Risk Assessment in Patients with a Left Ventricular Assist Device Across INTERMACS Profiles Using Bayesian Analysis.

Published online

Journal Article

Current risk stratification models to predict outcomes after a left ventricular assist device (LVAD) are limited in scope. We assessed the performance of Bayesian models to stratify post-LVAD mortality across various International Registry for Mechanically Assisted Circulatory Support (INTERMACS or IM) Profiles, device types, and implant strategies. We performed a retrospective analysis of 10,206 LVAD patients recorded in the IM registry from 2012 to 2016. Using derived Bayesian algorithms from 8,222 patients (derivation cohort), we applied the risk-prediction algorithms to the remaining 2,055 patients (validation cohort). Risk of mortality was assessed at 1, 3, and 12 months post implant according to disease severity (IM profiles), device type (axial versus centrifugal) and strategy (bridge to transplantation or destination therapy). Fifteen percentage (n = 308) were categorized as IM profile 1, 36% (n = 752) as profile 2, 33% (n = 672) as profile 3, and 15% (n = 311) as profile 4-7 in the validation cohort. The Bayesian algorithms showed good discrimination for both short-term (1 and 3 months) and long-term (1 year) mortality for patients with severe HF (Profiles 1-3), with the receiver operating characteristic area under the curve (AUC) between 0.63 and 0.74. The algorithms performed reasonably well in both axial and centrifugal devices (AUC, 0.68-0.74), as well as bridge to transplantation or destination therapy indication (AUC, 0.66-0.73). The performance of the Bayesian models at 1 year was superior to the existing risk models. Bayesian algorithms allow for risk stratification after LVAD implantation across different IM profiles, device types, and implant strategies.

Full Text

Duke Authors

Cited Authors

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

Published Date

  • January 24, 2019

Published In

PubMed ID

  • 30688695

Pubmed Central ID

  • 30688695

Electronic International Standard Serial Number (EISSN)

  • 1538-943X

Digital Object Identifier (DOI)

  • 10.1097/MAT.0000000000000910

Language

  • eng

Conference Location

  • United States