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 2012-2016. 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 v. centrifugal) and strategy (bridge to transplantation or destination therapy). 15% (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.