**Revealing of Initial Factors Defining Results of Operation in Patients with Aortic Valve Replacement and Coronary Artery Disease**

A. M. Karaskov1, F. F. Turaev2 and S. I. Jheleznev1

*1E.N. Meshalkin Novosibirsk State Research Institute of Circulation Pathology, Novosibirsk, 2V. Vakhidov Republican Specialized Center for Surgery, Tashkent, 1Russia 2Uzbekistan* 

### **1. Introduction**

18 Aortic Valve Surgery

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Echocardiography, pathophysiology, and the continuing evolution of surgery for

Moderate aortic valve stenosis is a common condition in patients with coronary heart disease (Gullinov and Garsia, 2005). Recent studies have shown that progression of aortic valve stenosis depends on the degree of valvular leaflets calcification; that aortic valve replacement does not increase mortality after coronary artery bypass grafting (CABG); moreover,valve replacement performed after CABG leads to decreased mortality, it was especially confirmed in patients with severe aortic valve stenosis. However, review of the literature concerning integration of the mathematical approaches in medicine has demonstrated that, the simple prognosis is more significant than an evaluation based on organ and system modeling for choice of treatment method and options for patients with such combined pathology. Repeated intervention is one of the most significant prognostic factors. Thus, after analyzing of 13,346 CABG cases Yap et al (2007) have shown that mortality of repeated interventions is approximately 3 times higher than that of primary interventions (4.8% and 1.8%, respectively). Patient's age is another such a factor. Urso et al. (2007) have established that one-year survival after aortic valve replacement in patients aged over 80 years (86,1%) is significantly less than that in the younger group. Analyzing of 1567 patients after valve replacement combined with CABG, Doenst et al.(2006) have demonstrated patients' gender influence on surgery outcomes, postoperatively women had higher stroke possibility (risk index was 1.52). We believe that various influences of parameters characterizing patient's baseline status on surgery outcome require more complex multivariate statistical analysis to be used. It allows defining rational number of the most significant factors determining the surgery prognosis related both to baseline status of patients with heart defects and immediate postoperative complications caused by interventional injury and heart hemodynamic changes (1, 2, 3, 4, 5, 6). Moreover, one of the authors of the article (Wann and Balkhy, 2009) considers that application of the most modern diagnostics tests (i.e. computed tomography coronary angiography) allows predicting an outcome of the scheduled surgery more accurately.

Revealing of Initial Factors Defining Results of Operation in

M = <sup>1</sup>

**3. Results and discussion** 

efficacy (see Table 2).

*N* <sup>1</sup> *n*

*i*=

Distribution parameters were evaluated by formulas as follows:

∑ Xi ; S =

researches. Prognosis model is based on the regression analysis.

LV ejection fraction after a surgery with prognostic significance 75-90%.

Patients with Aortic Valve Replacement and Coronary Artery Disease 21

1 1 *n*

1

Consistency of numerical data with normal distribution law was assessed with Kolmogorov test. If the numerical data did not correspond to normal distribution law, non-parametric statistical methods were used - Wilcoxon rank test. Power and direction of correlation between the signs were determined by Pearson correlation coefficient **(r)** and Spearman rank correlation, if distribution of the baseline data was not normal. The values of these tests range from -1 to +1. The extreme values are observed in signs associated with linear functional relation. The significance of selected correlation coefficient is assessed by statistics value r\* *n* − 2 / 1 2 − *r* = ta,f (1). Expression (1) permits to determine a, i.e. possibility of correlation coefficient difference from zero depending on r and sample size n. This, in turn, allows comparing the correlation of the same signs in the different sample sizes by possibility. Correlation power was assessed by a value of the correlation coefficient: strong, if r ≥0.7, moderate, if r = 0.3-0.7, weak, if r<0.3. The differences between compared values were significant if р<0.5, it is consistent with criteria accepted in medical and biological

Regression analysis was directed to the test of significance of one (dependent) variable Y from set of other ones, so called independent variables Xj = {X1, X2, … Xp}. The values of the prognostic parameter are defined as a result of determination of the risk factors based on analysis of the clinical materials. The purpose of linear regression analysis in this study was to predict the values of the resulted variable Y using the known values of physical parameters, EchoCG parameters and various additional features related to surgery specificity. Parameter of favorable surgery outcome was calculated as an arithmetic mean of risk factors. As a result of these calculations, the model was developed. Based on this model the program was created in "Excel–2000»: «Program for outcome prognosis of aortic valve replacement combined with coronary heart disease» (CERTIFICATE SPD RUz № DGU 01380») allowing to calculate a percentage of favorable surgery outcome and dynamics of

