title: A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality.
abstract: 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.
authors.affiliation: From the *Department of Biomedical Engineering, Carnegie Mellon Engineering, Pittsburgh, Pennsylvania; †Cardiovascular Institute, Allegheny General Hospital, Allegheny Health Network, Pittsburgh, Pennsylvania; ‡Decision Systems Laboratory, School of Information Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania; and §Faculty of Computer Science, Bialystok University of Technology, Bialystok, Poland.
authors.forename: Natasha A
authors.initials: NA
authors.lastname: Loghmanpour
authors.forename: Manreet K
authors.initials: MK
authors.lastname: Kanwar
authors.forename: Marek J
authors.initials: MJ
authors.lastname: Druzdzel
authors.forename: Raymond L
authors.initials: RL
authors.lastname: Benza
authors.forename: Srinivas
authors.initials: S
authors.lastname: Murali
authors.forename: James F
authors.initials: JF
authors.lastname: Antaki
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001499:Bayes Theorem; D006333:Heart Failure / Q000401:mortality* / Q000601:surgery*; D006353:Heart-Assist Devices; D006801:Humans; D053208:Kaplan-Meier Estimate; D000069550:Machine Learning; D018579:Patient Selection; D012372:ROC Curve; D012189:Retrospective Studies; D012306:Risk; D018570:Risk Assessment