通讯作者: 邹圣强▲, 1210xyz@163.com

DOI：10.12201/bmr.202603.00015

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

• 摘要：目的 构建严重颅脑损伤患者应激性高血糖的列线图模型并验证。方法 选取2020年1月至2024年8月江苏大学附属人民医院收治的262例严重颅脑损伤患者作为研究对象，采取单因素、多因素Logistic回归分析筛选特征变量，构建列线图模型，病通过Bootstrap重抽样法验证模型效能。结果 262例严重颅脑损伤患者中，有111例患者发生应激性高血糖，发生率为42.37%；影响严重颅脑损伤患者发生应激性高血糖的危险因素为血红蛋白、使用葡萄糖、使用机械通气；构建的列线图模型受试者工作特征曲线下面积0.724（95%CI：0.662～0.786），校准曲线（斜率=0.95）显示预测概率与实际观察概率具有良好的一致性，决策曲线图显示该模型有较高的临床净获益，列线图模型具有较高的准确性、区分度和临床实用性。结论 本研究构建了包含3个因素的严重颅脑损伤患者应激性高血糖风险预测模型，并使用列线图进行可视化，在训练人群中具备良好的区分度和校准度，临床适用范围较好。

  关键词： 颅脑损伤；应激性高血糖；列线图；预测模型

   

  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

  提交时间：2026-03-07

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
• html

• 图表

• 武晓梦, 李思娴, 张明爽, 贾圣楠, 戴佳航, 孙一丹, 谭冬, 袁宇. 外周血内皮祖细胞在颅脑损伤血管新生与神经修复中的作用机制及研究进展. 2026. doi: 10.12201/bmr.202601.00072

  胡欣. 剖宫产术中寒战风险预测模型及列线图的构建. 2025. doi: 10.12201/bmr.202501.00053

  罗智熹, 张磊. 目标体温管理对重型颅脑损伤脑脊液IL-6影响的研究. 2025. doi: 10.12201/bmr.202501.00079

  张二利, 何兰兰, 李丹阳, 沈立, 吴钟华, 张军, 叶永强. 基于脑小血管病影像学总负荷的急性脑梗死患者住院时间延长列线图模型构建与验证. 2025. doi: 10.12201/bmr.202501.00008

  付思思. 非小细胞肺癌患者肺叶切除术后恶心呕吐Nomogram分析模型构建与验证. 2025. doi: 10.12201/bmr.202507.00001

  徐鹏, 沈思, 朱佳辉, 夏森林, 徐华. 基于可解释机器学习的老年严重创伤并发急性呼吸窘迫综合征 预测模型构建及效能评估. 2026. doi: 10.12201/bmr.202602.00094

  蔡欣欣, 吴夏阳. 早产儿入院败血症风险预测模型的构建与验证. 2025. doi: 10.12201/bmr.202505.00013

  朱颖君, 郭馨瑶, 杨爽, 冯艳红. 基于超声自动应变技术构建射血分数保留的心衰列线图诊断模型的研究. 2025. doi: 10.12201/bmr.202512.00047

  魏乾坤, 吴芸, 徐新燕, 魏梦珂, 曾智萍, 代宇宁, 帕提曼·艾尼瓦尔, 张晶. 继发性不孕患者IVF-ET临床妊娠的预测模型构建. 2025. doi: 10.12201/bmr.202506.00074

  施明杰, 叶洪舟, 袁琛. 大环内酯类耐药肺炎支原体肺炎患儿发展为重症肺炎的危险因素分析及预测模型构建. 2024. doi: 10.12201/bmr.202409.00021

• 序号	提交日期	编号	操作
  1	2026-02-04	10.12201/bmr.202603.00015V1	下载

• 公开评论  匿名评论  仅发给作者 提交评论