sST2 and IL-6 predict prognosis of medically treated chronic thromboembolic pulmonary hypertension

BACKGROUND: Accurate risk stratification is crucial for guiding therapy in medically treated chronic thromboembolic pulmonary hypertension (CTEPH), in which inflammation plays a key pathogenic role. This study aimed to identify and validate prognostic inflammatory biomarkers in medically treated CTEPH, with the goal of refining risk assessment and improving clinical management. METHODS: This dual-cohort study enrolled 576 medically treated CTEPH patients (discovery cohort: n=372; validation cohort: n=204) from 2009 to 2021. Plasma levels of inflammatory biomarkers, including soluble suppression tumorigenicity-2 (sST2), galectin-3 and a panel of cytokines, were measured in all participants. The study end-point was all-cause mortality. The prognostic significance of inflammatory markers was evaluated using Kaplan-Meier survival analysis, Cox regression models, C-index, net reclassification improvement and integrated discrimination improvement. RESULTS: Plasma sST2 and interleukin-6 (IL-6) were identified as predictors of survival, and remained robust predictors of mortality after comprehensive adjustment for clinical covariates in both cohorts. Risk stratification using the sST2/IL-6 panel revealed distinct 5-year survival rate gradients: high sST2/high IL-6 (34.4%), low sST2/high IL-6 (61.4%), high sST2/low IL-6 (78.3%) and low sST2/low IL-6 (90.5%) in the discovery cohort (p<0.001), with consistent validation. Furthermore, integrating the sST2/IL-6 panel with existing risk models (COMPERA, COMPERA 2.0, French Pulmonary Hypertension Network (invasive and noninvasive) and REVEAL 2.0) significantly enhanced their prognostic discriminatory power, as evidenced by increased C-indexes, net reclassification improvement and integrated discrimination improvement in both cohorts. CONCLUSIONS: sST2 and IL-6 are independent prognostic biomarkers in medically treated CTEPH. Their combined use enhances risk stratification and adds incremental value to existing risk models.