Application of machine learning for patient response prediction to cardiac resynchronization therapy

Abstract

Heart failure (HF) is a leading cause of morbidity, mortality, and substantial health care costs. Prolonged conduction through the myocardium can occur with HF, and a device-driven approach, termed cardiac resynchronization therapy (CRT), can improve left ventricular (LV) myocardial conduction patterns. We used machine learning methods of classifying HF patients, namely Decision Trees, and Artificial Neural Networks (ANNs), to develop predictive models of individual outcomes following CRT. Clinical, functional, and biomarker data were collected in HF patients before and following CRT. A prospective 6-month endpoint of a reduction in LV volume was defined as a CRT response. Using this approach on 764 subjects (368 responders, 396 non-responders), each with 53 parameters, we could classify HF patients based on their response to CRT with more than 72% success. We also explored the utilization of machine learning techniques in predicting the magnitude of LV volume, 3 months after CRT placement. Using techniques such as linear regression and Artificial neural networks, we can predict the 3-month LV volume within a 17% median margin of error. We have demonstrated that using machine learning approaches can identify HF patients with a high probability of a positive CRT response. Developing these approaches into a clinical algorithm to assist in clinical decision-making regarding the use of CRT in HF patients would potentially improve outcomes and reduce health care costs.