{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,29]],"date-time":"2026-10-29T07:40:48Z","timestamp":1793259648316,"version":"4.5.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T00:00:00Z","timestamp":1740960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Sepsis-induced coagulopathy (SIC) is a complex condition characterized by systemic inflammation and coagulopathy. This study aimed to develop and validate a machine learning (ML) model to predict SIC risk in patients with sepsis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Patients with sepsis admitted to the intensive care unit (ICU) between March 1, 2021, and March 1, 2024, at Hebei General Hospital and Handan Central Hospital (East District) were retrospectively included. Patients were categorized into SIC and non-SIC groups. Data were split into training (70%) and testing (30%) sets. Additionally, for temporal validation, patients with sepsis admitted between March 1, 2024, and October 31, 2024, at Hebei General Hospital were included. Feature selection was performed using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. Nine ML algorithms were tested, and model performance was assessed using receiver operating characteristic curve (ROC) analysis, including area under the curve (AUC), calibration curves, and decision curve analysis (DCA). The SHaply Additive Explanations (SHAP) algorithm was used to interpret the best-performing model and visualize key predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among 847 patients with sepsis, 480 (56.7%) developed SIC. The random forest (RF) model with eight variables performed best, achieving AUCs of 0.782 [95% confidence interval (CI): 0.745, 0.818] in the training set, 0.750 (95% CI: 0.690, 0.809) in the testing set, and 0.784 (95% CI: 0.711, 0.857) in the validation set. Key predictors included activated partial thromboplastin time, lactate, oxygenation index, and total protein.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This ML model reliably predicts SIC risk. SHAP enhances interpretability, supporting early, individualized interventions to improve outcomes in patients with sepsis.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1552265","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T05:16:17Z","timestamp":1740978977000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Interpretable machine learning model for early morbidity risk prediction in patients with sepsis-induced coagulopathy: a multi-center 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