Цель. Оценка влияния ожирения, рассчитанного по индексу массы тела (ИМТ), на госпитальные результаты и трехлетнюю выживаемость после операции коронарного шунтирования (КШ) у больных ишемической болезнью сердца (ИБС).

Aim. To assess the long-term all-cause mortality after percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) in patients with stable coronary artery disease (CAD) under various clinical and anatomical scenarios.Material and methods. This single-center cohort retrospective study assessed the outcomes of CABG and PCI with implantation of second-generation drugeluting stents in 4177 patients with stable CAD. Inhospital, 30-day and remote 5-year all-cause mortality (mean follow-up period — 38 months) was assessed. Also, the influence of the initial severity of CAD, the presence/absence of diabetes, myocardial contractility on remote all-cause mortality after myocardial revascularization was assessed.Results. Inhospital and 30-day risks of death in patients who underwent PCI and CABG, after comparing the initial clinical characteristics, did not differ significantly. In the long-term follow-up period, PCI compared with CABG was associated with an increased all-cause mortality in the main groups (PCI vs CABG: odds ratio (OR) 1,84, 95% confidence interval (CI) 1,30-2,62, p<0,001), as well as in the following subgroups: (1) in patients with multivessel CAD (OR 1,77, 95% CI 1,19-2,64, p=0,005), (2) in patients with left main CAD >50% (OR 5,04, 95% CI 1,72-14,76, p=0,003), but not in patients with single-vessel disease (OR 2,084, 95% CI 0,9964,361, p=0,051). Diabetes in the main study groups did not affect the difference in mortality as follows: CABG had an advantage over PCI regardless of diabetes. However, CABG in patients with multivessel disease and diabetes, in contrast to patients without diabetes, led to a significant decrease in all-cause death risk (OR 2,29, 95% CI 1,173-4,47, p=0,015). Also, PCI compared with CABG was accompanied by an increase in the 5-year death risk in patients with an not reduced left ventricular ejection fraction (LVEF) >40% (OR 1,74, 95% CI 1,205-2,536, p=0,003), but not in patients with LVEF <40% (95% CI 1,314-4,709, p=0,809).Conclusion. The obtained data indicate a significant reduction in the 5-year risk of all-cause mortality in patients undergoing CABG compared to patients after PCI. Potential long-term benefit from CABG compared to PCI may be obtained in patients with complex coronary artery involvement (left main coronary artery stenosis >50%, lesion of two or more coronary arteries), with concomitant diabetes in multivessel disease, and patients with non-reduced LVEF (>40%).

Цель. Разработка и оценка эффективности моделей машинного обучения в прогнозировании внутригоспитального летального исхода после операции коронарного шунтирования (КШ) в сравнении со шкалой риска EuroSCORE II.

Цель – оценка влияния почечной дисфункции (снижение расчетной скорости клубочковой фильтрации (рСКФ) менее 60 мл/мин/1,73 м2) на риск развития госпитальных осложнений после операции коронарного шунтирования (КШ) в зависимости от проведения искусственного кровообращения (ИК) у больных со стабильной ишемической болезнью сердца (ИБС).

Relevance. The desire to improve and optimize the results of surgical treatment of coronary heart disease (CHD), along with the observed integration of artificial intelligence methods into healthcare, creates prerequisites for exploring the possibilities of machine learning meth Relevance. The desire to improve and optimize the results of surgical treatment of coronary heart disease (CHD), along with the observed integration of artificial intelligence methods into healthcare, creates prerequisites for exploring the possibilities of machine learning methods for predicting adverse outcomes after cardiac surgery. The purpose of our study was to evaluate and compare the accuracy of predicting death after CABG surgery using machine learning methods and the recommended cardiac risk assessment scale EuroSCORE 2. Materials and methods . Based on the analysis of depersonalized medical data on the outcomes of coronary artery bypass surgery in 2,826 patients with coronary artery disease (survivors — 2,785, deceased — 41), using machine learning methods (logistic regression (LR), LightGBM, XGBoost, CatBoost, boosting model), prognostic models were developed that assess the risk of intrahospital death after intervention. The forecasting efficiency of the obtained models was compared with the forecasting results of the EuroSCORE 2 scale. To evaluate the performance of the models, the metrics recommended for the analysis of unbalanced data were used: precision, recall, specificity, F1-measure, ROC-AUC. Results. The model developed with the help of LR had the maximum recall (0.88), but at the same time significantly overestimated the risk of death (precision — 0.03). F1-measure for the LR model was 0.06, ROC AUC — 0.77. Gradient boosting models (LightGBM, XGBoost, CatBoost), in comparison with LR, had higher indicators of precision, recall, specificity, F1-measures and AUC. At the same time, the best quality metrics were observed in the boosting model (BM), which combined LR and gradient boosting models. BM performance indicators: precision — 0.67, recall – 0,50, F1-measure — 0.57, specificity — 1.0, ROC-AUC — 0.85. The EuroSCORE 2 risk model showed extremely low efficiency in predicting death in the study sample: precision — 0.143, recall — 0.125, F1-measure — 0.133, specificity — 0.97, ROC-AUC — 0.47. Conclusion. Machine learning (ML) methods are promising in predictive analytics in cardiac surgery. In our study, predictive models based on ML showed an advantage in the accuracy of calculating the risk of hospital death after CABG in comparison with the classic EuroSCORE 2 model. To obtain an optimal risk model adapted to the conditions of application in the Russian Federation, largescale multicenter studies are needed.

Aim. To develop and evaluate the effectiveness of models for predicting mortality after coronary bypass surgery, obtained using machine learning analysis of preoperative data.Material and methods. As part of a cohort study, a retrospective prediction of in-hospital mortality after coronary artery bypass grafting (CABG) was performed in 2182 patients with stable coronary artery disease. Patients were divided into 2 following samples: learning (80%, n=1745) and training (20%, n=437). The initial ratio of surviving (n=2153) and deceased (n=29) patients in the total sample indicated a pronounced class imbalance, and therefore the resampling method was used in the training sample. Five machine learning (ML) algorithms were used to build predictive risk models: Logistic regression, Random Forrest, CatBoost, LightGBM, XGBoost. For each of these algorithms, cross-validation and hyperparameter search were performed on the training sample. As a result, five predictive models with the best parameters were obtained. The resulting predictive models were applied to the learning sample, after which their performance was compared in order to determine the most effective model.Results. Predictive models implemented on ensemble classifiers (CatBoost, LightGBM, XGBoost) showed better results compared to models based on logistic regression and random forest. The best quality metrics were obtained for CatBoost and LightGBM based models (Precision — 0,667, Recall — 0,333, F1-score — 0,444, ROC AUC — 0,666 for both models). There were following common high-ranking parameters for deciding on the outcome for both models: creatinine and blood glucose levels, left ventricular ejection fraction, age, critical stenosis (>70%) of carotid arteries and main lower limb arteries.Conclusion. Ensemble machine learning methods demonstrate higher predictive power compared to traditional methods such as logistic regression. The prognostic models obtained in the study for preoperative prediction of in-hospital mortality in patients referred for CABG can serve as a basis for developing systems to support medical decision-making in patients with coronary artery disease.