Work

Interpretable machine learning for early prediction of sepsis-induced coagulopathy: a multicenter retrospective development and validation study

Year: 2026

Type: article

Abstract: BACKGROUND: Sepsis-induced coagulopathy (SIC) is a frequent complication of sepsis that significantly impacts clinical outcomes, underscoring the necessity for early identification to facilitate timely intervention. This study aimed to develop an interpretable machine-learning model to predict early onset SIC within 3 days. METHODS: In this multicenter retrospective cohort study, four distinct cohorts derived from the MIMIC-IV dataset, the eICU Collaborative Research Database, and an ICU cohort from Zhongda Hospital, Southeast University, were used for model derivation and validation. Core predictive variables were identified through multi-step feature engineering using data collected within the first 24 h of ICU admission. Five machine learning models were trained based on these features and subsequently evaluated on three external validation cohorts. RESULTS: The derivation cohort, drawn from MIMIC-IV v2.2 database, included 11,033 patients. External validation encompassed three cohorts totaling 5,572 patients: MIMIC-IV v3.1 dataset (n = 1,371), eICU Database (n = 2,848), and Zhongda Hospital database (n = 1,353). Among five evaluated machine learning models, XGBoost demonstrated the best discriminative performance. The final model incorporated seven predictive variables: International Normalized Ratio, lactate, platelet count, Sequential Organ Failure Assessment (SOFA) circulation, renal, and respiratory subscores, and mechanical ventilation status. The model achieved robust prediction of early-stage SIC across validation sets (internal validation set AUC = 0.83; internal test set AUC = 0.82; external validation set AUC = 0.93,0.86,0.83, respectively). However, the model was ineffective for predicting late-onset SIC, with an internal test set AUC of 0.5 and external validation set AUCs of 0.48, 0.53, and 0.6, respectively. CONCLUSION: This study establishes the feasibility of an interpretable XGBoost model for accurately predicting early-onset SIC. As a promising clinical decision-support tool, its real-world utility and impact on patient outcomes should be further established through prospective validation prior to deployment.

Source: BMC Medical Informatics and Decision Making

DOI: 10.1186/s12911-026-03471-8