上海交通大学学报（医学版）

›› 2011, Vol. 31 ›› Issue (11): 1592-.doi: 10.3969/j.issn.1674-8115.2011.11.019

• 论著（临床研究） • 上一篇    下一篇

脑外伤患者预后预测模型的建立及验证

袁 方, 丁 军, 郭 衍, 高文伟, 王 敢, 陈世文, 陈 浩, 田恒力   

  1. 上海交通大学附属第六人民医院神经外科, 上海 200233
  • 出版日期:2011-11-28 发布日期:2011-11-29
  • 作者简介:袁 方（1988—）, 男, 硕士；电子信箱: yf021025@126.com。
  • 基金资助:

    上海市科委重点项目(10JC1412500)

Development and validation of prognostic models for patients with traumatic brain injury

YUAN Fang, DING Jun, GUO Yan, GAO Wen-wei, WANG Gan, CHEN Shi-wen, CHEN Hao, TIAN Heng-li   

  1. Department of Neurosurgery, the Sixth People's Hospital, Shanghai Jiaotong University, Shanghai 200233, China
  • Online:2011-11-28 Published:2011-11-29
  • Supported by:

    Shanghai Science and Technology Committee Foundation, 10JC1412500

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摘要：

目的 建立并验证预测脑外伤患者30 d内死亡率及6个月预后不良率的预测模型。方法 收集1 016例中重度脑外伤患者临床资料用作预测模型建立数据。系统分析入院相关危险因素与预后关系，Logistic回归建立不同的预测模型，通过拟合优度检验和计算C统计值(ROC曲线下面积)观察模型性能。内、外部验证模型并最终确定预测模型，开发脑外伤患者预后预测工具。结果 Logistic回归分析表明高龄、瞳孔对光反射消失、运动GCS评分下降、异常CT特征和异常常规实验室检查是脑外伤患者预后不良的独立危险因素。基于入院危险因素建立的预测模型性能良好（拟合优度检验P>0.05, C统计值0.709～0.882）。内部验证表明模型无过度乐观，外部验证证实预测模型外部适用性强（拟合优度检验P>0.05,C统计值0.844～0.992）。结论 建立的预测模型可以早期、简单且准确地预测脑外伤患者的预后，脑外伤患者预后预测工具可用于临床辅助临床决策的制定。

关键词: 预测模型, 预测, 脑外伤, 验证, 预后

Abstract:

Objective To develop and validate prognostic models for mortality within 30 d after traumatic brain injury and for 6-month unfavorable prognosis after traumatic brain injury. Methods The clinical data of 1 016 patients with moderate to severe traumatic brain injury were collected to develop prognostic models. The relationship between admission-related risk factors and prognosis was systematically analysed, different prognostic models were established with Logistic regression analysis, and the performance of these models was assessed with goodness-of-fit test and C statistic (area under the receiver operating characteristic curve). These models were internally and externally validated, the ultimate prognostic model was determined to serve as the tool of prognostic evaluation for patients with traumatic brain injury. Results Logistic regression analysis revealed that old age, loss of pupillary light reflex, decreased motor Glasgow Coma Score, abnormal CT features and abnormal routine laboratory findings were independent risk factors for unfavorable prognosis in patients with traumatic brain injury. The prognostic model based on the risk factors of admission had favorable performance (P>0.05 for goodness-of-fit test, 0.709-0.882 for C statistic). No overoptimism was revealed by internal validation, and the external validity was proved to be better by external validation (P>0.05 for goodness-of-fit test, 0.844-0.922 for C statistic). Conclusion The established model, which is convenient to manage, can timely and accurately predict the prognosis of patients with traumatic brain injury, and can help in decision-making in clinics.

Key words: prognostic model, prediction, traumatic brain injury, validation, prognosis