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The clinical and biological heterogeneity of acute respiratory distress syndrome (ARDS) complicates early prognostic assessment and may contribute to variability in treatment-associated outcomes. In this multicentre study of 2,868 patients from 10 centres, we developed and externally validated a multimodal risk-stratification framework integrating clinical variables, chest CT imaging, and serum proteomic data. Serum proteomic profiles were available in 879 patients and exploratory bronchoalveolar lavage fluid (BALF) single-cell RNA sequencing was performed in 26 patients to characterize pulmonary microenvironment of model-defined risk states. The clinical-imaging model achieved AUROCs of 0.831, 0.805, and 0.781 for 30-day mortality prediction in the training, internal test, and external validation cohorts, respectively, while proteomic integration improved discrimination to 0.892, 0.871, and 0.859 in the proteome-matched subset. Higher predicted risk was associated with impaired early liberation from mechanical ventilation and increased non-invasive ventilation failure. Serum proteomic analyses revealed distinct biological profiles of high-risk group characterized by tissue-injury and inflammatory signatures, such as KRT19, CHIT1, IGFBP2, and IL-6, with enrichment of inflammatory pathways (HMGB1 and TREM1 signalling), metabolic and nuclear receptor pathways (FXR/RXR and PPAR signalling), extracellular matrix remodelling (FAK signalling and collagen degradation), endothelial dysfunction, and coagulation pathways. BALF single-cell RNA further identified neutrophil-enriched pulmonary immune responses in high-risk patients. Glucocorticoid-associated 30-day mortality varied across model-defined risk strata ( P for interaction < 0.001 and 0.033 for the clinical-imaging and multimodal models, respectively), with lower mortality associated with glucocorticoid initiation among high-risk patients. These findings support multimodal risk stratification as a framework for identifying clinically and biologically distinct ARDS risk states and generating hypotheses regarding treatment-associated heterogeneity.
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Song et al. (2026) studied this question.
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