Modern approach towards surgical treatment of coronary heart disease in diabetic patientsIn the recent years there has been registered an increase in diabetes mellitus (DM)

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.

Aim      To develop an algorithm for using ultrasonic flowmetry (USF) and epicardial ultrasonic scanning (EpiUSS) for intraoperative assessment of anatomic and functional viability of conduits.Material and methods  For viability assessment of 460 coronary grafts in 150 patients who were operated at the Bakulev National Medical Research Center for Cardiovascular Surgery (2018–2021 г.), markers of graft failure were analyzed using the USF and EpiUSS data confirmed by results of graft angiography. According to RОС analysis, the Qmean and PI values indicative of the graft failure were determined. A CHAID decision tree was developed for assessing the prognostic significance of the analyzed parameters. Based on this prognostic model, an algorithm was developed for intraoperative diagnosis of anatomic and functional graft viability during coronary bypass surgery.Results The Qmean ≤20.5 ml/min values were associated with an increased relative risk (RR) of detecting graft failure (RR, 8.2; 95 % confidence interval, CI, 4.4–15.2). The developed model shows a high accuracy of predicting the graft failure (AUC = 0.906±0.03). The RR of graft failure at PI ≥2.65 was 3.3 (95 % CI, 2.17–5.08). The prognostic model for PI (AUC = 0.745±0.042) was sufficiently accurate with respect of possible graft failure. Nodes of high and low risk for graft failure were determined in the developed decision tree. The obtained model was characterized by high sensitivity and specificity (100 and 84.3 %, respectively).Conclusion      The combined use of USF and EpiUSS allows a highly accurate assessment of both morphological and functional characteristics of graft flow. The developed algorithm for the intraoperative diagnosis of anatomic and functional graft viability can be recommended for clinical use.

Coronary artery bypass graft (CABG) using short-scar incision (without median sternotomy) allows minimizing the invasiveness of the intervention, reducing the risks of postoperative complications, and also ensuring patient comfort and quick social and physical rehabilitation. The successful implementation of such operations is due not only to surgical skills and the integration of technological achievements into practice, but also to the appropriate selection of patients. The article presents a clinical case of successful re-operation of the subclavian-coronary artery bypass grafting on a beating heart using antero-lateral thoracotomy approach in a patient with angina relapse after CABG.