardial electrical abnormalities may affect ventricular repolarization and might be the main cause of increased QTc dispersion in hypertensives. In order to maximize reproducibility of QT-dispersion measurements, it is important to follow certain methodological rules that are reported below.

All 12-leads ECGs are obtained at a speed of 25 mm/sec. The ECG registration should be a simultaneous 12-lead ECG. QT intervals are measured manually on all possible leads. QT interval is defined as the interval from the onset of the QRS complex to the end of the T wave, which is defined as its return to the T-P baseline. If U wave is present, the QT interval is measured to the nadir of the curve between the T and U waves. QT intervals are then corrected with the Bazett's formula to compensate for its known dependence on heart rate: QTc = QT / √RR. Measurements on QT and RR intervals should be carried out in 3 consecutive cardiac cycles in all leads, and average values are then obtained. QTc dispersion is determined as the difference between the maximal and minimal QTc interval in different leads. No subject should have fewer than 9 measurable ECG leads. Various studies have also demonstrated the predictive value of the QT interval and QTc interval dispersion measured automatically by computerized ECG for noninvasive risk stratification in a population-based sample.

It should be mentioned that there are some methodological difficulties related to QT dispersion measurements, including the circadian variation of the QT interval. The QTc interval dispersion has obvious predictive value, but inter- and intraobserver varialbility limits the wider clinical use. In perspective, it is important to decrease measurement errors by improving measurement technique, define precisely normal values and demonstrate predictive value in studies with large number of patients with arterial hypertension.

### **2.5 T wave axis**

8 Advances in Electrocardiograms – Clinical Applications

QTc dispersion compared with normotensive subjects, and this increase was found to be higher in those hypertensive patients with LVH. In a prospective study, patients with QTcD ≥45 ms had a higher rate of major cardiovascular events, higher LVMI, increased values of systolic and diastolic blood pressure, higher number of PVB and higher Lown's score than patients with QTcD <45 ms (Dimopoulos 2009) . After adjustment for multiple known predictors of adverse outcome, QT interval corrected for heart rate remained associated with both all-cause and cardiovascular mortality. Increased QTc interval dispersion was also a significant predictor of cardiovascular mortality in LIFE trial (Saaden & Jones 2001). This additive risk prediction suggests that increased QTc and QTc interval dispersion on the surface ECG reflect different aspects of abnormal ventricular repolarization. However only few studies have investigated the prognostic role of QT-dispersion and QT-interval in arterial hypertension (Galinier 1997, Dimopoulos 2009, Oikarinen 2004, Saaden & Jones 2001). Antihypertensive therapy with angiotensin-converting enzyme inhibitors, calcium antagonists and beta-blockers has been shown beneficial in terms of QT-dispersion normalization and/or LVH decrease (Tomiyama 1998, Karpanou 1998, Galetta 2005,Lim 1999). Beta blockers are associated with a reduction in both QT and QTc interval dispersion, raising the possibility that a reduction in dispersion of ventricular repolarization may be an

The direct linking mechanism between QT-dispersion, ventricular arrhythmias, LVH and adverse outcome has not been fully clarified yet. Increased QTc dispersion has been associated with increased regional heterogeneity of ventricular repolarization and it has been considered as a possible noninvasive surrogate marker of susceptibility to malignant ventricular arrhythmias and cardiovascular mortality in large population studies and, in cardiac patients (Okin 2000, Elming 1998, Bruyne 1998, Sheehana 2004, Barr 1994, Glancy 1995, Anastasiou 2000). The degree of myocardial interstitial fibrosis induced by either systemic arterial hypertension and/or by ageing, as well as the inhomogeneous myocyte hypertrophy caused mainly by arterial hypertension, might play an important role in increasing action potential duration and amplitude in different myocardial regions (Dimopoulos 2008,2009). These myocardial electrical abnormalities may affect ventricular repolarization and might be the main cause of increased QTc dispersion in hypertensives. In order to maximize reproducibility of QT-dispersion measurements, it is important to follow

All 12-leads ECGs are obtained at a speed of 25 mm/sec. The ECG registration should be a simultaneous 12-lead ECG. QT intervals are measured manually on all possible leads. QT interval is defined as the interval from the onset of the QRS complex to the end of the T wave, which is defined as its return to the T-P baseline. If U wave is present, the QT interval is measured to the nadir of the curve between the T and U waves. QT intervals are then corrected with the Bazett's formula to compensate for its known dependence on heart rate: QTc = QT / √RR. Measurements on QT and RR intervals should be carried out in 3 consecutive cardiac cycles in all leads, and average values are then obtained. QTc dispersion is determined as the difference between the maximal and minimal QTc interval in different leads. No subject should have fewer than 9 measurable ECG leads. Various studies have also demonstrated the predictive value of the QT interval and QTc interval dispersion measured automatically by computerized ECG for noninvasive risk stratification in a

It should be mentioned that there are some methodological difficulties related to QT dispersion measurements, including the circadian variation of the QT interval. The QTc

important antiarrhythmic mechanism of beta blockade.

certain methodological rules that are reported below.

population-based sample.

Two electrocardiographic markers of ventricular repolarization abnormalities have been recently proposed: spatial T-wave axis deviation and T (peak)-T (end)-interval duration. In a cross-sectional study (Salles 2008), 810 treatment-resistant hypertensive patients were evaluated. Maximum T(peak)-T(end)-interval duration (Tpe(max)) was considered prolonged if it was beyond the upper quartile value (120 ms), and the spatial T-wave axis on the frontal plane was considered abnormally deviated if >105 degrees or < 15 degrees. Tpe(max)-interval prolongation, as well as QTc-interval prolongation, was found to be associated with body mass index, 24-h systolic blood pressure (SBP), indexed LVM, serum potassium, and heart rate. Abnormal T-axis deviation was associated with male gender, presence of coronary heart disease, serum creatinine, 24-h SBP, LVM, and serum potassium. All three repolarization parameters (T-wave axis deviation, T (peak)-T (end)-interval and QTc-interval) were shown to be associated with increased LVM, after adjustment for possible confounders. However, when included together into the same model, only abnormal T-axis and QTc-interval prolongation remained independently associated with LVM. All three parameters were also increased in patients with concentric hypertrophy. It seems that only abnormal T-wave axis deviation appears to have distinct and additive prognostic value compared with the more classic marker, the QTc interval. More investigational studies are needed to evaluate the prognostic value of T-wave axis abnormalities in arterial hypertension, prior to clinical application of this index.

### **2.6 T wave alternans (TWA)**

An association between the occurrence of TWA and inducibility of tachyarrhythmias has been reported in a previous electrophysiological study (Rosenbaum 1994). Another significant study (Hennersdorf 2001) has evaluated the significance of TWA in 51 patients with arterial hypertension. The patient population was divided into a group of 11 consecutive patients who had survived arrhythmic events or cardiogenic syncope and a group of 40 consecutive patients without documented ventricular arrhythmias. Patients exercised with a gradual increase of workload to maintain a heart rate of at least 105/min. Workload was increased in a stepwise fashion to avoid a sudden increase of the heart rate, which could provoke a false positive test result. After recording 254 consecutive heartbeats, ECG signals were digitally processed by a spectral analysis method and analyzed. The beat domain power spectrum of the T wave (J point 160 ms through end of the T wave) was calculated every 16 beats from sequential overlapping 128-beat sequences. The magnitude of the TWA was measured at a frequency of 0.5 cycles per beat. The results of this study showed that the prevalence of TWA was higher in patients with LVH. There was also a significant correlation between patients with a positive TWA and a survived arrhythmic event. None of the patients without documented ventricular arrhythmias had a positive alternans tracing. The underlying mechanism of a positive TWA is not clear yet; it seems that there is an alteration in action potential morphology or dispersion of repolarization. The changes in morphology of the action potentials might lead to spatial inhomogeneity in refractoriness and may result in increased vulnerability to ventricular fibrillation. During

The Prognostic Role of ECG in Arterial Hypertension 11

Devereux & Reichek, 1982, Repolarization abnormalities of left ventricular hypertrophy.

Dimopoulos et al., 2009, Prognostic Evaluation of QT-Dispersion in Elderly Hypertensive

Dimopoulos et al., 2008, QT Dispersion and Left Ventricular Hypertrophy in Elderly

Elming et al., 1998, The prognostic value of the QT interval and QT dispersion in all-cause

Galetta et al., 2005, Effect of nebivolol on QT dispersion in hypertensive patients with left

Galinier et al., 1997, Prognostic value of ventricular arrhythmias in systemic Hypertension. *Journal of Hypertension*, Vol.15, No.12, (December 1997), pp. 1779-1783 Glancy et al., 1995, QT dispersion and mortality after myocardial infarction. *Lancet*, Vol. 345,

Haider et al., 1998, Increased left ventricular mass and hypertrophy are associated with

Hancock et al., 2009 AHA/ACCF/HRS Recommendations for the Standardization and

Hennersdorf et al., 2001, T Wave Alternans and Ventricular Arrhythmias in Arterial

Kannel et al., 1969, Left ventricular hypertrophy by electrocardiogram: prevalence,

Kannel et al.,1970, Electrocardiographic left ventricular hypertrophy and risk of coronary

Karpanou et al., 1998, Regression of left ventricular hypertrophy results in improvement of

Koren et al., 1991, Relation of left ventricular mass and geometry to morbidity and mortality

Larsen et al., 2002, Prevalence and prognosis of electrocardiographic left ventricular

Lim et al., 1999, Reduces QT Dispersion in Hypertensive Individuals. *Hypertension,* Vol. 33,

Mayet et al., 1996, Left ventricular hypertrophy and QT dispersion in hypertension.

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Clinical, echocardiographic and hemodynamic correlates. *Journal of* 

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ventricular hypertrophy. *Biomed Pharmacother,* Vol. 59, No. 1-2, (January-February

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incidence, and mortality in the Framingham Study, *Annals of Internal Medicine*,

heart disease: the Framingham Study. *Annals of Internal Medicine*, Vol.72, No.6,

QT dispersion in patients with hypertension. *Am Heart* J, Vol. 136, No. 5,

in uncomplicated essential hypertension. *Annals of Internal Medicine*, Vol.114*,* No. 5,

hypertrophy, ST segment depression and negative T-wave: The Copenhagen City

repeated episodes of ischemia, action potential alternans was detectable in 95% of the cases with ventricular fibrillation. The dispersion of repolarization is closely related to the temporo-spatial pattern of depolarization-repolarization, which can alternate on a beat-tobeat basis. The temporo-spatial dispersion of cellular refractoriness seems to predispose the myocardium to wave front fractionation and subsequent reentry. It is noteworthy that the TWA can be influenced by various conditions such as ischemia, hypothermia, heart rate, and sympathetic tone. Drugs such as procainamide and amiodarone reduce magnitude of alternans and sotalol can lead to the conversion of TWA from negative to positive. In the case of cardiomyopathies, the development of small areas of scars and ischemia is considered to be of pathological relevance for the development of alterations in action potentials or dispersion of repolarization. Whether T wave alternans can be used as a clinical marker of susceptibility for sudden cardiac death and cardiovascular events is under research.

### **3. Conclusions**

The electrocardiographic findings in patients with arterial hypertension are valuable tools for risk stratification of these patients, by predicting cardiovascular events and sudden cardiac death. The ECG strain pattern, an old parameter of well established value, should always be searched as it provides additional prognostic information beyond the one derived from echocardiographic LVH and LVM. Also among hypertensive patients with left ventricular hypertrophy, the presence of non-sustained ventricular tachycardia on ECG Holter monitoring identifies patients with a high risk of mortality, who need more aggressive care. Silent ischemia on ECG Holter should be taken into account as ST depression episodes have a high prevalence (about 20%) in hypertensive patients. ST depression and T-wave inversion may reflect true subendocardial ischemia in the absence of coronary artery disease. The prolonged QT interval and increased QT interval dispersion have been associated with LVH and ventricular arrhythmias and seem to contain significant predictive value, but inter- and intraobserver varialbility limit their wider clinical applicability. The analysis of T wave axis and T wave alternans might also be helpful for risk stratification in patients with arterial hypertension. However, the clinical usefulness of these indices in arterial hypertension and their possible role in monitoring medical treatment is under investigation and further research is needed prior to its clinical application.

