Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026
[Construction of an Early-stage Risk Prediction Model for Trauma-Induced Coagulopathy].
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Xu Xu, He Meina, Shao Min, Liu Xin, Yi Qi, Zhang Bingyan, Huang Ying, Tan Chaochao
Аннотация
OBJECTIVE: Based on prospectively collected early clinical and laboratory data from trauma patients at admission, a risk prediction model for the early assessment of trauma-induced coagulopathy (TIC) in emergency trauma patients was constructed and validated.
METHODS: This study analyzed the clinical data and laboratory results of 285 emergency trauma patients admitted between January 2024 and December 2024. The patients were randomly divided into a training set (n = 199) and a test set (n = 86) at a 7∶3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of TIC and to construct a risk prediction model. The diagnostic efficacy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The calibration curve was plotted using the Bootstrap method to assess calibration, and the clinical net benefit was evaluated by decision curve analysis (DCA).
RESULTS: Multivariate logistic regression analysis identified head trauma, mean arterial pressure (MAP), prothrombin time (PT), and thrombin time (TT) as independent predictors of TIC, and a predictive model was developed. The AUC of the model was 0.804 (95% CI: 0.737-0.871) in the training set and 0.847 (95% CI: 0.767-0.927) in the test set. The calibration curve showed a high level of agreement between the predicted and actual probabilities. DCA indicated that the model provided significant clinical net benefit across a broad range of risk thresholds (0.2-1.0).
CONCLUSION: This study developed and validated a TIC risk prediction model that demonstrated excellent early predictive efficacy for TIC in emergency trauma patients.
METHODS: This study analyzed the clinical data and laboratory results of 285 emergency trauma patients admitted between January 2024 and December 2024. The patients were randomly divided into a training set (n = 199) and a test set (n = 86) at a 7∶3 ratio. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of TIC and to construct a risk prediction model. The diagnostic efficacy of the model was evaluated by the area under the receiver operating characteristic curve (AUC). The calibration curve was plotted using the Bootstrap method to assess calibration, and the clinical net benefit was evaluated by decision curve analysis (DCA).
RESULTS: Multivariate logistic regression analysis identified head trauma, mean arterial pressure (MAP), prothrombin time (PT), and thrombin time (TT) as independent predictors of TIC, and a predictive model was developed. The AUC of the model was 0.804 (95% CI: 0.737-0.871) in the training set and 0.847 (95% CI: 0.767-0.927) in the test set. The calibration curve showed a high level of agreement between the predicted and actual probabilities. DCA indicated that the model provided significant clinical net benefit across a broad range of risk thresholds (0.2-1.0).
CONCLUSION: This study developed and validated a TIC risk prediction model that demonstrated excellent early predictive efficacy for TIC in emergency trauma patients.