AUTHOR=Sha Tao , Jiang Hao , Feng Lei 

TITLE=Early prediction of sepsis-induced coagulopathy in the ICU using interpretable machine learning: a multi-center retrospective cohort study

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1681621

DOI=10.3389/fmed.2025.1681621

ISSN=2296-858X

ABSTRACT=BackgroundSepsis-induced coagulopathy (SIC) is a fatal complication in ICU patients, yet early risk prediction remains challenging. This study aimed to develop an interpretable machine learning model for predicting SIC within seven days of ICU admission.MethodsClinical data for model development were retrieved from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. Feature selection was performed using three distinct algorithms: least absolute shrinkage and selection operator (LASSO) regression, random forest recursive feature elimination (RF-RFE), and the Boruta method. Ten machine learning models underwent training employing 5-fold cross-validation on the training subset, with subsequent evaluation on the validation subset encompassing discrimination, calibration, and clinical utility metrics. The optimal model underwent further interpretability analysis through SHapley Additive exPlanations (SHAP) to elucidate variable contributions and their directional effects. External validation was then conducted using the electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). Finally, the best-performing model was implemented as a web-based Shiny application featuring an interactive interface.ResultsAmong 10,740 patients in MIMIC-IV, 2,232 (20.78%) developed SIC within 7 days post-ICU admission. A LightGBM model with thirteen variables demonstrated optimal performance, achieving an area under the receiver operating characteristic curve (AUROC) of 0.885 (95% confidence interval (CI): 0.874–0.897) in the internal validation set and 0.831 (95% CI: 0.819–0.843) in the external eICU-CRD cohort. Key predictive variables included Prothrombin Time-International Normalization Ratio (INR), platelet count, Sequential Organ Failure Assessment (SOFA), lactate, systolic blood pressure (SBP), red cell distribution width (RDW), bicarbonate, phosphate, hemoglobin, age, the presence of heart failure (HF), ischemic heart disease (IHD) and the use of continuous renal replacement therapy (CRRT). The model was deployed as a clinician-oriented web application providing an accessible interface (https://shatao.shinyapps.io/Sepsis_Induced_Coagulopathy/).ConclusionThis model demonstrated strong predictive ability and clinical interpretability, enabling early SIC identification and targeted intervention.