### **4. References**


repeated episodes of ischemia, action potential alternans was detectable in 95% of the cases with ventricular fibrillation. The dispersion of repolarization is closely related to the temporo-spatial pattern of depolarization-repolarization, which can alternate on a beat-tobeat basis. The temporo-spatial dispersion of cellular refractoriness seems to predispose the myocardium to wave front fractionation and subsequent reentry. It is noteworthy that the TWA can be influenced by various conditions such as ischemia, hypothermia, heart rate, and sympathetic tone. Drugs such as procainamide and amiodarone reduce magnitude of alternans and sotalol can lead to the conversion of TWA from negative to positive. In the case of cardiomyopathies, the development of small areas of scars and ischemia is considered to be of pathological relevance for the development of alterations in action potentials or dispersion of repolarization. Whether T wave alternans can be used as a clinical marker of susceptibility for sudden cardiac death and cardiovascular events is under

The electrocardiographic findings in patients with arterial hypertension are valuable tools for risk stratification of these patients, by predicting cardiovascular events and sudden cardiac death. The ECG strain pattern, an old parameter of well established value, should always be searched as it provides additional prognostic information beyond the one derived from echocardiographic LVH and LVM. Also among hypertensive patients with left ventricular hypertrophy, the presence of non-sustained ventricular tachycardia on ECG Holter monitoring identifies patients with a high risk of mortality, who need more aggressive care. Silent ischemia on ECG Holter should be taken into account as ST depression episodes have a high prevalence (about 20%) in hypertensive patients. ST depression and T-wave inversion may reflect true subendocardial ischemia in the absence of coronary artery disease. The prolonged QT interval and increased QT interval dispersion have been associated with LVH and ventricular arrhythmias and seem to contain significant predictive value, but inter- and intraobserver varialbility limit their wider clinical applicability. The analysis of T wave axis and T wave alternans might also be helpful for risk stratification in patients with arterial hypertension. However, the clinical usefulness of these indices in arterial hypertension and their possible role in monitoring medical treatment is

under investigation and further research is needed prior to its clinical application.

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research.

**3. Conclusions** 

**4. References** 


**2** 

*Mexico* 

**Electrocardiographic QT Interval Prolongation** 

**Risk Factors and Clinical Implications** 

*Laboratories of Biologicals and Reagents of Mexico, BIRMEX, Mexico City,* 

Jimenez-Corona Aida, Jimenez-Corona Maria Eugenia

*National Institute of Public Health, Cuernavaca, Morelos,* 

and Gonzalez-Villalpando Clicerio

**in Subjects With and Without Type 2 Diabetes –** 

Several studies have focused on the identification of patients at risk of sudden cardiac death, which is mostly due to depolarization and repolarization impairment. The measurement of QT interval indicates the total duration of ventricular myocardial depolarization and repolarization. Localized repolarization data can be obtained easily from the standard 12 lead electrocardiogram (ECG), a non-invasive method extensively used as a tool for cardiovascular risk assessment. [1,2] Non-uniform myocardial repolarization time may result from inhomogeneity, variation of action potential duration between the individual leads of the 12-lead ECG, or localized delay in activation due to slow conduction or altered conduction pathways. To ensure the recording of the earliest depolarization at the latest repolarization of the ventricular myocardium, the maximum QT interval should be measured from the beginning of the earliest QRS complex to the end of the latest T wave from all leads of a simultaneous 12-lead ECG. Nevertheless, the QT interval may reflect increased inhomogeneity of myocardial repolarization, resulting from delayed repolarization in some areas of the myocardium, and it can be caused by a uniform increase in action potential duration. A measure that can help differentiate between these two conditions is the QT dispersion. Using both the QT prolongation and the QT dispersion the individual lead variation and the interlead variation provide a measure of repolarization

The QT interval prolongation has been proposed as a marker of cardiovascular risk in the clinical setting and it has also been particularly associated with arrhythmias, sudden death and poor survival in apparently healthy subjects. [4,5] As for diabetic subjects, although some cross-sectional studies suggest that glycemic control, ischemic heart disease, and blood pressure, among other risk factors, are associated with the QT interval prolongation, its pathogenesis remains unclear. [6,8] Also, and increased mortality in newly diagnosed type 2

The aim of this study was to estimate the prevalence of QTc interval prolongation in diabetic and non-diabetic subjects, as well as to evaluate cross-sectionally and prospectively the associated risk factors of QTc interval prolongation and its clinical implications in subjects

diabetes patients has been associated with QT interval prolongation. [9,10]

**1. Introduction** 

heterogeneity. [3]


### **Electrocardiographic QT Interval Prolongation in Subjects With and Without Type 2 Diabetes – Risk Factors and Clinical Implications**

Jimenez-Corona Aida, Jimenez-Corona Maria Eugenia and Gonzalez-Villalpando Clicerio

*National Institute of Public Health, Cuernavaca, Morelos, Laboratories of Biologicals and Reagents of Mexico, BIRMEX, Mexico City, Mexico* 

### **1. Introduction**

12 Advances in Electrocardiograms – Clinical Applications

Messerli, FH.(1999), Hypertension and sudden cardiac death. *American Journal of* 

Oikarinen et al., 2004, QRS duration and QT interval predict mortality in hypertensive patients

Okin et al., 2004, Electrocardiographic Strain Pattern and Prediction of Cardiovascular

Okin et al., 2009, Prognostic Value of Changes in the Electrocardiographic Strain Pattern

Saadeh & Jones, 2001, Predictors of sudden cardiac death in never previously treated

Salles et al., 2005, Combined QT interval and voltage criteria improve left ventricular

Salles et al., 2006, Importance of the Electrocardiographic Strain Pattern in Patients With

Salles et al., 2008, Muxfeldt ES Recent ventricular repolarization markers in resistant

Schillaci et al., 2004, Prognostic significance of isolated, non-specific left ventricular repolarization abnormalities in hypertensive. *J Hypertens*, Vol.22 (2004), pp. 407–414 Sheehana et al., 2004, QT dispersion, QT maximum and risk of cardiac death in the Caerphilly Heart Study. *Eur J Cardiovasc Prevention Rehab* Vol. 11, (2004), pp.63–68 Sigurdson et al., 1996, Silent ST-T changes in an epidemiologic cohort study – a marker of

Sokolow & Lyon, 1949, The ventricular complex in left ventricular hypertrophy as obtained by unipolar precordial and limb leads. *Am Heart J.* Vol. 68, (1949), pp.161–186 Stramba et al., 1998, Prevalence of episodes of ST-segment depression among mild to

Szlachcic et al., 1992, What is the role of silent coronary artery disease and left ventricular

Tomiyama et al., 1998, Left ventricular geometric patterns and QT dispersion in borderline

Uen et al., 2006, Myocardial ischemia during everyday life in patients with arterial

variability. *Blood Press Monit,* Vol.11, No.4 (August 2006) pp.173-182

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with left ventricular hypertrophy: the Losartan Intervention for Endpoint Reduction in Hypertension Study. *Hypertension*,Vol. 43, No. 5, (May 2004), pp. 1029-1034 Okin et al., 2000, Assessment of QT Interval and QT Dispersion for Prediction of All-Cause

and Cardiovascular Mortality in American Indians. The Strong Heart Study.

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During Antihypertensive Treatment: The Losartan Intervention for End-Point Reduction in Hypertension Study (LIFE), *Circulation*, Vol. 119 (2009) pp.1883-1891 Rosenbaum et al., 1994, Electrical alternans and vulnerability to ventricular arrhythmias. *N* 

patients with essential hypertension: long-term follow-up. *Journal of Human* 

hypertrophy detection in resistant hypertension, *Hypertension,* Vol. 46, (2005), pp.

hypertension: Are they different from the traditional QT interval? *American Journal* 

hypertension or coronary heart disease, or both: The Reykjavik Study. *J Am Coll* 

moderate hypertensive patients in northern Italy: The Cardioscreen Study. *J* 

hypertrophy in the genesis of ventricular arrhythmias in men with essential

and mild hypertension: their evolution and regression. *Am J Hypertens*, Vol. 11, No.

hypertension: prevalence, risk factors, triggering mechanism and circadian

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3, (March 1998), pp. 286-292

2004) pp. 48-54

1207–1212

Several studies have focused on the identification of patients at risk of sudden cardiac death, which is mostly due to depolarization and repolarization impairment. The measurement of QT interval indicates the total duration of ventricular myocardial depolarization and repolarization. Localized repolarization data can be obtained easily from the standard 12 lead electrocardiogram (ECG), a non-invasive method extensively used as a tool for cardiovascular risk assessment. [1,2] Non-uniform myocardial repolarization time may result from inhomogeneity, variation of action potential duration between the individual leads of the 12-lead ECG, or localized delay in activation due to slow conduction or altered conduction pathways. To ensure the recording of the earliest depolarization at the latest repolarization of the ventricular myocardium, the maximum QT interval should be measured from the beginning of the earliest QRS complex to the end of the latest T wave from all leads of a simultaneous 12-lead ECG. Nevertheless, the QT interval may reflect increased inhomogeneity of myocardial repolarization, resulting from delayed repolarization in some areas of the myocardium, and it can be caused by a uniform increase in action potential duration. A measure that can help differentiate between these two conditions is the QT dispersion. Using both the QT prolongation and the QT dispersion the individual lead variation and the interlead variation provide a measure of repolarization heterogeneity. [3]

The QT interval prolongation has been proposed as a marker of cardiovascular risk in the clinical setting and it has also been particularly associated with arrhythmias, sudden death and poor survival in apparently healthy subjects. [4,5] As for diabetic subjects, although some cross-sectional studies suggest that glycemic control, ischemic heart disease, and blood pressure, among other risk factors, are associated with the QT interval prolongation, its pathogenesis remains unclear. [6,8] Also, and increased mortality in newly diagnosed type 2 diabetes patients has been associated with QT interval prolongation. [9,10]

The aim of this study was to estimate the prevalence of QTc interval prolongation in diabetic and non-diabetic subjects, as well as to evaluate cross-sectionally and prospectively the associated risk factors of QTc interval prolongation and its clinical implications in subjects

Electrocardiographic QT Interval Prolongation

**2.1 Statistical analysis** 

Stata Corporation, 2005).

*Characteristics of the sample* 

**3. Results** 

in Subjects With and Without Type 2 Diabetes – Risk Factors and Clinical Implications 15

type 2 diabetes, without myocardial infarction at baseline corroborrated by ECG, who were

Comparisons of clinical and laboratory features were made according to diabetes status at baseline. Proportions and means (standard deviation [s.d.]) were compared by Pearson Chi2 and by T student, respectively, while medians (interquartile range [IQR]) were compared by Wilcoxon test. QTc interval was analyzed as continuous and dichotomous variable. Because of the normal distribution of the QTc interval, all analyses were carried out using the original units. QTc interval prolongation, using the Bazzett´s formula, was defined as an interval ≥430 msec in men and 450 msec in women. Partial Pearson correlation between QTc interval and some risk factors were estimated in diabetic and non-diabetic subjects, separately. To estimate the association between some cardiovascular risk factors and QTc interval prolongation in diabetic and non-diabetic subjects, both together and separately, multiple linear regression for cross-sectional analysis and generalized estimating equations regression models (GEE), with family normal identity link, for longitudinal analysis were carried out. Models were performed with the forward method considering the biological and statistical relevance. Results are given as regression β coefficients and 95% confidence interval (95%CI). P value equal or less than 0.05 was considered significant. All analyses were done with Stata/SE 9.0 (Stata statistical software: Release 9. College Station. Texas:

A total of 1661 subjects (1443 non-diabetic and 218 diabetic subjects), aged 47.4±8.2 years at baseline were included. Table 1 shows comparisons of some QT interval risk factors between subjects with and without type 2 diabetes. Individuals with diabetes were older and had greater abdominal fat and upper-body fat accumulation, as well as higher BP levels, total cholesterol and fasting and 2-hour glucose levels compared with individuals without diabetes. The percentage of hypertension was higher for subjects with diabetes (27.1%) compared with non-diabetic subjects (13.0%); likewise, the proportion of individuals under antihypertensive medication was slightly greater in the diabetic group. Although the percentage of subjects who were under antihyperlipidemic medication was higher in subjects with diabetes compared with non-diabetic subjects, these differences were not significant. As for diabetic subjects, 136 (62.4%) were prevalent cases whereas 82 (37.6%) were incident cases, with a ratio 1:1.6. Mean of age at diagnosis of diabetes was 46.6 years

Some characteristic on the ECG were compared between diabetic and non-diabetic subjects at baseline and during follow-up and are shown in table 2. The mean of heart rate, QRS duration, and R amplitude in AVL lead were significant higher in diabetic compared with non-diabetic subjects. Mean of QTc by the Bazzet´s formula was significantly higher (p<0.001) in diabetic (414.0 msec) than in non-diabetic (404.3 msec) individuals. The prevalence of longer QTc interval (≥430 msec in men and ≥450 msec in women) was greater in diabetic (10.1%) compared with non-diabetic subjects (4.0%). Prevalence was remarkably higher in both diabetic and non-diabetic men (16.3% vs. 4.5%, p<0.001) compared with

(s.d. 8.0 years) and median of diabetes duration was 1.8 (IQR 25-75% 0-7.3).

followed-up for a median of time of 4.26 and 3.32 years, respectively.

with and without type 2 diabetes who had not suffered from previous myocardial infarction corroborated on the ECG.

