Phenomapping a Novel Classification System for Patients With Destination Therapy Left Ventricular Assist Devices

Abstract

Patients with continuous flow destination therapy (DT) left ventricular assist devices (LVAD) comprise a heterogeneous population. We hypothesized that phenotypic clustering of individuals with DT LVADs by their implantation characteristics will be associated with different long-term risk profiles. We analyzed 5,999 patients with continuous flow DT LVADs in Interagency Registry for Mechanically Assisted Circulatory Support using 18 continuous variable baseline characteristics. We Z-transformed the variables and applied a Gaussian finite mixture model to perform unsupervised clustering resulting in identification of 4 phenogroups. Survival analyses considered the competing risk for cumulative incidence of transplant or the composite end point of death or heart transplant where appropriate. Phenogroup 1 (n=1,163, 19%) was older (71 years) and primarily white (81%). Phenogroups 2 (n=648, 11%) and 3 (n=3,671, 61%) were of intermediate age (70 and 62 years), weight (85 and 87 kg), and ventricular size. Phenogroup 4 (n=517, 9%) was younger (40 years), heavier (108 kg), and more racially diverse. The cumulative incidence of death, heart transplant, bleeding, LVAD malfunction, and LVAD thrombosis differed among phenogroups. The highest incidence of death and the lowest rate of heart transplant was seen in phenogroup 1 (p <0.001). For adverse outcomes, phenogroup 4 had the lowest incidence of bleeding, whereas LVAD device thrombosis and malfunction were lowest in phenogroup 1 (p <0.001 for all). Finally, the incidence of stroke, infection, and renal dysfunction were not statistically different. In conclusion, the present unsupervised machine learning analysis identified 4 phenogroups with different rates of adverse outcomes and these findings underscore the influence of phenotypic heterogeneity on post-LVAD implantation outcomes.