Background Acute respiratory distress syndrome (ARDS) is a common clinical syndrome among critically ill patients and is associated with high mortality. Although scoring tools based on organ dysfunction currently exist, their ability to assess the immune status of patients remains limited. This study aimed to develop a predictive model for in-hospital mortality in non-immunosuppressed adults with pneumonia-associated ARDS by integrating clinical and host immune parameters. Methods This single-center retrospective study included 126 adults with p-ARDS. Data collected within 48 h of admission were analyzed. Candidate predictors ( p 0.10 in univariate analysis, excluding post-admission treatments) underwent simultaneous selection by BIC-based best subset selection and LASSO regression; variables identified by both methods were retained. A multivariable model combining selected predictors with the SOFA score (Model 2) was constructed, and a SOFA-only model served as the baseline (Model 1). Performance was evaluated for discrimination, calibration, and clinical utility. Bootstrap internal validation (500 iterations) was performed. Results The overall in-hospital mortality rate was 35.7% (45/126). The final combined model incorporated the following variables: heart failure (aOR = 5.207), CD3 + CD4 + T-cell count (aOR = 0.719 per 100 cells/μL), D-dimer (aOR = 1.113 per 1 μg/mL) and SOFA score (aOR = 1.126 per point). The model demonstrated excellent predictive performance, with an AUC of 0.805 (95% CI: 0.730–0.879), which was significantly higher than that of the baseline model (AUC: 0.707; 95% CI: 0.618–0.797; p = 0.020). Calibration curves and decision curve analysis further confirmed its good calibration and superior clinical net benefit. Bootstrap internal validation demonstrated robustness (optimism-corrected C-statistic: 0.788). Conclusion We developed a multidimensional model for predicting in-hospital mortality in non-immunosuppressed adult patients with p-ARDS. The model’s performance was superior to the SOFA score. External validation is needed for the widespread application of this model.
Xing et al. (Wed,) studied this question.
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