Application of machine learning prediction models for in-hospital mortality in diabetic sepsis patients: a study based on the MIMIC IV database.

backgroundSepsis complicated by diabetes mellitus represents a significant clinical challenge in intensive care units (ICUs), associated with substantial morbidity and mortality. Accurate prognostication of in-hospital mortality in this complex patient population is crucial for optimizing clinical decision making, resource allocation, and patient management strategies.

methodsWe conducted a retrospective cohort study utilizing data from the Medical Information Mart for Intensive Care (MIMIC-IV) database. The study focused on adult ICU patients with concurrent diagnoses of sepsis and diabetes mellitus. Six machine learning algorithms were evaluated for their predictive performance: Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), XGBoost and LightGBM. Additionally, we assessed the prognostic capability of the Sequential Organ Failure Assessment (SOFA) score. Model discrimination was quantified using the area under the receiver operating characteristic curve (AUC). To enhance model interpretability, we employed SHAP (SHapley Additive exPlanations) values to elucidate the relative importance of predictor variables.

resultsThe study cohort comprised 7716 patients, with 1357 (17.6%) experiencing in-hospital mortality. Among the machine learning models evaluated, GBDT exhibited superior predictive performance achieving an AUC of 0.792 (95% CI: 0.769-0.814). This performance surpassed that of other machine learning algorithms and the conventional SOFA score. SHAP analysis provided valuable insights into the hierarchical importance of various clinical and demographic features contributing to the GBDT model's predictions.

conclusionOur findings demonstrate the potential of machine learning approaches in accurately predicting in-hospital mortality among ICU patients with sepsis and diabetes mellitus. These advanced analytical techniques outperform traditional scoring systems such as SOFA. The integration of SHAP values enhances model interpretability, potentially facilitating the adoption of these predictive tools in clinical practice. Further multi-center validation studies and prospective trials are warranted to confirm the generalizability of these results and to explore the clinical impact of implementing such models in diverse healthcare settings.