ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1653699
This article is part of the Research TopicOutcome of Sepsis and Prediction of Mortality Risk - Volume IIView all 8 articles
Development and Validation of a Nomogram for Early Prediction of Sepsis-Induced Coagulopathy: A Multicenter Study
Provisionally accepted
- 1Critical care department, Chongqing General Hospital, Chongqing, China
- 2Critical care department, Chongqing General Hospital, Chongqing University, Chongqing, China
- 3Critical care department, Hebei General Hospital, Shijiazhuang, Hebei, China
- 4Critical care department, Handan Central Hospital, Handan, Hebei, China
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Background Sepsis-induced coagulopathy (SIC) is a coagulation disorder caused by vascular endothelial injury during sepsis. This study aimed to develop and validate a nomogram model for predicting early SIC onset in septic patients. Methods Patients with sepsis admitted to the ICUs of Hebei General Hospital and Handan Central Hospital (East District) between March 1, 2021 and March 1, 2024 were retrospectively analyzed. Patients were categorized into SIC and non-SIC groups based on in-hospital SIC occurrence. Data were randomly split into training (70%) and testing (30%) sets. An external validation cohort included patients from Hebei General Hospital ICU between March 1 and October 31, 2024. All predictors were collected within 24 hours of sepsis diagnosis. Candidate variables were screened using univariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression. A nomogram was then constructed. Model performance was evaluated for discrimination, calibration, and clinical utility using ROC curves, calibration plots, and decision curve analysis (DCA). Results A total of 847 patients were included, of whom 480 (56.7%) developed SIC. The final nomogram incorporated eight predictors: lactate, oxygenation index, total protein, total bilirubin, urea, procalcitonin, activated partial thromboplastin time, and monocyte count. The model demonstrated good predictive performance, with area under the curve (AUC) values of 0.783 (95% CI: 0.746–0.820) in the training set, 0.768 (95% CI: 0.710–0.826) in the testing set, and 0.782 (95% CI: 0.708–0.856) in the validation set. Calibration plots indicated good agreement between predicted and observed risks, and DCA confirmed favorable clinical utility. Conclusions We developed and externally validated a nomogram for early SIC prediction in sepsis. The model demonstrated high accuracy, good calibration, and clinical applicability. By visualizing key risk factors, the nomogram can aid individualized risk assessment and timely intervention, potentially improving patient outcomes.
Keywords: Sepsis-induced coagulopathy, pathogenic factors, nomogram, predictive model, Development and validation
Received: 27 Jun 2025; Accepted: 27 Aug 2025.
Copyright: © 2025 Tan, Zhou, Zhang, Yang, Du, Wang and Cao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yunxing Cao, Critical care department, Chongqing General Hospital, Chongqing, China
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