### **2. Methods**

The Mexico City Diabetes Study is a prospective, population-based investigation designed to describe the prevalence and incidence of diabetes and cardiovascular risk factors in lowincome urban population from Mexico City. The detailed methodology has been reported elsewhere. [11] Briefly, the sample size included 2282 men and non-pregnant women aged 35 to 64 years who completed a baseline interview and physical examination in 1989-1990. Two follow-up visits were carried out in 1994-1996 (n=1773) and in 1998-2000 (n=1764). All evaluations included medical history, physical examination, ECG, and several laboratory tests. Current smoking was defined as at least one cigarette per day in the last year. Physical examination included an anthropometric evaluation with participants wearing lightweight clothing and no shoes. Height was measured using a stadiometer with subjects standing on the floor with the back against a wall; weight was measured using a clinical scale. Body mass index (BMI) was calculated as weight/height2 in kg/m2. Waist circumference (WC) was measured considering the umbilicus as the landmark. Systolic (SBP) and diastolic blood pressure (DBP) were measured 3 times in the right arm of seated subjects (after resting for at least 5 min) using a random zero sphygmomanometer (Hawksley, London). We used the average of the last 2 readings as the BP of the participants. Hypertension was defined as SBP140 mmHg, DBP90 mmHg, or treatment with antihypertensive drugs. In every visit, participants completed a 75-g oral glucose tolerance test. Diabetes was defined according to the World Health Organization criteria with a fasting glucose ≥7mmol/l (126 mg/dl), 2 hour glucose ≥11.1 mmol/l (200 mg/dl), or treatment with oral antidiabetic drugs. [12] Fasting and 2-hour plasma glucose and insulin as well as fasting serum lipids and all other biomarkers were measured using previously reported methods [13] at the research laboratory of the Division of Clinical Epidemiology at the Medicine Department of the University of Texas Health Science Center at San Antonio, USA. Insulin resistance was estimated by the homeostasis model (HOMA-IR) as follows: [fasting insulin (units/ml) X fasting glucose (mmol/l)/22.5].

A resting standard 12-lead ECG was taken with the subject in a supine position at each examination. A standard interpretation of ECGs at a reading center (Wake Forest University, EPICARE Center) was made using the Minnesota Code. [14] Heart rate (HR), QRS duration, R amplitude in AVL lead (R-AVL), S amplitude in V3 lead (SV3), left ventricular hypertrophy (LVH), QT interval, and myocardial infarction, among other variables, were coded. Left ventricular hypertrophy (LVH) was defined as (R-AVL) + (SV3) ≥2600μv in men and ≥2200μv in women. Myocardial infaction was defined according to the following codes: Q-QS pattern with 1.1-1.2.7, Q-QS and T wave pattern 1.2.8-1.3, and wave T pattern with 5.1-5.3. QT interval was measured from the electrocardiogram tracing in lead II and defined as the first deflection of the QRS complex and the end as the point of maximal change in the slope as the T wave merges with the baseline. QT corrected (QTc) was calculated according to Bazzett's formula as QT/square root of (R–R interval). The same measurement instruments were used throughout the study.

The Institutional Review Boards of both The University of Texas Health Science Center and the Centro de Estudios en Diabetes approved the study protocol. Each participant gave informed consent. For this analysis, we included 1661 subjects, 218 with and 1443 without type 2 diabetes, without myocardial infarction at baseline corroborrated by ECG, who were followed-up for a median of time of 4.26 and 3.32 years, respectively.

### **2.1 Statistical analysis**

14 Advances in Electrocardiograms – Clinical Applications

with and without type 2 diabetes who had not suffered from previous myocardial infarction

The Mexico City Diabetes Study is a prospective, population-based investigation designed to describe the prevalence and incidence of diabetes and cardiovascular risk factors in lowincome urban population from Mexico City. The detailed methodology has been reported elsewhere. [11] Briefly, the sample size included 2282 men and non-pregnant women aged 35 to 64 years who completed a baseline interview and physical examination in 1989-1990. Two follow-up visits were carried out in 1994-1996 (n=1773) and in 1998-2000 (n=1764). All evaluations included medical history, physical examination, ECG, and several laboratory tests. Current smoking was defined as at least one cigarette per day in the last year. Physical examination included an anthropometric evaluation with participants wearing lightweight clothing and no shoes. Height was measured using a stadiometer with subjects standing on the floor with the back against a wall; weight was measured using a clinical scale. Body mass index (BMI) was calculated as weight/height2 in kg/m2. Waist circumference (WC) was measured considering the umbilicus as the landmark. Systolic (SBP) and diastolic blood pressure (DBP) were measured 3 times in the right arm of seated subjects (after resting for at least 5 min) using a random zero sphygmomanometer (Hawksley, London). We used the average of the last 2 readings as the BP of the participants. Hypertension was defined as SBP140 mmHg, DBP90 mmHg, or treatment with antihypertensive drugs. In every visit, participants completed a 75-g oral glucose tolerance test. Diabetes was defined according to the World Health Organization criteria with a fasting glucose ≥7mmol/l (126 mg/dl), 2 hour glucose ≥11.1 mmol/l (200 mg/dl), or treatment with oral antidiabetic drugs. [12] Fasting and 2-hour plasma glucose and insulin as well as fasting serum lipids and all other biomarkers were measured using previously reported methods [13] at the research laboratory of the Division of Clinical Epidemiology at the Medicine Department of the University of Texas Health Science Center at San Antonio, USA. Insulin resistance was estimated by the homeostasis model (HOMA-IR) as follows: [fasting insulin (units/ml) X

A resting standard 12-lead ECG was taken with the subject in a supine position at each examination. A standard interpretation of ECGs at a reading center (Wake Forest University, EPICARE Center) was made using the Minnesota Code. [14] Heart rate (HR), QRS duration, R amplitude in AVL lead (R-AVL), S amplitude in V3 lead (SV3), left ventricular hypertrophy (LVH), QT interval, and myocardial infarction, among other variables, were coded. Left ventricular hypertrophy (LVH) was defined as (R-AVL) + (SV3) ≥2600μv in men and ≥2200μv in women. Myocardial infaction was defined according to the following codes: Q-QS pattern with 1.1-1.2.7, Q-QS and T wave pattern 1.2.8-1.3, and wave T pattern with 5.1-5.3. QT interval was measured from the electrocardiogram tracing in lead II and defined as the first deflection of the QRS complex and the end as the point of maximal change in the slope as the T wave merges with the baseline. QT corrected (QTc) was calculated according to Bazzett's formula as QT/square root of (R–R interval). The same

The Institutional Review Boards of both The University of Texas Health Science Center and the Centro de Estudios en Diabetes approved the study protocol. Each participant gave informed consent. For this analysis, we included 1661 subjects, 218 with and 1443 without

corroborated on the ECG.

fasting glucose (mmol/l)/22.5].

measurement instruments were used throughout the study.

**2. Methods** 

Comparisons of clinical and laboratory features were made according to diabetes status at baseline. Proportions and means (standard deviation [s.d.]) were compared by Pearson Chi2 and by T student, respectively, while medians (interquartile range [IQR]) were compared by Wilcoxon test. QTc interval was analyzed as continuous and dichotomous variable. Because of the normal distribution of the QTc interval, all analyses were carried out using the original units. QTc interval prolongation, using the Bazzett´s formula, was defined as an interval ≥430 msec in men and 450 msec in women. Partial Pearson correlation between QTc interval and some risk factors were estimated in diabetic and non-diabetic subjects, separately. To estimate the association between some cardiovascular risk factors and QTc interval prolongation in diabetic and non-diabetic subjects, both together and separately, multiple linear regression for cross-sectional analysis and generalized estimating equations regression models (GEE), with family normal identity link, for longitudinal analysis were carried out. Models were performed with the forward method considering the biological and statistical relevance. Results are given as regression β coefficients and 95% confidence interval (95%CI). P value equal or less than 0.05 was considered significant. All analyses were done with Stata/SE 9.0 (Stata statistical software: Release 9. College Station. Texas: Stata Corporation, 2005).

### **3. Results**

### *Characteristics of the sample*

A total of 1661 subjects (1443 non-diabetic and 218 diabetic subjects), aged 47.4±8.2 years at baseline were included. Table 1 shows comparisons of some QT interval risk factors between subjects with and without type 2 diabetes. Individuals with diabetes were older and had greater abdominal fat and upper-body fat accumulation, as well as higher BP levels, total cholesterol and fasting and 2-hour glucose levels compared with individuals without diabetes. The percentage of hypertension was higher for subjects with diabetes (27.1%) compared with non-diabetic subjects (13.0%); likewise, the proportion of individuals under antihypertensive medication was slightly greater in the diabetic group. Although the percentage of subjects who were under antihyperlipidemic medication was higher in subjects with diabetes compared with non-diabetic subjects, these differences were not significant. As for diabetic subjects, 136 (62.4%) were prevalent cases whereas 82 (37.6%) were incident cases, with a ratio 1:1.6. Mean of age at diagnosis of diabetes was 46.6 years (s.d. 8.0 years) and median of diabetes duration was 1.8 (IQR 25-75% 0-7.3).

Some characteristic on the ECG were compared between diabetic and non-diabetic subjects at baseline and during follow-up and are shown in table 2. The mean of heart rate, QRS duration, and R amplitude in AVL lead were significant higher in diabetic compared with non-diabetic subjects. Mean of QTc by the Bazzet´s formula was significantly higher (p<0.001) in diabetic (414.0 msec) than in non-diabetic (404.3 msec) individuals. The prevalence of longer QTc interval (≥430 msec in men and ≥450 msec in women) was greater in diabetic (10.1%) compared with non-diabetic subjects (4.0%). Prevalence was remarkably higher in both diabetic and non-diabetic men (16.3% vs. 4.5%, p<0.001) compared with

Electrocardiographic QT Interval Prolongation

QTc interval by the Bazzett´s formula

QTc interval by the Bazzett´s formula

and without type 2 diabetes

IR (≥10 units) increased (Figure 3).

QTc interval by the Bazzett´s formula ≥430

Bazzett´s formula: QT/square root of (R–R interval).

Missing values in non-diabetic subjects: S amplitude in V3 lead 1.

At the end of follow-up

QTc interval by the Bazzett´s formula ≥430

At baseline

in Subjects With and Without Type 2 Diabetes – Risk Factors and Clinical Implications 17

Heart rate (bpm) 65.2 (9.2) 70.9 (12.0) <0.001 QRS duration (msec) 90.9 (10.4) 88.9 (10.7) 0.007 R amplitude in AVL lead 294.4 (239.8) 346.8 (270.5) 0.003 S amplitude in V3 lead 879.9 (523.3) 917.2 (545.1) 0.330 LVH, no (%) 33 (2.3) 6 (2.8) 0.672 QT interval (msec) 390.0 (25.4) 384.3 (28.5) 0.002

(msec) 404.3 (22.5) 414.5 (23.8) <0.001

in men and ≥450 in women (no., %) 58 (4.0) 22 (10.1) <0.001

Heart rate (bpm) 62.8 (9.5) 66.6 (9.4) <0.0001 QRS duration (msec) 89.9 (10.9) 88.0 (15.2) 0.027 R amplitude in AVL lead 338.5 (224.5) 369.0 (233.4) 0.063 S amplitude in V3 lead 815 (434.7) 880.3 (503.5) 0.045 LVH, no (%) 20 (1.4) 6 (2.8) 0.130

(msec) 406.7 (22.3) 416.1 (21.6) <0.001

in men and ≥450 in women (no., %) 77 (5.3) 20 (9.2) 0.024

Table 2. QTc interval segment and other electrocardiographic parameters in subjects with

similar trend was observed when BMI was substituted for WC. (Data not shown)

Figure 1 shows the relation between QTc interval and BMI in subjects with and without type 2 diabetes according to age. The values of QTc interval were slightly greater in subjects with greater BMI in both diabetic and non-diabetic individuals, regardless of age group. A

As for fasting glucose in diabetic subjects, a slight increment in the QTc interval was observed in subjects with higher levels of fasting glucose, particularly in subjects with levels between 12 mmol/l and 20 mmol/l. (Figure 2) For non-diabetic subjects, a modest increment in the QTc interval was observed when 2-hour glucose (≥6 mmol/l) and HOMA-

Non-diabetic

n=1443 mean (s.d.)

subjects Diabetic subjects p value

n=218 mean (s.d.)


