Explainable Machine Learning Analysis of Right Heart Failure After Left Ventricular Assist Device Implantation

Abstract

Right heart failure (RHF) remains a common and serious complication after durable left ventricular assist device (LVAD) implantation. We used explainable machine learning (ML) methods to derive novel insights into preimplant patient factors associated with RHF. Continuous-flow LVAD implantations from 2008 to 2017 in the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) were included. A total of 186 preimplant patient factors were analyzed and the primary outcome was 30 days of severe RHF. A boosted decision tree ML algorithm and an explainable ML method were applied to identify the most important factors associated with RHF, nonlinear relationships and interactions, and risk inflection points. Out of 19,595 patients, 19.1% developed severe RHF at 30 days. Thirty top predictors of RHF were identified with the top five being INTERMACS profile, Model for End-stage Liver Disease score, the number of inotropic infusions, hemoglobin, and race. Many top factors exhibited nonlinear relationships with key risk inflection points such as INTERMACS profile between 2 and 3, right atrial pressure of 15 mmHg, pulmonary artery pressure index of 3, and prealbumin of 23 mg/dl. Finally, the most important variable interactions involved INTERMACS profile and the number of inotropes. These insights could help formulate patient optimization strategies prior to LVAD implantation.