As a result of the performed analysis the variables pooled in factor groups **(F)** affecting the surgery prognosis were determined: **F1** – blood supply disturbance (HF, NYHA FC), **F2** – physical parameters (gender, age\*, weight\*, height\*, body surface area\*, Ketle index\*, CTI\*), **F3** – hemodynamic parameters (SBP\*, DBP\*, MBP\*, BSV, HR\*, BMV\*, TPR\*, SPR,HI\*, LV stroke work\*), **F4** – heart parameters (EDD\*,ESD\*, EDV\*, ESV\*, SV\*, EF\*, FS\*, RF\*, SVE\*, RV\*,LA\*, RA\*, PA\*), **F5** – myocardial parameters (IVS\*,LVPW\*, LVMM\*, sPLVWT and dPLVWT\*, 2HD\*), **F6** –valve morphology (calcification degree on AV, regurgitation degree on AV, MV, and TV), **F7** – valve parameters (FA and ascending aorta diameter\*, AV gradients\*, АО\* surface, МО\* surface, MV gradients\*,Еmv, Аmv, Е/А mv), **F8** – coronary blood supply parameters (blood supply type, percentage of coronary artery occlusion (LAD, DB, CA, RCA), number of planned bypass grafting). Indexed parameters, reverse values and second degree were considered in «\*» variables, it has been leading to increase in prognosis

*<sup>N</sup> <sup>i</sup>*<sup>=</sup> <sup>−</sup> <sup>∑</sup> (Xi-M)2; m = M *<sup>S</sup>*

*N*

The objective of this study was to investigate factors affecting the outcomes of combined interventions performed in patients with aortic valve defects and coronary artery lesions and to evaluate anatomical and hemodynamic parameters influencing the prognosis.

## **2. Material and methods of the study**

One hundred twenty eight (128) patients who underwent one-step aortic valve replacement and CABG were enrolled in the study (104 men and 24 women aged from 40 to 73, mean age was 56.4±1.5 years). Aortic valve stenosis was predominant in 82.8% (106) cases; aortic insufficiency was predominant in 17.2% (22) cases. Aortic valve lesions were caused by rheumatic process (65.6%), atherosclerotic degeneration and calcification (15.6%), and infective endocarditis (18.8%). All patients underwent examination including chest X-ray, ECG, EchoCG. Increase in cardiothoracic index and change in pulmonary circulation were observed on X-ray scans. Enlargement of ascending aorta was revealed in all patients. Left ventricle hypertrophy and intraventricular conduction disturbance were observed on ECG. Aortic valve defect was complicated by valvular and extravalvular calcification in 87.1% patients: 3.2% - Grade I, 22.6% -Grade II, 32.3% - Grade III, 29% - Grade IV, absolutely, it was a complicating factor for surgery. Table 1 presents the distribution of patients by chronic heart failure (CHF) and New York Heart Association Functional Class (NYHA FC).


Table 1. Distribution by chronic heath failure stage and functional class

All patients were operated using cardiopulmonary bypass and cardioplegia. Mean time of cardiopulmonary bypass was 178.5±7.8 min, time of aortic occlusion was 132.8±5.0 min. One hundred eight (108) mechanical (75 bicuspid, 33 unicuspid) and 20 biological prostheses were implanted. The most common aortic valve prostheses were MEDINZH, SorinBicarbon, EMIKS, KEM-AV-MONO, KEM-AV -COMPOZIT.

All patients who had significant coronary artery lesions (stenosis >50%) underwent coronary artery bypass grafting: one artery – in 56 (43.8%) patients, two arteries – in 42 (32.8%) patients, three arteries – in 30 (23.4%) patients. Concomitant mitral and tricuspid insufficiency was corrected in 25 and 23 patients, respectively. Atrioventricular valve insufficiency was in all cases caused by fibrous annulus dilatation, which was treated with support ring implantation. Patient status at baseline was a landmark to determine all totality of defect pathogenetic disorders, and evaluation of the factors affecting the separate components of complete clinical picture creation permitted to consider specially the causes, conditions and consequences of systemic positions. Calculations were performed using «STATISTICA for Windows», v.6.0 and original programs developed in "Excel - 2000" on "Visual Basic for Application" integrated computer language. Group data were divided into numeral and classification ones; additional tables for deviations (abs. and %) of variables from baseline levels were calculated. Difference significance was evaluated by χ2 criterion and 2x2 tables by adjusted Fisher test.