\*Median (IQR 25-75).

†Only subjects with hypertension.

Missing values in non-diabetic subjects: WC, 26; HDL-C, 2; 2-hour glucose, 1; antihyperlipidemic medication, 37.

Missing values in diabetic subjects: current smoking, 1; WC, 3; triglycerides, 2; 2-hour glucose, 122; antihyperlipidemic medication, 9.

Table 1. Characteristic of the study population by diabetes status

women (6.5% vs. 3.7%, p>0.05). Similar differences were observed on the QTc interval as continuous and dichotomous variable during the follow-up. In addition, after stratifying by hypertension, prevalence of longer QTc interval was significantly higher in both diabetic (13.3%) and non-diabetic subjects (5.2%) with hypertension compared with diabetic (8.9%) and non-diabetic subjects (3.8%) without hypertension. When stratification was made by BMI<25 and BMI≥25, prevalence remained significantly higher in diabetic compared with non-diabetic subjects with normal weight (14% vs. 1.8%, respectively) and with overweigh/obesity (9.1% vs. 4.7%, respectively).

Age (years) 46.6 (8.0) 52.5 (7.7) <0.001 Age at diabetes diagnosis (years) - 47.8 (8.3) - Women (no., %) 848 (58.8) 138 (63.3) 0.204 Current smoking (no., %) 487 (33.8) 65 (30.0) 0.269 BMI (kg/m2) 28.0 (4.3) 29.1 (4.8) 0.001

Men 93.6 (9.1) 98.8 (11.7) <0.001 Women 97.8 (13.4) 101.6 (11.5) 0.002 Hypertension (no., %) 188 (13.0) 61 (28.0) <0.001 SBP (mmHg) 115.6 (16.1) 122.9 (18.6) <0.001 DBP (mmHg) 72.5 (10.3) 75.4 (9.6) <0.001 Total cholesterol (mmol/L)\* 4.9 (4.2-5.6) 5.2 (4.5-5.9) <0.001

Men 0.7 (0.6-0.9) 0.8 (0.7-0.9) 0.319 Women 0.9 (0.7-1.0) 0.9 (0.7-1.0) 0.334 Triglycerides (mmol/L)\* 1.9 (1.4-2.8) 2.5 (1.8-3.6) <0.001 Fasting glucose (mmol/L)\* 4.7 (4.2-5.1) 8.9 (6.6-13.5) <0.001 2-hour glucose (mmol/L)\* 5.7 (4.6-6.8) 14.1 (11.6-18.8) <0.001 Antihypertensive medication (no., %)† 67 (35.6) 27 (44.3) 0.227 Antihyperlipidemic medication (no., %) 4 (0.3) 1 (0.5) 0.638

Prevalent - 136 (62.4) - Incident - 82 (37.6) - Hypoglycemic medication (no., %) - 143 (65.9) - Diabetes duration (years)\* - 1.8 (0-7.3) - Duration of follow-up (years)\* 3.32 (3.15-3.74) 4.26 (4.00-4.44) 0.0001

Missing values in non-diabetic subjects: WC, 26; HDL-C, 2; 2-hour glucose, 1; antihyperlipidemic

Missing values in diabetic subjects: current smoking, 1; WC, 3; triglycerides, 2; 2-hour glucose, 122;

women (6.5% vs. 3.7%, p>0.05). Similar differences were observed on the QTc interval as continuous and dichotomous variable during the follow-up. In addition, after stratifying by hypertension, prevalence of longer QTc interval was significantly higher in both diabetic (13.3%) and non-diabetic subjects (5.2%) with hypertension compared with diabetic (8.9%) and non-diabetic subjects (3.8%) without hypertension. When stratification was made by BMI<25 and BMI≥25, prevalence remained significantly higher in diabetic compared with non-diabetic subjects with normal weight (14% vs. 1.8%, respectively) and with

Table 1. Characteristic of the study population by diabetes status

overweigh/obesity (9.1% vs. 4.7%, respectively).

n=1443 n=218

Waist circumference (cm)

HDL-C, (mmol/L)\*

Diabetes cases

\*Median (IQR 25-75).

medication, 37.

†Only subjects with hypertension.

antihyperlipidemic medication, 9.

Non-diabetic

subjects Diabetic subjects p value


Missing values in non-diabetic subjects: S amplitude in V3 lead 1. Bazzett´s formula: QT/square root of (R–R interval).

Table 2. QTc interval segment and other electrocardiographic parameters in subjects with and without type 2 diabetes

Figure 1 shows the relation between QTc interval and BMI in subjects with and without type 2 diabetes according to age. The values of QTc interval were slightly greater in subjects with greater BMI in both diabetic and non-diabetic individuals, regardless of age group. A similar trend was observed when BMI was substituted for WC. (Data not shown)

As for fasting glucose in diabetic subjects, a slight increment in the QTc interval was observed in subjects with higher levels of fasting glucose, particularly in subjects with levels between 12 mmol/l and 20 mmol/l. (Figure 2) For non-diabetic subjects, a modest increment in the QTc interval was observed when 2-hour glucose (≥6 mmol/l) and HOMA-IR (≥10 units) increased (Figure 3).

Electrocardiographic QT Interval Prolongation

300

subjects with type 2 diabetes.

95%CI 0.08; 0.78) (Table 3).

*Longitudinal analysis* 

*Cross-sectional analysis* 

350

400

QTc interval (msec)

450

500

550

in Subjects With and Without Type 2 Diabetes – Risk Factors and Clinical Implications 19

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 Fasting glucose level (mmol/l)

In the whole sample, QTc interval prolongation was significantly associated with age, sex, BMI, and diabetes. For each unit of change on age (year), the QTc interval increased 0.36 msec (95%CI 0.23; 0.49). For each unit of increment on BMI, the QTc interval increased 0.75 msec (95%CI 0.51; 1.00). Women had a greater QTc interval mean than men (difference of 13.36 msec (95%CI 11.36; 15.76). Regarding diabetes, the difference on the QTc interval between diabetic and non-diabetic subjects was 6.33 msec (95%CI 3.24; 9.43). Models stratified by diabetes status were also performed. In diabetic subjects, risk factors significantly associated with QTc interval were sex and BMI, whereas age had a borderline significance. For each unit of increment on BMI, the QTc interval increased 0.72 msec (95%CI 0.07; 1.37). Women had a greater QTc interval than men (difference of 15.93 msec, 95%CI 9.50; 22.35). In non-diabetic subjects, risk factors significantly associated with QTc interval were age (beta=0.35, 95%CI 0.21; 0.50), sex (beta=13.69, 95%CI 11.27; 16.10), BMI (beta=0.62, 95%CI 0.33; 0.91), hypertension (beta=4.18, 95%CI 0.14; 8.22), and HOMA-IR (beta=0.43,

When the whole sample was considered, the progression of QTc interval prolongation was significantly associated with age, sex, BMI, hypertension, and diabetes. The QTc interval prolongation increased with age (beta= 0.33, 95%CI 0.24; 0.42) and BMI (beta= 0.73, 95%CI 0.56; 0.90). Women had a greater QTc interval prolongation than men (difference of 11.84 msec, 95%CI 10.09; 13.59), as did diabetic compared with non-diabetic subjects (difference of 6.58 msec, 95%CI 4.02; 9.13). In a model restricted to diabetic subjects, the QTc interval was predicted by sex (beta= 11.18, 95%CI 6.27; 16.10), BMI (beta= 0.84, 95%CI 0.38; 1.30), and fasting glucose (beta= 0.42, 95%CI 0.11; 0.74). In non-diabetic subjects, predictors of QTc

Fig. 2. Comparison between QTc interval level and fasting glucose level at baseline in

Fig. 1. Comparison between QTc interval level and BMI at baseline in subjects with and without type 2 diabetes stratifying by age <50 (upper panel) years and ≥50 years (bottom panel).

In diabetic subjects, partial Pearson correlations with QTc interval were statistically significant for age (rho=0.15, p=0.024), BMI (rho=0.19, p=0.006), and WC (rho=0.20, p=0.003). No significant correlation with diabetes duration was observed. In non-diabetic subjects, correlations with QTc interval were significant for age (rho=0.16, p<0.0001), SPB (rho=0.10, p=0.0002), DBP (rho=0.07, p=0.008), BMI (rho=0.23, p<0.0001), and WC (rho=0.21, p<0.0001). During follow-up, the correlation of QTc interval with BMI and WC remained significant (p<0.001) in both diabetic (BMI, rho=0.24 and WC, rho=0.25) and non-diabetic individuals (BMI, rho =0.23 and WC, rho=0.26).

Fig. 2. Comparison between QTc interval level and fasting glucose level at baseline in subjects with type 2 diabetes.

### *Cross-sectional analysis*

18 Advances in Electrocardiograms – Clinical Applications

Non-diabetic subjects0 Diabetic subjects

20 30 40 50 60 20 30 40 50 60

Body mass index (kg/m2)

Non-diabetic subjects Diabetic subjects

20 30 40 50 60 20 30 40 50 60

Fig. 1. Comparison between QTc interval level and BMI at baseline in subjects with and without type 2 diabetes stratifying by age <50 (upper panel) years and ≥50 years (bottom panel).

In diabetic subjects, partial Pearson correlations with QTc interval were statistically significant for age (rho=0.15, p=0.024), BMI (rho=0.19, p=0.006), and WC (rho=0.20, p=0.003). No significant correlation with diabetes duration was observed. In non-diabetic subjects, correlations with QTc interval were significant for age (rho=0.16, p<0.0001), SPB (rho=0.10, p=0.0002), DBP (rho=0.07, p=0.008), BMI (rho=0.23, p<0.0001), and WC (rho=0.21, p<0.0001). During follow-up, the correlation of QTc interval with BMI and WC remained significant (p<0.001) in both diabetic (BMI, rho=0.24 and WC, rho=0.25) and non-diabetic

Body mass index (kg/m2)

300

300

individuals (BMI, rho =0.23 and WC, rho=0.26).

350

400

QTc interval (msec)

450

500

550

350

400

QTc interval (msec)

450

500

550

In the whole sample, QTc interval prolongation was significantly associated with age, sex, BMI, and diabetes. For each unit of change on age (year), the QTc interval increased 0.36 msec (95%CI 0.23; 0.49). For each unit of increment on BMI, the QTc interval increased 0.75 msec (95%CI 0.51; 1.00). Women had a greater QTc interval mean than men (difference of 13.36 msec (95%CI 11.36; 15.76). Regarding diabetes, the difference on the QTc interval between diabetic and non-diabetic subjects was 6.33 msec (95%CI 3.24; 9.43). Models stratified by diabetes status were also performed. In diabetic subjects, risk factors significantly associated with QTc interval were sex and BMI, whereas age had a borderline significance. For each unit of increment on BMI, the QTc interval increased 0.72 msec (95%CI 0.07; 1.37). Women had a greater QTc interval than men (difference of 15.93 msec, 95%CI 9.50; 22.35). In non-diabetic subjects, risk factors significantly associated with QTc interval were age (beta=0.35, 95%CI 0.21; 0.50), sex (beta=13.69, 95%CI 11.27; 16.10), BMI (beta=0.62, 95%CI 0.33; 0.91), hypertension (beta=4.18, 95%CI 0.14; 8.22), and HOMA-IR (beta=0.43, 95%CI 0.08; 0.78) (Table 3).

