Background As a major health hazard, the incidence of coronary heart disease has been increasing year by year. Although coronary revascularization, mainly percutaneous coronary intervention, has played an important role in the treatment of coronary heart disease, major adverse cardiovascular events (MACE) such as recurrent or persistent angina pectoris after coronary revascularization remain a very difficult problem in clinical practice. Objective Given the high probability of MACE after coronary revascularization, the aim of this study was to develop and validate a predictive model for MACE occurrence within 6 months based on machine learning algorithms. Methods A retrospective study was performed including 1004 patients who had undergone coronary revascularization at The People’s Hospital of Liaoning Province and Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from June 2019 to December 2020. According to the characteristics of available data, an oversampling strategy was adopted for initial preprocessing. We then employed six machine learning algorithms, including decision tree, random forest, logistic regression, naïve Bayes, support vector machine, and extreme gradient boosting (XGBoost), to develop prediction models for MACE depending on clinical information and 6-month follow-up information. Among all samples, 70% were randomly selected for training and the remaining 30% were used for model validation. Model performance was assessed based on accuracy, precision, recall, F1-score, confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), and visualization of the ROC curve. Results Univariate analysis showed that 21 patient characteristic variables were statistically significant (P<.05) between the groups without and with MACE. Coupled with these significant factors, among the six machine learning algorithms, XGBoost stood out with an accuracy of 0.7788, precision of 0.8058, recall of 0.7345, F1-score of 0.7685, and AUC of 0.8599. Further exploration of the models to identify factors affecting the occurrence of MACE revealed that use of anticoagulant drugs and course of the disease consistently ranked in the top two predictive factors in three developed models. Conclusions The machine learning risk models constructed in this study can achieve acceptable performance of MACE prediction, with XGBoost performing the best, providing a valuable reference for pointed intervention and clinical decision-making in MACE prevention.

Purpose To observe the effect of cardiac rehabilitation (CR) in patients with partial revascularization performed on multiple coronary artery lesions and explore its possible mechanism. Patients and Methods A total of 400 patients with multiple coronary artery lesions were enrolled and randomly divided into a complete revascularization group and a CR group, with 200 cases in each group. Target lesion revascularization was performed radically in the complete revascularization group, while it was partially completed in the CR group, and postoperative CR was performed. All the patients were put under conventional treatment. Left ventricular end diastolic dimension (LVEDD), left ventricular ejection fraction (LVEF), 6-minute walking distance (6-MWD), quality-of-life scores, safety and levels of serum nitric oxide (NO), nitric oxide synthase (NOS), superoxide dismutase (SOD), and vascular endothelial growth factor (VEGF) were evaluated and compared between two groups before and after training. Results There was no significant difference in LVEDD, LVEF, 6-MWD, quality-of-life scores, levels of serum NO, NOS, SOD, and VEGF between two groups before training ( p >0.05). 1 year later, compared with the complete revascularization group, the occurrence of major adverse events in the CR group declined ( p >0.05); the measurements of LVEDD decreased and LVEF increased ( p >0.05), 6-MWD increased significantly ( p <0.05), quality-of-life scores were higher ( p <0.05), the levels of serum NO, NOS, and SOD increased noticeably, and the levels of serum VEGF decreased significantly in the CR group ( p <0.05). There were significant differences within the same group, before and after training ( p <0.05). Conclusion Cardiac rehabilitation training, not increase in the incidence of adverse events, is effective and safe after partial revascularization in patients with multiple coronary artery lesions, which has notable clinical advantages in promoting patients’ exercise endurance and quality-of-life by improving the nitric oxide synthase system and antioxidant system and reducing the level of VEGF.

BACKGROUND As a major health hazard, the incidence of coronary heart disease has been increasing year by year. Although coronary revascularization, mainly percutaneous coronary intervention, has played an important role in the treatment of coronary heart disease, major adverse cardiovascular events (MACE) such as recurrent or persistent angina pectoris after coronary revascularization remain a very difficult problem in clinical practice. OBJECTIVE Given the high probability of MACE after coronary revascularization, the aim of this study was to develop and validate a predictive model for MACE occurrence within 6 months based on machine learning algorithms. METHODS A retrospective study was performed including 1004 patients who had undergone coronary revascularization at The People’s Hospital of Liaoning Province and Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from June 2019 to December 2020. According to the characteristics of available data, an oversampling strategy was adopted for initial preprocessing. We then employed six machine learning algorithms, including decision tree, random forest, logistic regression, naïve Bayes, support vector machine, and extreme gradient boosting (XGBoost), to develop prediction models for MACE depending on clinical information and 6-month follow-up information. Among all samples, 70% were randomly selected for training and the remaining 30% were used for model validation. Model performance was assessed based on accuracy, precision, recall, F1-score, confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), and visualization of the ROC curve. RESULTS Univariate analysis showed that 21 patient characteristic variables were statistically significant (<i>P</i>&lt;.05) between the groups without and with MACE. Coupled with these significant factors, among the six machine learning algorithms, XGBoost stood out with an accuracy of 0.7788, precision of 0.8058, recall of 0.7345, F1-score of 0.7685, and AUC of 0.8599. Further exploration of the models to identify factors affecting the occurrence of MACE revealed that use of anticoagulant drugs and course of the disease consistently ranked in the top two predictive factors in three developed models. CONCLUSIONS The machine learning risk models constructed in this study can achieve acceptable performance of MACE prediction, with XGBoost performing the best, providing a valuable reference for pointed intervention and clinical decision-making in MACE prevention.