Interpretable Machine Learning for In-Hospital Mortality Prediction in Patients With Diabetes and ARDS: Development and External Validation.

Background

Patients with diabetes mellitus complicated by acute respiratory distress syndrome (ARDS) are a high-risk subgroup, but population-specific models for in-hospital mortality remain limited. We aimed to develop and externally validate machine learning models using the 2023 New Global Definition of ARDS.

Methods

This cross-national, multicenter retrospective cohort study used MIMIC-IV version 3.1 and ICU data from six Chinese institutions. Adults with diabetes and ARDS who stayed in the ICU for more than 24 hours were included. Clinical variables first recorded within 24 hours after ICU admission were candidate predictors. LASSO regression with 10-fold cross-validation selected features. Seven machine learning models were compared. Performance was assessed using discrimination, calibration, Brier score, decision curve analysis, and SHAP.

Results

A total of 539 MIMIC-IV patients were divided into training and internal validation sets, and 478 patients from six Chinese centers formed the external cohort. In-hospital mortality was 16.9%, 16.7%, and 20.3%, respectively. Logistic Regression achieved the highest internal AUROC (0.900), with an AUPRC of 0.588, sensitivity of 0.889, specificity of 0.800, and Brier score of 0.093. In external validation, its AUROC was 0.757, AUPRC 0.368, and Brier score 0.150. Observed mortality increased across higher predicted-risk groups. SHAP identified HCO₃⁻, PaCO₂, age, temperature, SpO₂, red blood cell count, platelet count, and respiratory rate as key predictors.

Conclusions

This study developed and externally validated machine learning models for in-hospital mortality prediction in patients with diabetes and ARDS. Logistic Regression showed potential as an interpretable early ICU risk-stratification tool.