#### *Longitudinal analysis*

When the whole sample was considered, the progression of QTc interval prolongation was significantly associated with age, sex, BMI, hypertension, and diabetes. The QTc interval prolongation increased with age (beta= 0.33, 95%CI 0.24; 0.42) and BMI (beta= 0.73, 95%CI 0.56; 0.90). Women had a greater QTc interval prolongation than men (difference of 11.84 msec, 95%CI 10.09; 13.59), as did diabetic compared with non-diabetic subjects (difference of 6.58 msec, 95%CI 4.02; 9.13). In a model restricted to diabetic subjects, the QTc interval was predicted by sex (beta= 11.18, 95%CI 6.27; 16.10), BMI (beta= 0.84, 95%CI 0.38; 1.30), and fasting glucose (beta= 0.42, 95%CI 0.11; 0.74). In non-diabetic subjects, predictors of QTc

Electrocardiographic QT Interval Prolongation

Age (years) 0.35

Women 13.69

BMI (kg/m2) 0.62

Hypertension 4.18

HDL-C (mmol/L) -2.50

HOMA-IR 0.43

Fasting glucose

Diabetes duration

Antihypertensive medication

Hypoglycemic

**4. Discussion** 

Non-diabetic subjects N=1368

(95%CI) <sup>p</sup>

(0.21;0.50) <0.001 0.37

(11.27;16.10) <0.001 15.93

(0.33;0.91) <0.001 0.72

(0.14;8.22) 0.043 0.93

(-7.68;2.68) 0.344 -5.56

Diabetes - - - - 6.33

Table 3. Risk factors associated with the QT interval prolongation in subjects with and

duration was observed (beta=0.42, 95%CI 0.04; 0.80, p=0.032). (Data not shown)

interval prolongation were age (beta=0.34, 95%CI 0.25; 0.44), sex (beta=12.23, 95%CI 10.29; 14.18), BMI (beta=0.64, 95%CI 0.44; 0.83), hypertension (beta=3.72, 95%CI 1.57; 5.87), HOMA-IR (beta=0.45, 95%CI 0.16; 0.75) (Table 4). Antihypertensive therapy had a negative effect in QTc prolongation. In a multivariate model with diabetes duration equal or greater than 1 year, the increment of QT interval prolongation remained similar for fasting glucose (beta=0.38, 95%CI 0.06; 0.70, p=0.021), whereas a significant increment with diabetes

The methods used in the Mexico City Diabetes Study meet the accepted international criteria in terms of study protocol, diagnostic algorithms, and particularly electrocardiographic interpretations. [11] The ECGs were interpreted without disclosure of clinical or laboratory data, in a reference center recognized as a gold standard for this procedure. A rigorous quality control procedure was followed along the study. In this population, there is a high prevalence of cardiovascular risk factors, namely overweight,

Beta

(mmol/L) - - 0.44

(years) - - 0.28


medication - - -4.45

without type 2 diabetes. Cross-sectional analysis

in Subjects With and Without Type 2 Diabetes – Risk Factors and Clinical Implications 21

Diabetic subjects N=218

(95%CI) <sup>p</sup>

(-0.04;0.79) 0.077 0.36

(9.50;22.35) <0.001 13.56

(0.07;1.37) 0.030 0.75

(-6.02;7.88) 0.792 3.11

(-18.67;7.54) 0.404 -2.69

(-0.29;0.85) 0.331 - -

(-11.91;3.00) 0.240 - -

(-0.25;1.13) 0.209

(0.08;0.78) 0.016 - - - -

(-8.68;4.17) 0.491 - - -1.21

Beta

Whole sample N=1661

(95%CI) <sup>p</sup>

(0.23;0.49) <0.0001

(11.36;15.76) <0.0001

(0.51;1.00) <0.0001

(-0.47;6.69) 0.088

(-7.33;1.94) 0.255

(3.24-9.43) <0.0001

(-6.64;4.23) 0.664

Beta

Fig. 3. Comparison between QTc interval level and fasting glucose, 2-hour glucose and HOMA-IR levels at baseline in subjects without type 2 diabetes.


Table 3. Risk factors associated with the QT interval prolongation in subjects with and without type 2 diabetes. Cross-sectional analysis

interval prolongation were age (beta=0.34, 95%CI 0.25; 0.44), sex (beta=12.23, 95%CI 10.29; 14.18), BMI (beta=0.64, 95%CI 0.44; 0.83), hypertension (beta=3.72, 95%CI 1.57; 5.87), HOMA-IR (beta=0.45, 95%CI 0.16; 0.75) (Table 4). Antihypertensive therapy had a negative effect in QTc prolongation. In a multivariate model with diabetes duration equal or greater than 1 year, the increment of QT interval prolongation remained similar for fasting glucose (beta=0.38, 95%CI 0.06; 0.70, p=0.021), whereas a significant increment with diabetes duration was observed (beta=0.42, 95%CI 0.04; 0.80, p=0.032). (Data not shown)

### **4. Discussion**

20 Advances in Electrocardiograms – Clinical Applications

2 4 6 8 10 12 Fasting glucose level (mmol/l)

2 4 6 8 10 12 2-hours glucose level (mmol/l)

hour

0 5 10 15 20 25 HOMA-IR

Fig. 3. Comparison between QTc interval level and fasting glucose, 2-hour glucose and

300

300

> 300

HOMA-IR levels at baseline in subjects without type 2 diabetes.

350

400

QTc interval (msec)

450

500

550

350

400

QTc interval (msec)

450

500

550

350

400

QTc interval (msec)

450

500

550

> The methods used in the Mexico City Diabetes Study meet the accepted international criteria in terms of study protocol, diagnostic algorithms, and particularly electrocardiographic interpretations. [11] The ECGs were interpreted without disclosure of clinical or laboratory data, in a reference center recognized as a gold standard for this procedure. A rigorous quality control procedure was followed along the study. In this population, there is a high prevalence of cardiovascular risk factors, namely overweight,

Electrocardiographic QT Interval Prolongation

IQR 2.1-10.6).

duration.

regulate heart rate and the heart rate variability. [19,20]

the QTc interval is not mediated by insulin.

able to determine the type of variation on repolarization.

in Subjects With and Without Type 2 Diabetes – Risk Factors and Clinical Implications 23

prolongation has been associated with a high risk of ischemic heart disease, ventricular fibrillation, and sudden death (range 2 to 5) in several studies, even in subjects with short duration of diabetes. [10] In our study, we observed a significantly higher proportion of prolonged QTc interval in diabetic compared with non-diabetic subjects (10.1% vs. 4.0%). After adjustment for other risk factors, the mean difference on the QTc interval between diabetic and non-diabetic subjects was 6.33 msec. It has been suggested that this difference relates to the sympathetic activity present in diabetes, which reduces both the ability to

As for diabetes duration, it has been reported as a risk factor for chronic complications, including QTc interval prolongation, the latter being related to neuropathy in subjects with diabetes. [19] In our study, neither at baseline nor at follow-up duration of diabetes was significantly associated with QTc interval, which could be explained, in part, by the high proportion of new cases at baseline (37.6%). When the analysis was restricted to subjects with diabetes duration equal or greater than 1 year, QTc interval prolongation was predicted by duration, independently of other risk factors, despite the short median duration of the disease in subjects with previous diagnosis of diabetes (median 5.4 years,

We found a significant prospective association between fasting plasma glucose and prolonged QTc interval in diabetic subjects, even after adjustment for other risk factors. Some studies have reported an association between fasting glucose and QTc interval, particularly in individuals in the normal high level or with impaired fasting glucose, after adjustment for diabetes duration, among other risk factors. [21-22] However, other studies have not found any significant association. [23] As for non-diabetic subjects, we noted an independent association between HOMA-IR and QT-interval prolongation. These results showed an important degree of insulin resistance, maybe related to overweight and obesity in this population, and both predicted QTc interval prolongation. No association was observed with fasting plasma glucose in this group. By contrast, previous studies have reported an association between plasma glucose and QT interval in healthy subjects [24], even after hyperglycemic clamp insulin release, which suggests that the effect of glucose on

The association of higher BMI with the prolongation of the QTc interval observed in the non-diabetic group is not clearly seen in the diabetic group, probably because of the effect of weight loss as a result of poor metabolic control. It is particularly interesting the finding that HOMA-IR index has a significant association with the prolongation of the QTc duration in both the cross-sectional and the prospective analyses. The pathophysiologic implications direct our attention to the cellular effects of insulin resistance in the electrophysiology that mediates the depolarization and repolarization of the myocardium. Somewhat surprisingly, we could not show a demonstrable effect of therapy for diabetes or hypertension in the QTc

Some of the limitations with the QT interval evaluation relate to the lack of accuracy and reproducibility of the measurements, since there is no standard method for analysis and lead selection. The definition for the end of the QT interval is unclear as well, and may represent a changing T wave morphology that could provide a measure of altered disparity of repolarization. [25,26] Nevertheless, its application as a non-invasive and cost-effective screening tool is invaluable for cardiovascular risk stratification of population. On the other hand, because of the lack of QT interval dispersion measurement in this study, we were not


Models were run by using generalized estimating equations with family normal and identity link.

Table 4. Risk factors associated with the QT interval prolongation in subjects with and without type 2 diabetes. Longitudinal analysis

obesity, diabetes, hypertension, and dyslipidemia. [15-17] This circumstance offers a unique opportunity to study the effect of the above-mentioned factors on the QTc interval as a proxy for the repercussions of the electrophysiologic phenomena on the heart cycle. Our findings clearly show the deleterious effects of the identified cardiovascular risk factors on the QTc interval, particularly those related to insulin resistance.

In the present study, prevalence of QTc interval prolongation was higher in diabetic that in non-diabetic subjects without previous myocardial infarction detected by ECG, independently of age and sex. As expected, subjects with diabetes had 6 times the risk of developing QTc interval prolongation compared with non-diabetic subjects. In multivariate models, QTc interval prolongation was consistently predicted by sex, BMI, and fasting glucose in diabetic subjects. In non-diabetic subjects, age, sex, BMI, hypertension, HOMA-IR, and antihypertensive medication predicted QTc interval prolongation. In both groups, results were largely unchanged when WC was used in place of BMI, or when 2-hour glucose was included instead of fasting and HOMA-IR in diabetic and non-diabetic subjects, respectively.

Several studies have demonstrated that the prevalence of prolonged QTc interval is higher in subjects with type 2 diabetes (26%) than in subjects without. [18-21] Also, the QTc interval

Diabetic subjects N=216

(-0.8; 0.53) 0.151 0.33

(6.27; 16.10) <0.0001 11.84

(0.38; 1.30) <0.0001 0.73

(-1.80; 5.14) 0.345 2.63

(0.16; 0.75) 0.002 - - - -

(-6.50; -0.64) 0.017 - - -1.27

(0.11; 0.74) 0.009 - -

(-0.12; 0.61) 0.191 - -

(-8.41; 2.41) 0.277 - -

Beta (95%CI) <sup>p</sup> Whole sample N=1661

(95%CI) <sup>p</sup>

(0.24; 0.42) <0.0001

(10.09; 13.59) <0.0001

(0.56; 0.90) <0.0001

(0.74; 4.52) 0.006

(4.02; 9.13) <0.0001

(-3.78; 1.24)

Beta

Non-diabetic subjects N=1368

(95%CI) <sup>p</sup>

(0.25; 0.44) <0.0001 0.22

(10.29; 14.18) <0.0001 11.18

(0.44; 0.83) 0.0001 0.84

(1.57; 5.87) 0.001 1.67

Diabetes - - - - 6.58

Models were run by using generalized estimating equations with family normal and identity link. Table 4. Risk factors associated with the QT interval prolongation in subjects with and

obesity, diabetes, hypertension, and dyslipidemia. [15-17] This circumstance offers a unique opportunity to study the effect of the above-mentioned factors on the QTc interval as a proxy for the repercussions of the electrophysiologic phenomena on the heart cycle. Our findings clearly show the deleterious effects of the identified cardiovascular risk factors on

In the present study, prevalence of QTc interval prolongation was higher in diabetic that in non-diabetic subjects without previous myocardial infarction detected by ECG, independently of age and sex. As expected, subjects with diabetes had 6 times the risk of developing QTc interval prolongation compared with non-diabetic subjects. In multivariate models, QTc interval prolongation was consistently predicted by sex, BMI, and fasting glucose in diabetic subjects. In non-diabetic subjects, age, sex, BMI, hypertension, HOMA-IR, and antihypertensive medication predicted QTc interval prolongation. In both groups, results were largely unchanged when WC was used in place of BMI, or when 2-hour glucose was included instead of fasting and HOMA-IR in diabetic and non-diabetic subjects,

Several studies have demonstrated that the prevalence of prolonged QTc interval is higher in subjects with type 2 diabetes (26%) than in subjects without. [18-21] Also, the QTc interval

Beta

0.34

0.64

(mmol/L) - - 0.42

(years) - - 0.24


medication - - -3.00

the QTc interval, particularly those related to insulin resistance.

without type 2 diabetes. Longitudinal analysis

Women 12.23

Hypertension 3.72

HOMA-IR 0.45

Fasting glucose

Diabetes duration

Antihypertensive medication

Hypoglycemic

respectively.

