Abstract 10389: Identifying High-Risk Clinical Phenogroups of Pulmonary Hypertension Through a Clustering Analysis

Abstract

Introduction: The classification and management of pulmonary hypertension (PH) is challenging due to clinical and hemodynamic heterogeneity of patients. We sought to identify distinct phenogroups of PH that are at particularly high-risk for adverse events. Methods: A hospital-based cohort of patients referred for right heart catheterization between 2005-2016 with PH (mean pulmonary artery pressure > 20 mmHg at rest) were included. Key exclusion criteria were shock, cardiac arrest, cardiac transplant or valvular surgery. K-prototypes, an unsupervised clustering algorithm, was used to cluster patients into subgroups based on 11 clinical covariates. The optimal number of clusters was determined using the silhouette method. Results: Among 5208 patients with mean age 64 (SD 12) years, 39% women, we identified 6 phenogroups when clustering on baseline clinical comorbidities ( Table 1 ). Phenogroups 2 and 4 had the greatest baseline prevalence of heart failure (both) and diabetes (group 4). Over a median follow-up of 6.3 (IQR 3.6 to 9.8) years we observed 2182 deaths and 2002 major cardiovascular events (MACE). Phenogroups 2 and 4 had the highest risk for future adverse events including death (age and sex adjusted HR 1.33, 95% CI 1.05-1.68 and 1.42, 95% CI 1.13-1.77, each compared with the lowest risk group 3 respectively) and MACE (HR 5.97, 95% CI 4.83-7.38 and 4.06, 95% CI 3.30-4.99, compared with group 3 respectively; Figure 1 ). Conclusions: Cluster-based analyses identify patients with PH and specific comorbid cardiovascular burden that are at higher risk for adverse clinical outcomes. Further studies are needed to better understand clinical heterogeneity among patients with PH.