Open AccessA New Bayesian Network-Based Risk Stratification Model for Prediction of Short-Term and Long-Term LVAD Mortality
Author(s) -
Natasha A. Loghmanpour,
Manreet Kanwar,
Marek J. Drużdżel,
Raymond L. Benza,
Srinivas Murali,
James F. Antaki
Publication year - 2015
Publication title -
asaio journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.961
H-Index - 66
eISSN - 1538-943X
pISSN - 1058-2916
DOI - 10.1097/mat.0000000000000209
Subject(s) - medicine , risk stratification , receiver operating characteristic , bayesian network , destination therapy , risk assessment , bayesian probability , intensive care medicine , ventricular assist device , emergency medicine , statistics , heart failure , machine learning , computer science , mathematics , computer security
Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score 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 Interagency Registry for Mechanically Assisted Circulatory Support dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow LVAD patients and 226 preimplant variables. We then derived Bayesian models for mortality at each of five time end-points postimplant (30 days, 90 days, 6 month, 1 year, and 2 years), 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 receiver operator characteristic (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. Preimplant interventions, such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relations of multiple variables on clinical outcomes. Their potential to develop a reliable risk stratification tool for use in clinical decision making on LVAD patients encourages further investigation.