A Bayesian Model to Predict Survival After Left Ventricular Assist Device Implantation.

Journal Article (Journal Article)

OBJECTIVES: This study investigates the use of a Bayesian statistical models to predict survival at various time points in patients undergoing left ventricular assist device (LVAD) implantation. BACKGROUND: LVADs are being increasingly used in patients with end-stage heart failure. Appropriate patient selection continues to be key in optimizing post-LVAD outcomes. METHODS: Data used for this study were derived from 10,277 adult patients from the INTERMACS (Inter-Agency Registry for Mechanically Assisted Circulatory Support) who had a primary LVAD implanted between January 2012 and December 2015. Risk for mortality was calculated retrospectively for various time points (1, 3, and 12 months) after LVAD implantation, using multiple pre-implantation variables. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables. RESULTS: A set of 29, 26, and 31 pre-LVAD variables were found to be predictive at 1, 3, and 12 months, respectively. Predictors of 1-month mortality included low Inter-Agency Registry for Mechanically Assisted Circulatory Support profile, number of acute events in the 48 h before surgery, temporary mechanical circulatory support, and renal and hepatic dysfunction. Variables predicting 12-month mortality included advanced age, frailty, device strategy, and chronic renal disease. The accuracy of all Bayesian models was between 76% and 87%, with an area under the receiver operative characteristics curve of between 0.70 and 0.71. CONCLUSIONS: A Bayesian prognostic model for predicting survival based on the comprehensive INTERMACS registry provided highly accurate predictions of mortality based on pre-operative variables. These models may facilitate clinical decision-making while screening candidates for LVAD therapy.

Full Text

Duke Authors

Cited Authors

  • Kanwar, MK; Lohmueller, LC; Kormos, RL; Teuteberg, JJ; Rogers, JG; Lindenfeld, J; Bailey, SH; McIlvennan, CK; Benza, R; Murali, S; Antaki, J

Published Date

  • September 2018

Published In

Volume / Issue

  • 6 / 9

Start / End Page

  • 771 - 779

PubMed ID

  • 30098967

Pubmed Central ID

  • PMC6119115

Electronic International Standard Serial Number (EISSN)

  • 2213-1787

Digital Object Identifier (DOI)

  • 10.1016/j.jchf.2018.03.016


  • eng

Conference Location

  • United States