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A New Bayesian Network-Based Risk Stratification Model for Prediction of Short-term and Long-term LVAD Mortality

AUTHORS: Natasha A. Loghmanpour MS,  Manreet K. Kanwar MD,  Marek J. Druzdzel PhDRaymond L. Benza MD,  Srinivas Murali MD, and  James F. Antaki PhD


Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score (DTRS) and HeartMate II Risk Score (HMRS) have limited predictive ability. This study aims to overcome the limitations of traditional statistical methods by performing the first application of Bayesian analysis to the comprehensive INTERMACS dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow (CF) LVAD patients and 226 pre-implant variables. We then derived Bayesian models for mortality at each of five time endpoints post-implant (30 day, 90 day, 6 month, 1 year, and 2 year), achieving accuracies of 95, 90, 90, 83, and 78%, Kappa values of 0.43, 0.37, 0.37, 0.45, and 0.43, and area under the ROC of 91, 82, 82, 80 and 81% respectively. This was in comparison to the HMRS with an ROC of 57 and 60% at 90-days and 1-year, respectively. Pre-implant interventions such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relationships of multiple variables on clinical outcomes.

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