Machine learning‐based prediction of 1‐year mortality in hypertensive patients undergoing coronary revascularization surgery

Abstract

Background: Machine learning (ML) has shown promising results in all fields of medicine, including preventive cardiology. Hypertensive patients are at higher risk of mortality after coronary artery bypass graft (CABG) surgery; thus, we aimed to design and evaluate five ML models to predict 1-year mortality among hypertensive patients who underwent CABG.Hyothesis: ML algorithms can significantly improve mortality prediction after CABG.Methods: Tehran Heart Center's CABG data registry was used to extract several baseline and peri-procedural characteristics and mortality data. The best features were chosen using random forest (RF) feature selection algorithm. Five ML models were developed to predict 1-year mortality: logistic regression (LR), RF, artificial neural network (ANN), extreme gradient boosting (XGB), and naïve Bayes (NB). The area under the curve (AUC), sensitivity, and specificity were used to evaluate the models.Results: Among the 8,493 hypertensive patients who underwent CABG (mean age of 68.27 ± 9.27 years), 303 died in the first year. Eleven features were selected as the best predictors, among which total ventilation hours and ejection fraction were the leading ones. LR showed the best prediction ability with an AUC of 0.82, while the least AUC was for the NB model (0.79). Among the subgroups, the highest AUC for LR model was for two age range groups (50-59 and 80-89 years), overweight, diabetic, and smoker subgroups of hypertensive patients.Conclusions: All ML models had excellent performance in predicting 1-year mortality among CABG hypertension patients, while LR was the best regarding AUC. These

Mortality across the spectrum of hemoglobin level in patients undergoing surgical coronary revascularization

“…Coronary artery bypass grafting (CABG) surgery is a lifesaving procedure in severe coronary artery disease 2 . Besides the importance of procedure techniques, baseline and preoperative features and comorbidities determine its success rate, in addition to short‐term and long‐term outcomes 3–9 …”

“…2 Besides the importance of procedure techniques, baseline and preoperative features and comorbidities determine its success rate, in addition to short-term and long-term outcomes. [3][4][5][6][7][8][9] Anemia can cause postoperative complications such as arrhythmia, hypoxemia, and heart failure; if left untreated. 10 The major adverse cardiac and cerebrovascular events rate in patients undergoing coronary artery bypass grafting with preoperative anemia is higher than in patients with normal Hb concentration.…”