The Journal of Practical Medicine ›› 2026, Vol. 42 ›› Issue (14): 2521-2532.doi: 10.3969/j.issn.1006-5725.2026.14.005

• Cardiovascular and Cerebrovascular Diseases Column • Previous Articles    

Nomogram prediction model for early acute kidney injury in patients with traumatic brain injury based on a coagulation dysfunction score

Zhongqi ZHANG,Yajie ZHANG,Ziyi LIANG,Junjie XU,Yang LU,Zhuang SHA,Jun LIANG()   

  1. Department of Neurosurgery,the Affiliated Hospital of Xuzhou Medical University,Xuzhou 221002,Jiangsu,China
  • Received:2026-04-19 Online:2026-07-25 Published:2026-08-05
  • Contact: Jun LIANG E-mail:lj1971@126.com

Abstract:

Objective To explore the independent factors associated with early acute kidney injury (AKI) in patients with traumatic brain injury (TBI), construct and validate a nomogram prediction model, and compare its discriminative efficacy with that of random forest and XGBoost models. Methods Clinical data of TBI patients admitted to the Affiliated Hospital of Xuzhou Medical University from January 2023 to December 2025 were retrospectively collected. The patients were then divided into the AKI group and the non-AKI group according to whether AKI occurred within 48 hours after admission. Univariate and multivariate logistic regression analyses were employed to identify the independent factors associated with early AKI. Subsequently, a nomogram was constructed based on the multivariate model. In addition, random forest and XGBoost models were developed, and the discriminative efficacy of these three models was compared through 5-fold cross-validation. Results A total of 368 patients with TBI were included, among whom 54 (14.7%) developed AKI. Multivariate logistic regression analysis demonstrated that age, blood transfusion, mechanical ventilation, systemic infection, and coagulation dysfunction score were independently associated with early AKI, whereas the Glasgow Coma Scale (GCS) score served as a protective factor (all P < 0.05). The nomogram presented excellent calibration, with a mean absolute error of 0.034. Decision curve analysis suggested potential clinical benefits across low-to-moderate threshold probabilities. The 5-fold cross-validation area under the curve (AUC) values were 0.862 for the logistic model, 0.870 for the random forest model, and 0.852 for the XGBoost model. According to DeLong tests, there were no statistically significant differences among the three models. SHAP analysis disclosed the relative contribution and importance ranking of each variable. Conclusions The logistic nomogram model, which is based on age, GCS score, blood transfusion, mechanical ventilation, systemic infection, and coagulation dysfunction score, demonstrated excellent discrimination, calibration, and significant potential clinical reference value. It can offer a reliable reference for the early identification of AKI risk and the individualized management of TBI patients in neurosurgical practice. Considering that this was a single-center retrospective study with internal validation, the model still needs to be further validated in multicenter prospective cohorts.

Key words: traumatic brain injury, acute kidney injury, coagulation dysfunction score, nomogram, machine learning

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