The Right Heart Network and Risk Stratification in Pulmonary Arterial Hypertension

ABSTRACT Background Prognostic in pulmonary arterial hypertension (PAH) is closely related to indices of right ventricular function. A better understanding of their relationship may provide important implications for risk stratification in PAH. Research Questions Can clinical network graphs inform risk stratification in PAH? Methods Our cohort consisted of 231 patients with PAH followed for a median of 7.1 years. We used an undirected, correlation network to visualize the relationship between clinical features in PAH. Our network was enriched for right heart parameters and included N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP), comprehensive echocardiographic parameters, hemodynamics as well as six-minute walking distance (6MWD), vital signs, laboratory data and diffusing capacity for carbon monoxide (DLCO). Connectivity was assessed using eigenvector and betweenness centrality to reflect global and regional connectivity, respectively. Cox proportional hazards regression was used to model event free survival for the combined end-point of death or lung transplantation. Results We identified a network of closely intertwined features centered around NT-proBNP with 6MWD emerging as a secondary hub. Less connected nodes included DLCO, systolic blood pressure, albumin and sodium. Over the follow-up period, death or transplantation occurred in 92 patients (39.8%). A strong prognostic model was achieved with a Harrell’s C-index of 0.81 (0.77-0.85). when combining central right heart features (NT-proBNP and RV end-systolic remodeling index) with 6MWD and less connected nodes, e.g. DLCO, systolic blood pressure, albumin, sodium, sex, connective tissue disease (CTD) etiology and prostanoid therapy. When added to baseline risk model, serial change in NT-proBNP significantly improved outcome prediction at 5 years (increase in C-statistic of 0.071 ±0.024, p= 0.003). Interpretation NT-proBNP emerged as a central hub in the intertwined PAH network. Connectivity analysis provides explainability for feature selection and combination in outcome models.