Development and validation of an interpretable machine learning model for early prediction in patients with diabetes and sepsis.

We aimed to identify and validate key predictive factors influencing 28-day survival rates in patients with diabetes and sepsis and to develop a predictive model based on these factors to assist clinical decision-making. In this retrospective cohort study, we examined data from 303 patients with diabetes and sepsis treated at the Emergency Department of West China Hospital, Sichuan University, between June 2022 and November 2023. The Least Absolute Shrinkage and Selection Operator (LASSO) method was employed to identify key predictive factors from 52 characteristics. A logistic regression model was then developed to create a nomogram for predicting 28-day survival rates. Model performance was assessed using calibration curves, Harrell's C-index, bootstrap validation, decision curve analysis, and receiver operating characteristic (ROC) curve analysis. Six major predictive factors were identified: age, consciousness level, acid-base balance (pH level), aspartate aminotransferase (AST) level, myoglobin concentration, and the need for mechanical ventilation. The nomogram exhibited excellent concordance with the calibration curve, achieving a C-index of 0.833 and demonstrating robust discriminative capability, as validated through bootstrapping. Decision curve analysis indicated that the model provided a greater net benefit within a patient survival probability threshold ranging from 20% to 80%. ROC curve analysis revealed an area under the curve of 0.833, highlighting the model's strong discriminatory power. The predictive model developed in this study for the 28-day survival rate of patients with diabetes and sepsis demonstrates high predictive accuracy and serves as an effective clinical decision-making tool for healthcare professionals.