Abstract

Abstract Aims Right ventricular (RV) failure (RVF) after left ventricular assist device (LVAD) implant is an important cause of morbidity and mortality. Modern, data‐driven approaches for defining and predicting RVF have been under‐utilized. Methods Two hundred thirty‐two patients were identified with a mean age of 55 years; 40 (17%) were women, 132 were (59%) Caucasian and 74 (32%) were Black. Patients were split between Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) Classes 1, 2 and 3 (25%, 38% and 34%, respectively). Within this group, ‘provisional RVF’ patients were identified, along with ‘no RVF’ patients. ‘No RVF’ patients were defined as patients who never demonstrated more than moderate RV dysfunction on a post‐LVAD transthoracic echocardiogram (TTE) (ordinal RV function <3), never required an RV assist device (RVAD), were not discharged on sildenafil and were not on a pulmonary vasodilator or inotropic medication at 3 months after LVAD implant. In total, n = 67 patients were defined as ‘no RVF’. The remaining patients represented the ‘provisional RVF’ population ( n = 165). Extensive electronic health records queries yielded >1200 data points per patient. Using <1 and >1 month post‐LVAD time windows motivated by established, expert‐consensus definitions of ‘early’ and ‘late’ post‐implant RVF, unbiased clustering analysis was performed to identify hidden patient ‘phenogroups’ within these two established RVF populations. Clusters were compared on post‐implant clinical metrics and 1 year outcomes. Lastly, pre‐implant metrics were used to generate models for predicting post‐implant RVF phenogroup. Results Within the ‘early RVF’ time window, distinct ‘well’ and ‘sick’ patient phenogroup clusters were identified. These clusters had similar RV function and pulmonary vasodilator usage during the first month after LVAD but differed significantly in heart failure therapy tolerance, renal ( P < 0.001) and hepatic ( P = 0.013) function, RVAD usage ( P = 0.001) and 1 year mortality ( P = 0.047). Distinct ‘well’ and ‘sick’ phenogroups were also identified in the ‘late RVF’ time window. These clusters had similar RV function ( P = 0.111) and RVAD proportions ( P = 0.757) but differed significantly in heart failure medication tolerance, pulmonary vasodilator usage ( P = 0.001) and 1 year mortality ( P < 0.001). Prediction of phenogroup clusters from the ‘early RVF’ population achieved an area under the receiver operating characteristic curve (AUROC) of 0.84, with top predictors including renal function, liver function, heart rate and pre‐LVAD RV function. Conclusions Distinct, potentially predictable phenogroups of patients who have significantly different long‐term outcomes exist within consensus‐defined post‐LVAD RVF populations.