What type of study is this?

This is a Validation Study study.

What question did this study set out to answer?

To establish a machine learning model predicting 28-day mortality risk in patients with sepsis-induced coagulopathy.

February 7, 2026BMC Medical Informatics and Decision Making0 citationsOpen Access

Explainable machine learning model for predicting short-term outcomes in sepsis- induced coagulopathy

JWJianmin WuShenyang Pharmaceutical University XZXianwei ZhangWenzhou City People's Hospital CLChenglong LiangChonnam National University

Key Points

To establish a machine learning model predicting 28-day mortality risk in patients with sepsis-induced coagulopathy.
Data collection from the Medical Information Mart for Intensive Care IV Database
Model training and external validation using patient data from the First Affiliated Hospital of Wenzhou Medical University
Utilization of LASSO regression and logistic regression analysis to identify predictive factors
Evaluation of models with ROC curves, calibration curves, and Decision Curve Analysis
Interpretation of models using Shapley Additive Explanations (SHAP)
The XGBoost model achieved an AUC of 0.840 in the test set and 0.864 in external validation.
Model accuracy was 0.807, sensitivity 0.836, and specificity 0.798.
The developed models provide robust predictive performance for assessing mortality risk in SIC patients.

Abstract

Sepsis-Induced coagulopathy (SIC) is not only a common complication in the development process of sepsis but also related to poor prognosis of sepsis. We aimed to establish a machine learning (ML) model to predict the 28-day mortality risk of patients with SIC. We collected data for model training from the Medical Information Mart for Intensive Care IV Database version 2.2 to establish the model. We extracted patient data from the First Affiliated Hospital of Wenzhou Medical University for the model’s external validation. We used Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression analysis to identify predictive factors for a 28-day mortality risk. Then, we built prognostic prediction models for SIC patients using multiple ML classification models. We evaluated predictive performance using Receiver Operating Characteristic (ROC) curves, calibration curves, and Decision Curve Analysis (DCA). We used Shapley Additive Explanations (SHAP) to interpret the models. We selected seventeen variables for model development, and the XGBoost model performed the best. The area under the curve (AUC) (95% CI) of the test set reached 0.840 (0.810–0.870), with an accuracy of 0.807, sensitivity of 0.836, and specificity of 0.798. The model also demonstrated excellent predictive performance in external validation, with an AUC (95% CI) of 0.864 (0.794–0.934). We constructed an XGBoost model and provided model interpretability using the SHAP. This model provides a basis for assessing the 28-day mortality risk of patients with SIC, aiding in clinical decision support and the formulation of personalized treatment strategies.

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