Age (years)

BMI (kg/m2) prolongation has been associated with a high risk of ischemic heart disease, ventricular fibrillation, and sudden death (range 2 to 5) in several studies, even in subjects with short duration of diabetes. [10] In our study, we observed a significantly higher proportion of prolonged QTc interval in diabetic compared with non-diabetic subjects (10.1% vs. 4.0%). After adjustment for other risk factors, the mean difference on the QTc interval between diabetic and non-diabetic subjects was 6.33 msec. It has been suggested that this difference relates to the sympathetic activity present in diabetes, which reduces both the ability to regulate heart rate and the heart rate variability. [19,20]

As for diabetes duration, it has been reported as a risk factor for chronic complications, including QTc interval prolongation, the latter being related to neuropathy in subjects with diabetes. [19] In our study, neither at baseline nor at follow-up duration of diabetes was significantly associated with QTc interval, which could be explained, in part, by the high proportion of new cases at baseline (37.6%). When the analysis was restricted to subjects with diabetes duration equal or greater than 1 year, QTc interval prolongation was predicted by duration, independently of other risk factors, despite the short median duration of the disease in subjects with previous diagnosis of diabetes (median 5.4 years, IQR 2.1-10.6).

We found a significant prospective association between fasting plasma glucose and prolonged QTc interval in diabetic subjects, even after adjustment for other risk factors. Some studies have reported an association between fasting glucose and QTc interval, particularly in individuals in the normal high level or with impaired fasting glucose, after adjustment for diabetes duration, among other risk factors. [21-22] However, other studies have not found any significant association. [23] As for non-diabetic subjects, we noted an independent association between HOMA-IR and QT-interval prolongation. These results showed an important degree of insulin resistance, maybe related to overweight and obesity in this population, and both predicted QTc interval prolongation. No association was observed with fasting plasma glucose in this group. By contrast, previous studies have reported an association between plasma glucose and QT interval in healthy subjects [24], even after hyperglycemic clamp insulin release, which suggests that the effect of glucose on the QTc interval is not mediated by insulin.

The association of higher BMI with the prolongation of the QTc interval observed in the non-diabetic group is not clearly seen in the diabetic group, probably because of the effect of weight loss as a result of poor metabolic control. It is particularly interesting the finding that HOMA-IR index has a significant association with the prolongation of the QTc duration in both the cross-sectional and the prospective analyses. The pathophysiologic implications direct our attention to the cellular effects of insulin resistance in the electrophysiology that mediates the depolarization and repolarization of the myocardium. Somewhat surprisingly, we could not show a demonstrable effect of therapy for diabetes or hypertension in the QTc duration.

Some of the limitations with the QT interval evaluation relate to the lack of accuracy and reproducibility of the measurements, since there is no standard method for analysis and lead selection. The definition for the end of the QT interval is unclear as well, and may represent a changing T wave morphology that could provide a measure of altered disparity of repolarization. [25,26] Nevertheless, its application as a non-invasive and cost-effective screening tool is invaluable for cardiovascular risk stratification of population. On the other hand, because of the lack of QT interval dispersion measurement in this study, we were not able to determine the type of variation on repolarization.

Electrocardiographic QT Interval Prolongation

Antonio. Diabetes. 1992; 41:484-492.

316:745-746.

1141.

262.

3):253-262.

in Subjects With and Without Type 2 Diabetes – Risk Factors and Clinical Implications 25

[10] Naas AAO, Davidson NC, Thompson C, Cummings F, Ogston SA, Jung RT, Newton

[11] Stern MP, Gonzalez C, Mitchell BD, Villalpando E, Haffner SM, Hazuda HP. Genetic

[12] World Health Organization: Definition, Diagnosis and Classification of Diabetes

[14] Prineas R, Crow R, Blackburn H. The Minnesota Code Manual of Electrocardiographic

[15] Gonzalez-Villalpando C, Stern MP, Villalpando E, Hazuda H, Haffner S, Lisci E.

[16] Haffner S, Gonzalez-Villalpando C, Hazuda HP, et al. Prevalence of hypertension in Mexico City and San Antonio, Texas. Circulation 1994; 90:1542-1549. [17] Gonzalez-Villalpando C, Stern MP, Arredondo-Perez B, Martinez-Diaz S, Haffner S.

[18] Veglio M, Bruno G, Borra M, Macchia G, Bargero G, D'Errico N, Pagano GF, Cavallo-

[19] Weik, Dorian P, Newman D, Langer A. Association between QT dispersion and autonomic dysfunction in patients with diabetes mellitus. JACC. 1995; 26:859-863. [20] Psallas M, Tentolouris N, Cokkinos A, Papadogiannis D, Cokkinos DV, Katsilambros

[21] Brown DW, Giles WH, Greenlund KJ, Valdez R, Croft JB. Impaired fasting glucose,

[22] Cardoso CR, Salles GF, Deccache W. Prognostic value of QT interval parameters in

[23] Cardoso C, Salles G, Bloch K, Deccache W, Siqueira-Filho AG. Clinical determinants of

Examination Survey. J Cardiovasc Risk. 2001; 8:227-233.

Diabetes Complications. 2003; 17:169-178.

Augsburg Cohort Study. Diabetes Care. 2008; 31:556-561.

Findings. Littleton, MA, John Wrigth-PSG, Inc., 1982

economic level. Rev Invest Clin. 1992; 44:321-328.

Diabetes Study. Arch Med Res 1996; 27:19-23.

based cohort. J Intern Med. 2002; 25:317-324.

dysfunction in the diabetic and nondiabetic population: the MONICA/KORA

RW, Struthers AD. QT and QTc dispersion are accurate predictors of cardiac death in newly diagnosed non-insulin –dependent diabetes: cohort study. BMJ. 1998;

and environmental determinants of type II diabetes in Mexico City and San

Mellitus and its Complications: Report of a WHO Consultation. Part 1: Diagnosis and Classification of Diabetes Mellitus. Geneva, World Health Organization, 1999. [13] Haffner SM, Kennedy E, Gonzalez C, Stern MP, Miettinen H. A Prospective analysis of

the HOMA model: The Mexico City Diabetes Study. Diabetes Care. 1996; 19:1138-

Prevalence of diabetes and glucose intolerance in an urban population at a low-

Undiagnosed hypercholesterolemia: A serious health challenge. The Mexico City

Perin P. Prevalence of increased QT interval duration and dispersion in type 2 diabetic patients and its relationship with coronary heart disease: a population-

N. QT dispersion: comparison between diabetic and non-diabetic individuals and correlation with cardiac autonomic neuropathy. Hellenic J Cardiol. 2006; 47:255-

diabetes mellitus, and cardiovascular disease risk factors are associated with prolonged QTc duration. Results from the Third National Health and Nutrition

type 2 diabetes mellitus: results of a long-term follow-up prospective study. J

increased QT dispersion in patients with diabetes mellitus. Int J Cardiol. 2001; 79(2-

### **5. Conclusion**

In this study, in addition to specific cardiovascular risk factors associated with the QTc interval prolongation in diabetic and non-diabetic subjects, general excess of body weight measured by BMI was a significant risk factor for both groups. Our findings clearly show the deleterious effects of the identified cardiovascular risk factors on the QTc interval, particularly those related to insulin resistance. In diabetic subjects, the lack of metabolic control (measured by fasting glucose level) predicted strongly the QTc prolongation, whereas in non-diabetic subjects the presence of insulin resistance (HOMA-IR) predicted it. Given the cardiovascular clinical implications of QTc interval in subjects with and without diabetes, further interventional researches are needed to confirm whether the metabolic control in diabetic subjects and the decrease of insulin resistance in non-diabetic subjects together with weight reduction can prevent QTc interval prolongation.

### **6. Acknowledgments**

The authors would like to thank the residents of the neighborhoods that participated in the study. The Research Grant RO1HL 24799 of the National Heart Lung and Blood Institute, Bethesda, MD, USA, supported this work. Funding from The Consejo Nacional de Ciencia y Tecnologia, CONACYT, Grants 2092/M9303, F677-M9407, 3502-M9607 helped in some parts of the study. The Fundacion Mexicana para la Salud provided administrative support.

### **7. References**


In this study, in addition to specific cardiovascular risk factors associated with the QTc interval prolongation in diabetic and non-diabetic subjects, general excess of body weight measured by BMI was a significant risk factor for both groups. Our findings clearly show the deleterious effects of the identified cardiovascular risk factors on the QTc interval, particularly those related to insulin resistance. In diabetic subjects, the lack of metabolic control (measured by fasting glucose level) predicted strongly the QTc prolongation, whereas in non-diabetic subjects the presence of insulin resistance (HOMA-IR) predicted it. Given the cardiovascular clinical implications of QTc interval in subjects with and without diabetes, further interventional researches are needed to confirm whether the metabolic control in diabetic subjects and the decrease of insulin resistance in non-diabetic subjects

The authors would like to thank the residents of the neighborhoods that participated in the study. The Research Grant RO1HL 24799 of the National Heart Lung and Blood Institute, Bethesda, MD, USA, supported this work. Funding from The Consejo Nacional de Ciencia y Tecnologia, CONACYT, Grants 2092/M9303, F677-M9407, 3502-M9607 helped in some parts of the study. The Fundacion Mexicana para la Salud provided administrative support.

[1] Gardner MJ, Montague TJ, Armstrong CS, Horacek BM, Smith ER. Vulnerability to

[2] Abildskov JA, Green LS. The recognition of arrhythmia vulnerability by body surface

[3] Mirvis DM. Spatial variation of QT intervals in normal persons and patients with acute

[4] Schouten EG, Dekker JM, Meppelimk P, Kok FJ, Vanderbroucke JP, Pool J. QT interval

[5] Shin HS, Lee WY, Kim SW, Jung CH, Rhee EJ, Kim BJ, Sung KC, Kim BS, Kang JH, Lee

corrected QT interval in non-diabetic subjects. Circ J. 2005;69:409-413. [6] Kumar R, Fisher M, Macfarlane PW. Review: Diabetes and the QT interval: time for debate. British Journal of Diabetes & Vascular Disease. 2004; 4:146-150. [7] Okin PM, Devereux RV, Lee ET, Galloway JM, Howard BV. Electrocardiographic

mortality in diabetes. The Strong Heart Study. Diabetes. 2004; 53:434-440. [8] Salles GF, Cardoso CRL, Deccache W. Multivariate associates of QT interval parameters

and geometric patterns. Journal of Human Hypertension. 2003; 17:561–567. [9] Ziegler D, Zentai CP, Perz S, Rathmann W, Haastert B, Döring A, Meisinger C; KORA

electrocardiographic mapping Circulation. 1987; 75(4 Pt2):III79-85.

myocardial infarction. J Am Coll Cardiol. 1985; 5:625-631.

population. Circulation. 1991; 84:1516-1523.

ventricular arrhythmias: Assessment by mapping of body surface potential.

prolongation predicts cardiovascular mortality in an apparently healthy

MH, Park JR. Sex difference in the relationship between insulin resistance and

repolarization complexity and abnormality predict all-cause and cardiovascular

in diabetic patients with arterial hypertension: importance of left ventricular mass

Study Group. Prediction of mortality using measures of cardiac autonomic

together with weight reduction can prevent QTc interval prolongation.

**5. Conclusion** 

**6. Acknowledgments** 

**7. References** 

Circulation. 1986; 73:684-692.

dysfunction in the diabetic and nondiabetic population: the MONICA/KORA Augsburg Cohort Study. Diabetes Care. 2008; 31:556-561.


