Construction and Validation of a Predictive Model for Stress Hyperglycemia in Patients with Severe Traumatic Brain Injury

Corresponding author: ZOU shengqiang, 1210xyz@163.com

DOI: 10.12201/bmr.202603.00015

•  

  Abstract: Objective To establish and validate a curvilinear model of stress hyperglycemia in patients with severe traumatic brain injury.Methods A total of 262 patients with severe traumatic brain injury admitted to the Affiliated Peoples Hospital of Jiangsu University from January 2020 to August 2024 were selected as study subjects. Univariate and multivariate logistic regression analyses were performed to identify characteristic variables, and a regression model was constructed. The models efficacy was validated using the Bootstrap resampling method.Ｒesults Among 262 patients with severe traumatic brain injury, 111 developed stress hyperglycemia, yielding an incidence rate of 42.37%. Risk factors influencing the occurrence of stress hyperglycemia in these patients included hemoglobin levels, glucose administration, mechanical ventilation use. For the test group, the constructed nomogram model was found to have an area under the receiver operating characteristic curve (AUC) of 0.724 (95% CI: 0.662～0.786). Good agreement between predicted and observed probabilities was demonstrated by the calibrated curve (slope = 0.95). High clinical net benefit for this model was indicated by the decision curve analysis. High accuracy, discriminatory power, and clinical utility were exhibited by the logit model.Conclusion This study constructed a predictive model for stress hyperglycemia risk in patients with severe traumatic brain injury, incorporating three factors. The model was visualized using a nomogram and demonstrated good discrimination and calibration in the training population, indicating favorable clinical applicability.

  Key words: traumatic brain injury；stress hyperglycemia；Nomogram；prediction model

  Submit time: 7 March 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
• html

• 图表

• lisixian, zhangmingshuang, jiashengnan, daijiahang, sunyidan, tan dong, yuanyu. Mechanisms and Research Progress of Peripheral Blood Endothelial Progenitor Cells in Angiogenesis and Neural Repair After Traumatic Brain Injury. 2026. doi: 10.12201/bmr.202601.00072

  Construction and application evaluation of risk prediction model and nomogram for shivering during cesarean sectio. 2025. doi: 10.12201/bmr.202501.00053

  zhanglei. Effect of targeted temperature management on cerebrospinal fluid IL-6 in patients with severe craniocerebral injury. 2025. doi: 10.12201/bmr.202501.00079

  HE Lanlan, LI Danyang, SHEN Li, WU Zhonghua, ZHANG Jun, YE Yongqiang. Construction and validation of nomogram model for prolonged length of stay in patients with acute cerebral infarction based on total cerebral small vessel disease burden scores. 2025. doi: 10.12201/bmr.202501.00008

  付思思. Construction and validation of Nomogram analysis model for nausea and vomiting after lobectomy in non-small cell lung cancer patients. 2025. doi: 10.12201/bmr.202507.00001

  Shen Si, Zhu Jiahui, Xia Senlin, Xu Hua. A predictive model and performance evaluation for acute respiratory distress syndrome in the elderly patients with severe trauma based on interpretable machine learning. 2026. doi: 10.12201/bmr.202602.00094

  WU Xiayang. Establishment and verification of a risk prediction model for neonatal sepsis in premature infants. 2025. doi: 10.12201/bmr.202505.00013

  guoxinyao, yanshuang, fengyanhong. . 2025. doi: 10.12201/bmr.202512.00047

  WU Yun, XU Xinyan, WEI Mengke, ZENG Zhiping, DAI Yuning, PATIMAN Ainiwaer, ZHANG Jing. Predictive modeling of IVF-ET clinical pregnancy in patients with secondary infertility. 2025. doi: 10.12201/bmr.202506.00074

  YE Hongzhou, YUAN Chen. Risk factors of severe pneumonia in children with macrolide-resistant Mycoplasma pneumoniae pneumonia and the construction of prediction model. 2024. doi: 10.12201/bmr.202409.00021

• ID	Submit time	Number	Download
  1	2026-02-04	10.12201/bmr.202603.00015V1	Download

• Public  Anonymous  To author only Submit Comments