Improving the Prediction of 1-Year Right Ventricular Failure After Left Ventricular Assist Device Implantation

Abstract

Previous predictive models for postimplant right heart failure (RHF) following left ventricular assist device (LVAD) implantation have demonstrated limited performance on validation datasets and are susceptible to overfitting. Thus, the objective of this study was to develop an improved predictive model with reduced overfitting and improved accuracy in predicting RHF in LVAD recipients. The study involved 11,967 patients who underwent continuous-flow LVAD implantation between 2008 and 2016, with an RHF incidence of 9% at 1 year. Using an eXtreme Gradient Boosting (XGBoost) algorithm, the training data were used to predict RHF at 1 year postimplantation, resulting in promising area under the curve (AUC)-receiver operating characteristic (ROC) of 0.8 and AUC-precision recall curve (PRC) of 0.24. The calibration plot showed that the predicted risk closely corresponded with the actual observed risk. However, the model based on data collected 48 hours before LVAD implantation exhibited high sensitivity but low precision, making it an excellent screening tool but not a diagnostic tool.