The objective of this study was to investigate factors affecting the outcomes of combined interventions performed in patients with aortic valve defects and coronary artery lesions

One hundred twenty eight (128) patients who underwent one-step aortic valve replacement and CABG were enrolled in the study (104 men and 24 women aged from 40 to 73, mean age was 56.4±1.5 years). Aortic valve stenosis was predominant in 82.8% (106) cases; aortic insufficiency was predominant in 17.2% (22) cases. Aortic valve lesions were caused by rheumatic process (65.6%), atherosclerotic degeneration and calcification (15.6%), and infective endocarditis (18.8%). All patients underwent examination including chest X-ray, ECG, EchoCG. Increase in cardiothoracic index and change in pulmonary circulation were observed on X-ray scans. Enlargement of ascending aorta was revealed in all patients. Left ventricle hypertrophy and intraventricular conduction disturbance were observed on ECG. Aortic valve defect was complicated by valvular and extravalvular calcification in 87.1% patients: 3.2% - Grade I, 22.6% -Grade II, 32.3% - Grade III, 29% - Grade IV, absolutely, it was a complicating factor for surgery. Table 1 presents the distribution of patients by chronic heart failure (CHF) and New York Heart Association Functional Class (NYHA FC).

NYHA Functional Class Number of patients HF Number of patients

All patients were operated using cardiopulmonary bypass and cardioplegia. Mean time of cardiopulmonary bypass was 178.5±7.8 min, time of aortic occlusion was 132.8±5.0 min. One hundred eight (108) mechanical (75 bicuspid, 33 unicuspid) and 20 biological prostheses were implanted. The most common aortic valve prostheses were MEDINZH, SorinBicarbon,

All patients who had significant coronary artery lesions (stenosis >50%) underwent coronary artery bypass grafting: one artery – in 56 (43.8%) patients, two arteries – in 42 (32.8%) patients, three arteries – in 30 (23.4%) patients. Concomitant mitral and tricuspid insufficiency was corrected in 25 and 23 patients, respectively. Atrioventricular valve insufficiency was in all cases caused by fibrous annulus dilatation, which was treated with support ring implantation. Patient status at baseline was a landmark to determine all totality of defect pathogenetic disorders, and evaluation of the factors affecting the separate components of complete clinical picture creation permitted to consider specially the causes, conditions and consequences of systemic positions. Calculations were performed using «STATISTICA for Windows», v.6.0 and original programs developed in "Excel - 2000" on "Visual Basic for Application" integrated computer language. Group data were divided into numeral and classification ones; additional tables for deviations (abs. and %) of variables from baseline levels were calculated. Difference significance was evaluated by χ2 criterion

IV 29 (22.6%) Table 1. Distribution by chronic heath failure stage and functional class

EMIKS, KEM-AV-MONO, KEM-AV -COMPOZIT.

and 2x2 tables by adjusted Fisher test.

II 21 (16.1%) IIA 88 (68.7%) III 78 (61.3%) IIБ 40 (31.3%)

and to evaluate anatomical and hemodynamic parameters influencing the prognosis.

**2. Material and methods of the study** 

Distribution parameters were evaluated by formulas as follows:

$$\text{NM} = \frac{1}{N} \sum\_{i=1}^{n} \quad \text{Xi}; \qquad \text{S} = \sqrt{\frac{1}{N-1}} \sum\_{i=1}^{n} \quad \text{(Xi-M)} \\ \text{2;} \qquad \text{m} = \text{M} \ \frac{\text{S}}{\sqrt{N}}$$

Consistency of numerical data with normal distribution law was assessed with Kolmogorov test. If the numerical data did not correspond to normal distribution law, non-parametric statistical methods were used - Wilcoxon rank test. Power and direction of correlation between the signs were determined by Pearson correlation coefficient **(r)** and Spearman rank correlation, if distribution of the baseline data was not normal. The values of these tests range from -1 to +1. The extreme values are observed in signs associated with linear functional relation. The significance of selected correlation coefficient is assessed by statistics value r\* *n* − 2 / 1 2 − *r* = ta,f (1). Expression (1) permits to determine a, i.e. possibility of correlation coefficient difference from zero depending on r and sample size n. This, in turn, allows comparing the correlation of the same signs in the different sample sizes by possibility. Correlation power was assessed by a value of the correlation coefficient: strong, if r ≥0.7, moderate, if r = 0.3-0.7, weak, if r<0.3. The differences between compared values were significant if р<0.5, it is consistent with criteria accepted in medical and biological researches. Prognosis model is based on the regression analysis.

Regression analysis was directed to the test of significance of one (dependent) variable Y from set of other ones, so called independent variables Xj = {X1, X2, … Xp}. The values of the prognostic parameter are defined as a result of determination of the risk factors based on analysis of the clinical materials. The purpose of linear regression analysis in this study was to predict the values of the resulted variable Y using the known values of physical parameters, EchoCG parameters and various additional features related to surgery specificity. Parameter of favorable surgery outcome was calculated as an arithmetic mean of risk factors. As a result of these calculations, the model was developed. Based on this model the program was created in "Excel–2000»: «Program for outcome prognosis of aortic valve replacement combined with coronary heart disease» (CERTIFICATE SPD RUz № DGU 01380») allowing to calculate a percentage of favorable surgery outcome and dynamics of LV ejection fraction after a surgery with prognostic significance 75-90%.
