Soluble ST2 dynamics are associated with delayed hospital discharge in COVID-19 patients: a joint-modeling analysis of the DisCoVeRy trial’s longitudinal data.

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OBJECTIVES: Identifying robust biomarkers reflecting thromboinflammation, endothelial injury, angiogenesis, and fibrosis that predict clinical recovery remains challenging. Although several biomarkers are associated with disease severity, the added value of their longitudinal dynamics beyond established markers is unclear. We assessed the association between biomarker trajectories and time to hospital discharge in hospitalized patients with COVID-19. METHODS: We analyzed hospitalized adults with PCR-confirmed SARS-CoV-2 infection enrolled in the DisCoVeRy randomized trial. Blood samples were collected at baseline and days 3, 5, 8, and 11 to measure D-dimer and 12 biomarkers. The primary outcome was time to hospital discharge by day 29, with death as a competing risk. Bayesian joint models combining linear mixed-effects models for biomarker trajectories and cause-specific Cox models were adjusted for baseline severity, ISARIC 4C score, and treatment arm. Models included D-dimer alone and each biomarker added individually. Results are reported as adjusted hazard ratios (aHR) with 95% credibility intervals (CrI). RESULTS: ), but not when including sST2 (0.68 [0.42-1.07], P=0.09). CONCLUSION: Longitudinal sST2 dynamics were associated with delayed hospital discharge before day 29 and provided better prognostic information than D-dimer. By integrating signals of inflammation, endothelial injury, and tissue remodeling, sST2 may serve as a biomarker of impaired recovery and a potential tool for risk stratification and clinical decision support. External validation by prospective studies is needed to confirm our findings.

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