Abstract
Use of a left ventricular assist device (LVAD) can benefit patients with end stage heart failure, but only with careful patient selection. In this study, previously derived Bayesian network models for predicting LVAD patient mortality at 1, 3, and 12 months post-implant were evaluated on retrospective data from a single implant center. The models performed well at all three time points, with a receiver operating characteristic area under the curve (ROC AUC) of 78, 76, and 75%, respectively. This evaluation of model performance verifies the utility of these models in “real life” scenarios at an individual institution.
Highlights
Heart failure is a chronic, progressive condition that affects over 6 million Americans
left ventricular assist device (LVAD) can improve quality of life and increase patient survival [4, 5], and require changes in daily life, a significant investment of time and money, and are associated with risks of adverse events [6]. These tradeoffs underscore the importance of careful patient selection, for which predictive models can serve as an important component of risk assessment
We recently developed models to predict post-LVAD mortality at 1, 3, and 12 months after implant [7] using the data from the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS), the largest registry of retrospective LVAD patient data in the United States [4]
Summary
Heart failure is a chronic, progressive condition that affects over 6 million Americans It is characterized by a decline in function of the heart to pump enough blood to perfuse the body [1]. LVADs can improve quality of life and increase patient survival [4, 5], and require changes in daily life, a significant investment of time and money, and are associated with risks of adverse events [6]. These tradeoffs underscore the importance of careful patient selection, for which predictive models can serve as an important component of risk assessment
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