Development of machine learning models to predict response after cardiac resynchronization therapy

Abstract

Abstract Background There have been few practical and precise tools to predict response after cardiac resynchronisation therapy (CRT). Purpose We intend to develop predictive models using machine learning (ML) approaches and easily available features prior to implantation. Methods The baseline features of 596 patients receiving CRT were retrospectively collected. Nine predictive models were established, including logistic regression (LR), Elastic Net (EN), lasso regression, ridge regression (Ridge), neural network, support vector machine (SVM), random forest, XGBoost and k-nearest neighbor. Sensitivity, specificity, precision, accuracy, F1, area under receiver operating characteristic curve (AU-ROC) and average precision of each model were evaluated, and AU-ROC was compared between each pair of ML models and further between ML models and the latest guidelines. Results Sensitivity was highest with SVM by 0.69, and specificity was highest with LR by 0.81. The models EN and Ridge showed the highest overall predictive power with an average AU-ROC of 0.77. Specifically, the Ridge model provided significant higher AU-ROC than any other model (all P<0.05). All ML models showed significant higher AU-ROC than those derived from the latest guidelines (all P<0.05). Additionally, the effect size analysis identified LBBB, LVESD, and history of PCI as the most crucial predictive features. Conclusion ML algorithms produced efficient predictive models for evaluation of response after CRT with features prior to implantation. Tools developed accordingly might improve selection of CRT candidates and reduce rate of non-response in the future. ROC and PR curves of predictive models Funding Acknowledgement Type of funding source: None