Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database

Background: To develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS). Methods: This retrospective cohort study included 2466 patients diagnosed with sepsis and ARDS within 24 h of ICU admission. Demographic, clinical, and laboratory parameters were extracted from Medical Information Mart for Intensive Care III (MIMIC-III) database. Feature selection was performed using the Boruta algorithm, followed by the construction of seven ML models: logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, decision tree, Random Forest, and extreme gradient boosting. Model performance was evaluated using the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results: The study identified 24 variables significantly associated with mortality. The optimal ML model, a Random Forest model, demonstrated an AUC of 0.8015 in the test set, with high accuracy and specificity. The model highlighted the importance of blood urea nitrogen, age, urine output, Simplified Acute Physiology Score II, and albumin levels in predicting mortality. Conclusions: The model's superior predictive performance underscores the potential for integrating advanced analytics into clinical decision-making processes, potentially improving patient outcomes and resource allocation in critical care settings.