Prognostic assessment of sepsis-induced acute respiratory distress syndrome in older patients using clinical and CT-based radiomic features

BACKGROUND: Sepsis-induced acute respiratory distress syndrome (SI-ARDS) is associated with high mortality rates, necessitating early risk stratification. This study aimed to develop and validate a radiomics-based nomogram integrating computed tomography (CT) features and clinical parameters to predict 28-day mortality in older patients with SI-ARDS. METHODS: In this retrospective cohort study, 302 older patients (≥60 years) diagnosed with SI-ARDS between January 2019 and December 2023 were enrolled. Radiomic features were extracted from admission chest CT images. Patients were randomly allocated to training (n = 242) and validation (n = 60) cohorts. Three predictive models-radiomic, clinical, and combined-were constructed using Maximum Relevance Minimum Redundancy (MRMR) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Model performance was assessed using the concordance index (C-index), calibration curves, and decision curve analysis. A nomogram was developed based on the optimal model for clinical application. RESULTS: The fusion model achieved superior discrimination compared with the radiomic model, clinical model, and Sequential Organ Failure Assessment score in both cohorts (C-index: training, 0.850 vs. 0.798, 0.781, and 0.654; validation, 0.839 vs. 0.768, 0.779, and 0.696; all p < 0.001). The model demonstrated excellent calibration and provided greater net clinical benefit across threshold probabilities of 10 %-90 %. Risk stratification using the nomogram identified distinct prognostic groups with significantly different 28-day survival (log-rank p < 0.001). CONCLUSION: The nomogram developed from the fusion model demonstrated superior predictive performance for 28-day mortality in older patients with SI-ARDS compared to conventional scoring systems, though multicenter validation is required to confirm clinical utility.