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Nearly one third of patients receiving cardiac resynchronization therapy (CRT) suffer non-response. We intend to develop predictive models using machine learning (ML) approaches and easily attainable features before CRT implantation.The baseline characteristics of 752 CRT recipients from two hospitals were retrospectively collected. Nine ML predictive models were established, including logistic regression (LR), elastic network (EN), lasso regression (Lasso), ridge regression (Ridge), neural network (NN), support vector machine (SVM), random forest (RF), XGBoost and k-nearest neighbour (k-NN). Sensitivity, specificity, precision, accuracy, F1, log-loss, area under the receiver operating characteristic (AU-ROC), and average precision (AP) of each model were evaluated. AU-ROC was compared between models and the latest guidelines. Six models had an AU-ROC value above 0.75. The LR, EN and Ridge models showed the highest overall predictive power compared with other models with AU-ROC at 0.77. The XGBoost model reached the highest sensitivity at 0.72, while the highest specificity was achieved by Ridge model at 0.92. All ML models achieved higher AU-ROCs that those derived from the latest guidelines (all P 0.05). The effect size analysis identified left bundle branch block, left ventricular end-systolic diameter, and history of percutaneous coronary intervention as the most crucial predictors of CRT response. An online tool to facilitate the prediction of CRT response is freely available at http://www.crt-response.com/.ML algorithms produced efficient predictive models for evaluation of CRT response with features before implantation. Tools developed accordingly could improve the selection of CRT candidates and reduce the incidence of non-response.

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia seen in clinical practice, accounting for approximately one-third of hospitalizations for heart rhythm disorders. The prevalence of AF is estimated to be 1% in the general population and increases markedly with advancing age, rising from about 0.5% of people in their fifties to nearly 10% of the octogenarians (1) .A F is associated with substantial morbidity and mortality. It confers a 5-fold increased risk of stroke and a double risk of death (1). AF generally occurs secondary to various cardiac and systemic disorders, including hypertension, cor