Development and Internal Validation of a 24-Hour Landmark Model for 28-Day Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome.

Sepsis-associated acute respiratory distress syndrome (ARDS) carries substantial short-term mortality. Differences in predictor timing, validation design, and comparators complicate evaluation of published prediction models. This single-center retrospective cohort study included 955 adults with sepsis-associated ARDS who reached a 24-h landmark after intensive care unit (ICU) admission. All-cause mortality was assessed by day 28 from ICU admission. Sequential Organ Failure Assessment (SOFA)-only logistic regression, conventional multivariable logistic regression, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, and extreme gradient boosting (XGBoost) were compared. The multivariable models used 26 candidate predictors (30 encoded parameters). Five repetitions of five-fold outer cross-validation with three-fold inner tuning provided held-out predictions. Evaluation included the area under the receiver operating characteristic curve (AUC), calibration, classification metrics, and decision-curve net benefit. Held-out tree-based Shapley additive explanations described XGBoost predictions. Among 955 patients, 474 (49.6%) died. AUCs based on averaged held-out probabilities were 0.732 (95% confidence interval [CI], 0.698-0.761) for LASSO, 0.729 (0.696-0.761) for conventional regression, 0.709 (0.674-0.740) for XGBoost, 0.707 (0.673-0.739) for random forest, and 0.581 (0.545-0.617) for SOFA alone. The LASSO-minus-conventional-regression AUC difference was 0.003 (95% CI, -0.003 to 0.010). Intervals resampled fixed predictions without refitting. LASSO had a Brier score of 0.209, calibration intercept of -0.001, and slope of 1.065. The final equation retained age, Charlson Comorbidity Index, arterial oxygen partial pressure/fraction of inspired oxygen ratio, SOFA, lactate, bilirubin, other infection site, and vasopressor use. LASSO provided moderate discrimination without a clear advantage over conventional regression. The separately tuned final equation requires source-data verification, external validation, and prospective clinical-utility assessment before clinical use.