**3** 

*USA* 

*Stanford University,* 

**The Prevalence and Prognostic Value of** 

**Rest Premature Ventricular Contractions** 

This chapter discusses research conducted to evaluate the prognostic significance of premature ventricular contractions (PVCs) on a routine electrocardiogram (ECG), and to evaluate their relationship to heart rate, heart failure and sport participation. Identifying parameters to help risk stratify patients and provide prognostic implications can help identify individuals who may benefit from early diagnosis and intervention. Our discussion utilizes research from a large database of computerized 12-lead ECGs, including 45,402 members of the US Veterans Administration, of which 352 were known to have heart failure. In addition, 750 athletes were analyzed. Echocardiograms and treadmill tests were performed on all those with heart failure and echocardiograms were part of the annual

Briefly, there were 1731 patients with PVCs (3.8%) in the total veteran population. Twentynine of the 352 with heart failure exhibited a PVC (8%) and 5 of the 750 athletes exhibited a PVC (0.7%). Compared to patients without PVCs, those with PVCs had significantly higher all-cause (39% vs. 22%, p<0.001) and cardiovascular mortality (20% vs. 8%, p<0.001). PVCs remain a significant predictor even after adjustment for age and other ECG abnormalities. The presence of multiple PVCs or complex morphologies did not add significant additional prognostic information. Those patients with PVCs had a significantly higher heart rate than those without PVCs (meanSD: 78.615 vs. 73.516 bpm, p<0.001). When patients were divided into groups by heart rate (<60, 60-79, 80-99 and >100 bpm) and by the presence or absence of PVCs, mortality increased progressively with heart rate and doubled with the presence of PVCs. Using regression analysis, heart rate was demonstrated to be an

We identified 352 patients (64 ± 11 years; 7 females) with a history of clinical HF undergoing treadmill testing for clinical reasons at the VAPAHCS (1987-2007). Patients with rest PVCs were defined as having ≥1 PVC on the ECG prior to testing (n=29; 8%).During a median follow-up period of 6.2 years, there were 178 deaths of which 76 (42.6%) were due to CV causes. At baseline, compared to patients without rest PVCs, those with rest PVCs had a lower ejection fraction (EF) (30% vs. 45%) and the prevalence of EF≤35% was higher (75% vs. 41%). They were more likely to have smoked (76% vs. 55%).The all-cause and CV mortality rates were significantly higher in the rest PVCs group (72% vs. 49%, p=0.01 and 45% vs. 20%, p=0.002; respectively). After adjusting for age, beta-blocker use, rest ECG findings, resting heart rate (HR), EF, maximal systolic blood pressure, peak HR and exercise capacity,

**1. Introduction**

physical exam of the college athletes.

independent and significant predictor of PVCs.

Matthew D. Solomon and Victor Froelicher


### **The Prevalence and Prognostic Value of Rest Premature Ventricular Contractions**

Matthew D. Solomon and Victor Froelicher *Stanford University, USA* 

### **1. Introduction**

26 Advances in Electrocardiograms – Clinical Applications

[24] Marfella R, Nappo F, DeAngelis L, Siniscalchi M, Rossi F, Giugliano D. The effect of

[25] Statters DJ, Malik M, Ward DE, Camm AJ. QT Dispersion: Problems of Methodology and Clinical Significance. J Cardiovasc Electrophysiol. 1994; 5:672-85. [26] Kautzner J, Yi G, Camm AJ, Malik M. Short- and long-term reproducibility of QT, QTc,

575.

1994; 17(5 Pt 1):928-937.

acute hyperglycaemia on QTc duration in healthy man. Diabetologia. 2000; 43:571-

and QT dispersion measurement in healthy subjects. Pacing Clin Electrophysiol.

This chapter discusses research conducted to evaluate the prognostic significance of premature ventricular contractions (PVCs) on a routine electrocardiogram (ECG), and to evaluate their relationship to heart rate, heart failure and sport participation. Identifying parameters to help risk stratify patients and provide prognostic implications can help identify individuals who may benefit from early diagnosis and intervention. Our discussion utilizes research from a large database of computerized 12-lead ECGs, including 45,402 members of the US Veterans Administration, of which 352 were known to have heart failure. In addition, 750 athletes were analyzed. Echocardiograms and treadmill tests were performed on all those with heart failure and echocardiograms were part of the annual physical exam of the college athletes.

Briefly, there were 1731 patients with PVCs (3.8%) in the total veteran population. Twentynine of the 352 with heart failure exhibited a PVC (8%) and 5 of the 750 athletes exhibited a PVC (0.7%). Compared to patients without PVCs, those with PVCs had significantly higher all-cause (39% vs. 22%, p<0.001) and cardiovascular mortality (20% vs. 8%, p<0.001). PVCs remain a significant predictor even after adjustment for age and other ECG abnormalities. The presence of multiple PVCs or complex morphologies did not add significant additional prognostic information. Those patients with PVCs had a significantly higher heart rate than those without PVCs (meanSD: 78.615 vs. 73.516 bpm, p<0.001). When patients were divided into groups by heart rate (<60, 60-79, 80-99 and >100 bpm) and by the presence or absence of PVCs, mortality increased progressively with heart rate and doubled with the presence of PVCs. Using regression analysis, heart rate was demonstrated to be an independent and significant predictor of PVCs.

We identified 352 patients (64 ± 11 years; 7 females) with a history of clinical HF undergoing treadmill testing for clinical reasons at the VAPAHCS (1987-2007). Patients with rest PVCs were defined as having ≥1 PVC on the ECG prior to testing (n=29; 8%).During a median follow-up period of 6.2 years, there were 178 deaths of which 76 (42.6%) were due to CV causes. At baseline, compared to patients without rest PVCs, those with rest PVCs had a lower ejection fraction (EF) (30% vs. 45%) and the prevalence of EF≤35% was higher (75% vs. 41%). They were more likely to have smoked (76% vs. 55%).The all-cause and CV mortality rates were significantly higher in the rest PVCs group (72% vs. 49%, p=0.01 and 45% vs. 20%, p=0.002; respectively). After adjusting for age, beta-blocker use, rest ECG findings, resting heart rate (HR), EF, maximal systolic blood pressure, peak HR and exercise capacity,

The Prevalence and Prognostic Value of Rest Premature Ventricular Contractions 29

Of the ECGs originally classified as having PVCs present, 14% were found to be misclassified as having PVCs when none were present. The most common reason for these errors was misclassification of artifact or of aberrantly-conducted supraventricular beats. All the misclassified patients were reclassified into the 'PVC absent' group, leaving 1731

The 'PVC present' ECGs were then reviewed to categorize the patterns and morphologic characteristics of confirmed PVCs. These characteristics included presence of multiple PVCs on a single 10-sec ECG, presence of couplets or salvos (≥3 consecutive PVCs), presence of bigeminal or trigeminal rhythm, and presence of multiform PVC morphologies. For the purposes of this study, complex PVCs were defined as repetitive PVCs (≥2 consecutive) and multiform morphologies. Non-sustained ventricular tachycardia was rarely documented on these short recordings and the few short runs of 3 or more consecutive beats were not

Heart rate was determined by the program by counting all QRS complex templates in 10 seconds and multiplying by six. When PVCs were present, the R-R interval between the normal, dominant sinus QRS complexes (NN) was manually measured except when the pattern of PVCs made it impossible (i.e., bigeminy). This NN interval was used to calculate

An 'abnormal' ECG was defined as the presence of one or more of the following: pathologic Q waves, left or right bundle branch block, intraventricular conduction delay, Wolff-Parkinson-White syndrome, right or left ventricular hypertrophy (Romhilt-Estes), left atrial enlargement, or abnormal ST segments. All remaining ECGs were classified as 'normal'.

The Social Security Death Index and California Health Department Service were used to ascertain vital status as of 12/31/00. Cause of death was available from the latter and deaths determined by the Social Security Death Index were classified by review of the Veteran's Affairs clinical data base. All-cause death and cardiovascular death were used as endpoints. Mean follow-up was 5.5 years. Data regarding cardiac interventions or events was not

Number Crunching System SoftwareÔ (Kaysville, UT) was used for all statistical analyses after transferring the data from a Microsoft ACCESS (Redmond, WA) database. Unpaired two-tailed t tests were used for univariate comparison of variables. Cox proportional hazard analysis was performed to evaluate those with and without PVCs and those with multiple and complex PVCs. Multivariate Cox hazard function analysis was performed to demonstrate if the various PVC characteristics were independently and significantly associated with time until death after considering age and other ECG abnormalities. Analysis was repeated using data from only those patients with normal ECGs. Kaplan Meier survival analysis was again performed after patients were divided into groups by heart rate (<60, 60-79, 80-99 and >100 bpm) and by the presence or absence of PVCs. Multivariate regression analysis was performed with heart rate as the dependent variable and with the following independent variables: age, gender, abnormal/normal ECG classification, in- or outpatient status and PVCs present or not. Logistic regression was performed with PVCs as the dependent variable and with the following independent variables: age, gender, abnormal/normal ECG classification, PVCs present or absent and in- or outpatient status.

patients for analysis in the 'PVC present' group.

analyzed separately.

the intrinsic sinus rate.

**3.3 Follow-up** 

available.

**3.4 Statistical methods** 

rest PVCs was associated with a 5.5 –fold increased risk of CV mortality (p=0.004). Considering the presence of PVCs during exercise and/or recovery did not affect our results. All five athletes with at least one PVC on a routine screening ECG as part of the annual physical exams had normal echocardiograms and no clinical manifestations of heart disease.

Thus, we concluded that PVCs on a resting ECG are a significant and independent predictor of all-cause and cardiovascular mortality. Increased heart rate predicts mortality in patients with and without PVCs and the combination dramatically increases mortality. These findings together with the demonstrated independent association of heart rate with PVCs suggest that a hyper-adrenergic state is present in patients with PVCs and that it likely contributes to their adverse prognosis. In patients with heart failure the presence of PVCs on a resting ECG is associated with a significant increase in risk of CV death independent of age and exercise capacity. PVCs are rare in young athletes and do not appear to be associated with cardiac disease.

### **2. Background**

Premature ventricular contractions (PVCs) are common electrocardiographic findings in patients with and without structural heart disease. Prior evidence suggests that the presence of PVCs has prognostic value but it is unclear what underlying process they represent. A surprising observation in some studies has been an apparent association between higher mean resting heart rates and the presence of PVCs. Activation of the sympathetic nervous system is an important factor in the genesis of ventricular arrhythmias (Anderson, 2003; Podrid, Fuchs & Candinas, 1990). Enhanced automaticity, triggered activity, and reentry are all mechanisms generating rhythm abnormalities; all three mechanisms are markedly potentiated by the action of catecholamines. We sought to make use of our large database of electrocardiogram (ECG) data to confirm the prognostic significance of PVCs and analyze the interaction between heart rate and PVCs. We hypothesized that both elevated heart rate and the presence of PVCs are markers of sympathetic nervous system (SNS) activity and would, therefore, be correlated with each other and predictive of mortality. This would support the other lines of evidence suggesting that the sympathetic nervous system is active in the genesis of PVCs.

### **3. Research methods**

### **3.1 Study population**

All initial ECGs of consecutive veterans (outpatient and inpatient) who obtained ECGs for any reason at the Palo Alto VA Medical Center from April 1987 until December 1999 were considered in the study. From these 46,959 ECGs, those with atrial fibrillation and paced rhythms were excluded, leaving 45,402 for analysis.

### **3.2 Electrocardiography**

Computerized 12-lead resting 10-second ECG recordings were digitally recorded on the Marquette MAC system. Only the initial ECG was considered for patients with multiple ECGs in the database. Most of the ECG analysis was performed using the GE/Marquette ECG analysis program (www.gemedicalsystems.com). PVCs were defined as at least one QRS complex that was premature, ectopic shaped and had a QRS duration of greater than 120 ms. All ECGs classified as having PVCs were manually over-read by two cardiologists. Of the ECGs originally classified as having PVCs present, 14% were found to be misclassified as having PVCs when none were present. The most common reason for these errors was misclassification of artifact or of aberrantly-conducted supraventricular beats. All the misclassified patients were reclassified into the 'PVC absent' group, leaving 1731 patients for analysis in the 'PVC present' group.

The 'PVC present' ECGs were then reviewed to categorize the patterns and morphologic characteristics of confirmed PVCs. These characteristics included presence of multiple PVCs on a single 10-sec ECG, presence of couplets or salvos (≥3 consecutive PVCs), presence of bigeminal or trigeminal rhythm, and presence of multiform PVC morphologies. For the purposes of this study, complex PVCs were defined as repetitive PVCs (≥2 consecutive) and multiform morphologies. Non-sustained ventricular tachycardia was rarely documented on these short recordings and the few short runs of 3 or more consecutive beats were not analyzed separately.

Heart rate was determined by the program by counting all QRS complex templates in 10 seconds and multiplying by six. When PVCs were present, the R-R interval between the normal, dominant sinus QRS complexes (NN) was manually measured except when the pattern of PVCs made it impossible (i.e., bigeminy). This NN interval was used to calculate the intrinsic sinus rate.

An 'abnormal' ECG was defined as the presence of one or more of the following: pathologic Q waves, left or right bundle branch block, intraventricular conduction delay, Wolff-Parkinson-White syndrome, right or left ventricular hypertrophy (Romhilt-Estes), left atrial enlargement, or abnormal ST segments. All remaining ECGs were classified as 'normal'.

### **3.3 Follow-up**

28 Advances in Electrocardiograms – Clinical Applications

rest PVCs was associated with a 5.5 –fold increased risk of CV mortality (p=0.004). Considering the presence of PVCs during exercise and/or recovery did not affect our results. All five athletes with at least one PVC on a routine screening ECG as part of the annual physical exams had normal echocardiograms and no clinical manifestations of heart disease. Thus, we concluded that PVCs on a resting ECG are a significant and independent predictor of all-cause and cardiovascular mortality. Increased heart rate predicts mortality in patients with and without PVCs and the combination dramatically increases mortality. These findings together with the demonstrated independent association of heart rate with PVCs suggest that a hyper-adrenergic state is present in patients with PVCs and that it likely contributes to their adverse prognosis. In patients with heart failure the presence of PVCs on a resting ECG is associated with a significant increase in risk of CV death independent of age and exercise capacity. PVCs are rare in young athletes and do not appear to be

Premature ventricular contractions (PVCs) are common electrocardiographic findings in patients with and without structural heart disease. Prior evidence suggests that the presence of PVCs has prognostic value but it is unclear what underlying process they represent. A surprising observation in some studies has been an apparent association between higher mean resting heart rates and the presence of PVCs. Activation of the sympathetic nervous system is an important factor in the genesis of ventricular arrhythmias (Anderson, 2003; Podrid, Fuchs & Candinas, 1990). Enhanced automaticity, triggered activity, and reentry are all mechanisms generating rhythm abnormalities; all three mechanisms are markedly potentiated by the action of catecholamines. We sought to make use of our large database of electrocardiogram (ECG) data to confirm the prognostic significance of PVCs and analyze the interaction between heart rate and PVCs. We hypothesized that both elevated heart rate and the presence of PVCs are markers of sympathetic nervous system (SNS) activity and would, therefore, be correlated with each other and predictive of mortality. This would support the other lines of evidence suggesting that the sympathetic nervous system is active

All initial ECGs of consecutive veterans (outpatient and inpatient) who obtained ECGs for any reason at the Palo Alto VA Medical Center from April 1987 until December 1999 were considered in the study. From these 46,959 ECGs, those with atrial fibrillation and paced

Computerized 12-lead resting 10-second ECG recordings were digitally recorded on the Marquette MAC system. Only the initial ECG was considered for patients with multiple ECGs in the database. Most of the ECG analysis was performed using the GE/Marquette ECG analysis program (www.gemedicalsystems.com). PVCs were defined as at least one QRS complex that was premature, ectopic shaped and had a QRS duration of greater than 120 ms. All ECGs classified as having PVCs were manually over-read by two cardiologists.

associated with cardiac disease.

**2. Background** 

in the genesis of PVCs.

**3. Research methods 3.1 Study population** 

**3.2 Electrocardiography** 

rhythms were excluded, leaving 45,402 for analysis.

The Social Security Death Index and California Health Department Service were used to ascertain vital status as of 12/31/00. Cause of death was available from the latter and deaths determined by the Social Security Death Index were classified by review of the Veteran's Affairs clinical data base. All-cause death and cardiovascular death were used as endpoints. Mean follow-up was 5.5 years. Data regarding cardiac interventions or events was not available.

### **3.4 Statistical methods**

Number Crunching System SoftwareÔ (Kaysville, UT) was used for all statistical analyses after transferring the data from a Microsoft ACCESS (Redmond, WA) database. Unpaired two-tailed t tests were used for univariate comparison of variables. Cox proportional hazard analysis was performed to evaluate those with and without PVCs and those with multiple and complex PVCs. Multivariate Cox hazard function analysis was performed to demonstrate if the various PVC characteristics were independently and significantly associated with time until death after considering age and other ECG abnormalities. Analysis was repeated using data from only those patients with normal ECGs. Kaplan Meier survival analysis was again performed after patients were divided into groups by heart rate (<60, 60-79, 80-99 and >100 bpm) and by the presence or absence of PVCs. Multivariate regression analysis was performed with heart rate as the dependent variable and with the following independent variables: age, gender, abnormal/normal ECG classification, in- or outpatient status and PVCs present or not. Logistic regression was performed with PVCs as the dependent variable and with the following independent variables: age, gender, abnormal/normal ECG classification, PVCs present or absent and in- or outpatient status.

The Prevalence and Prognostic Value of Rest Premature Ventricular Contractions 31

Those patients with PVCs had a significantly higher heart rate than those without PVCs (mean SD: 78.6 15 vs. 73.5 16 bpm, P<0.001). This heart rate difference persisted when patients were sub-grouped by gender, race, outpatient vs. inpatient status and those with otherwise normal ECGs (i.e., least likely to have structural heart disease). Those patients with PVCs always had a significantly higher mean heart rate. Further analysis of heart rate differences showed that women had a significantly lower mean HR than men (71.1 ± 13.7 vs 74.0 ± 16.2, p<0.001), outpatients had a lower mean heart rate than inpatients (72.5 ± 15.0 vs 76.9 ± 17.7, p<0.001), and those with an otherwise normal ECG had a lower heart rate than

**P- Value** 

Age 65±12 66±11 0.05 65±12 68±10 <0.001

Height 69±3 69±3 0.26 69±3 70±3 0.30

Weight 186±39 186±39 >0.9 186±39 184±37 0.52

BMI 27±6 27±6 0.4 27±6 27±6 0.36

Outpatients 68% 68% 0.89 68% 67% 0.68

Heart rate 77±16 81±15 <0.001 79±16 84±14 <0.001

Q Wave 25% 23% 0.32 23% 31% 0.007

LVH 8 % 9% 0.59 8% 11% 0.18

RVH 0.3% 0.5% 0.60 0.3% 1.5% 0.009

RBBB 7% 7% 0.81 7% 7% 0.85

LBBB 3% 3% 0.77 3% 3% 0.81

LAE 10% 11% 0.81 10.5% 10.6% >0.9

Abnormal ECG 48% 47% 0.72 47% 54% 0.04

Table II. Demographics and ECG findings in patients with single versus multiple and non-

Age, height (inches), weight (pounds), BMI and heart rate are presented as the mean ± SEM. BMI = body mass index; ECG = electrocardiogram; LAE = left atrial enlargement; LBBB = left bundle branch block; LVH = left ventricular hypertrophy; PVC = premature ventricular contraction; RBBB =

right bundle branch block; RVH = right ventricular hypertrophy.

complex versus complex PVCs.

**Noncomplex PVCs N=1527** 

**Complex PVCs N=199** 

**P- Value** 

those with an abnormal ECG (73.2 ± 15.6 vs 75.2 ± 16.8, p<0.001).

**Multiple PVCs N=815** 

**Single PVCs N=911** 

**Variable** 

Demographics

ECG Findings

### **4. Results**

After exclusion of patients with atrial fibrillation and paced rhythms, there were 43,671 patients without PVCs and 1731 patients with PVCs (3.8%). Demographic and ECG characteristics of the groups are shown in Table I. The group with PVCs was older than those without PVCs (mean age SD: 65 12 vs. 56 15, P<0.001). As this was a VA study, the patients are 90% male but there was a slightly higher percentage of men among those with PVCs (90% vs. 94%, p=0.01). There were no significant differences between height, weight or BMI. Patients with PVCs had a significantly higher prevalence of Q waves, LVH, LAE, bundle branch blocks and ECGs classified as abnormal. Of the ECG characteristics evaluated, only RVH was similar between the groups.


Age, height (inches), weight (pounds), BMI and heart rate are presented as the mean ± SEM. BMI = body mass index; ECG = electrocardiogram; LAE = left atrial enlargement; LBBB = left bundle branch block; LVH = left ventricular hypertrophy; PVC = premature ventricular contraction; RBBB = right bundle branch block; RVH = right ventricular hypertrophy

Table I. Demographics and ECG findings in patients with and without PVCs.

After exclusion of patients with atrial fibrillation and paced rhythms, there were 43,671 patients without PVCs and 1731 patients with PVCs (3.8%). Demographic and ECG characteristics of the groups are shown in Table I. The group with PVCs was older than those without PVCs (mean age SD: 65 12 vs. 56 15, P<0.001). As this was a VA study, the patients are 90% male but there was a slightly higher percentage of men among those with PVCs (90% vs. 94%, p=0.01). There were no significant differences between height, weight or BMI. Patients with PVCs had a significantly higher prevalence of Q waves, LVH, LAE, bundle branch blocks and ECGs classified as abnormal. Of the ECG characteristics

> **No PVCs N=43,671**

Age 56±15 56±15 65±12 <0.001

Males 90.0% 90% 94% 0.01

Height 69±4 69±4 69±4 0.3

Weight 182 ± 40 182 ± 40 184 ± 41 0.1

BMI 27±6 27±6 27±6 0.4

Outpatients 73% 73% 70% 0.004

Heart rate 73.7 ± 16 73.5 ± 16 78.6 ± 15 <0.001

Q Wave 13% 11% 23% <0.001

LVH 5% 5% 8 % <0.001

RVH 0.3% 0.3% 0.3% 0.6

RBBB 4% 3% 7% <0.001

LBBB 1% 1% 3% <0.001

LAE 4% 3.8% 10.7% <0.001

Abnormal ECG 24% 23% 43% <0.001

Age, height (inches), weight (pounds), BMI and heart rate are presented as the mean ± SEM. BMI = body mass index; ECG = electrocardiogram; LAE = left atrial enlargement; LBBB = left bundle branch block; LVH = left ventricular hypertrophy; PVC = premature ventricular contraction; RBBB =

Table I. Demographics and ECG findings in patients with and without PVCs.

right bundle branch block; RVH = right ventricular hypertrophy

**PVCs** 

**N=1,731 P-value** 

evaluated, only RVH was similar between the groups.

**N=45,402** 

**Variable Total** 

Demographics

ECG Findings

**4. Results** 

Those patients with PVCs had a significantly higher heart rate than those without PVCs (mean SD: 78.6 15 vs. 73.5 16 bpm, P<0.001). This heart rate difference persisted when patients were sub-grouped by gender, race, outpatient vs. inpatient status and those with otherwise normal ECGs (i.e., least likely to have structural heart disease). Those patients with PVCs always had a significantly higher mean heart rate. Further analysis of heart rate differences showed that women had a significantly lower mean HR than men (71.1 ± 13.7 vs 74.0 ± 16.2, p<0.001), outpatients had a lower mean heart rate than inpatients (72.5 ± 15.0 vs 76.9 ± 17.7, p<0.001), and those with an otherwise normal ECG had a lower heart rate than those with an abnormal ECG (73.2 ± 15.6 vs 75.2 ± 16.8, p<0.001).


Age, height (inches), weight (pounds), BMI and heart rate are presented as the mean ± SEM. BMI = body mass index; ECG = electrocardiogram; LAE = left atrial enlargement; LBBB = left bundle branch block; LVH = left ventricular hypertrophy; PVC = premature ventricular contraction; RBBB = right bundle branch block; RVH = right ventricular hypertrophy.

Table II. Demographics and ECG findings in patients with single versus multiple and noncomplex versus complex PVCs.

The Prevalence and Prognostic Value of Rest Premature Ventricular Contractions 33

event free survival between groups is also clearly demonstrated with Kaplan-Meier cumulative survival curve (Figure 2). The PVC and heart rate stratifications performed similarly when patients were divided into those with normal and abnormal ECGs. Cox regression analysis (controlled for age, gender, outpatient vs. inpatient status and normal vs. abnormal ECG) demonstrates that PVCs (RR 1.61, 95% CI 1.44-1.80, p<0.001) and heart rate (RR 1.02 for each 1 bpm increase, 95% CI 1.01-1.02, p<0.001) are independent predictors of

> **Heart Rate 60-79**
