[1]赵志杰江璞玉邵宇豪李新宇冯东福.基于急诊入院指标的颅脑损伤病人早期预后多维度预测模型的建立[J].中国临床神经外科杂志,2022,27(02):90-94.[doi:10.13798/j.issn.1009-153X.2022.02.008]
　ZHAO Zhi-jie,JIANG Pu-yu,SHAO Yu-hao,et al.Establishment and validation of a prognosis prediction model using emergency admission data for patients with traumaticbrain injury[J].,2022,27(02):90-94.[doi:10.13798/j.issn.1009-153X.2022.02.008]
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基于急诊入院指标的颅脑损伤病人早期预后多维度预测模型的建立()

《中国临床神经外科杂志》[ISSN:1009-153X/CN:42-1603/TN]

卷:
27
期数:
2022年02期
页码:
90-94
栏目:
论著
出版日期:
2022-02-25

文章信息/Info

Title:
Establishment and validation of a prognosis prediction model using emergency admission data for patients with traumaticbrain injury
文章编号:
1009-153X（2022）02-0090-05
作者:
赵志杰江璞玉邵宇豪李新宇冯东福
201999 上海，上海交通大学医学院附属第九人民医院神经外科（赵志杰、江璞玉、邵宇豪、李新宇、冯东福）；201999 上海，上海交通大学医学院创伤医学研究所（冯东福）
Author(s):
ZHAO Zhi-jie1;  JIANG Pu-yu1;  SHAO Yu-hao1;  LI Xin-yu1;  FENG Dong-fu1; 2.
1. Department of Neurosurgery, The Ninth People’s Hospital, Medical School, Shanghai Jiao Tong University, Shanghai 201999, China; 2. Institute of Traumatic Medicine, Medical School,Shanghai Jiao Tong University, Shanghai 201999, China
关键词:
颅脑损伤; 早期预后; 预测模型; 急诊入院指标
Keywords:
Traumatic brain injury;  Predictive model;  Thirty-day outcome;  Emergency admission data
分类号:
R 739.41; Q 786
DOI:
10.13798/j.issn.1009-153X.2022.02.008
文献标志码:
A
摘要:
目的 构建一种结合病人急诊入院时基线特征、CT影像指标和血生化指标的预测模型，以评估颅脑损伤（TBI）病人伤后30 d预后。方法选择2013年4月至2020年8月收治的666例TBI，其中2013年4月至2018年4月收治的557例为训练组，2018年5月至2020年8月收治的109例为测试组。伤后30 d，使用GOSE评分进行预后评估，5~8分为预后良好，1~4分为预后不良。使用单因素logistic回归和LASSO回归分析筛选最终纳入模型的因素，并采用多因素logistic回归分析构建预测模型。C指数、校准曲线和决策曲线分析评估模型的临床适用性，并用测试组进行验证。结果发病30 d，训练组预后良好470例，预后不良87例；测试组预后良好87例，预后不良22例。单因素logictic回归和LASSO回归筛选出三类预测因素：基线特征（GCS评分、年龄、瞳孔反射），CT特征（中线移位、蛛网膜下腔出血、基底池状态），血生化指标（单核细胞计数、血红蛋白、血糖）。C指数、校准曲线和决策曲线分析显示多因素logistic回归构建的预测模型具有良好的临床应用价值。结论我们构建了一个基于TBI病人急诊入院时基线特征、CT特征和血生化指标的预测模型，对TBI病人30 d预后具有较好的区分度。
Abstract:
Objective To develop a predictive model using emergency admission data and to assess its value for the preditiction of 30-day outcomes of the patients with traumatic brain injury (TBI). Methods The emergency admission data of 666 patients with TBI who were admitted to our hospital from April 2013 to August 2020, including baseline data, CT features and blood biochemical test data, were collected retrospectively. Of 666 patients, 557 patients who were admitted to our hospital from April 2013 to April 2018 were divided into the training group and 109 patients who were admitted from May 2018 to August 2020 were divided into the test group. The 30-day outcomes were assessed using GOSE score, with score of 5~8 as good prognosis and score of 1~4 as poor prognosis. Univariate logistic regression analysis and LASSO regression analysis were used to screen the factors of the training group that were finally included in the model, and multivariate logistic regression analysis was used to construct a predictive model. C-index, calibration curve, and decision curve analysis were used to assess the clinical applicability of the model and the data of test group were used to validate the model. Results On the 30th day of onset, 470 patients had a good prognosis and 87 patients had a poor prognosis in the training group; 87 patients had a good prognosis and 22 patients had a poor prognosis in the test group. Univariate logistic and LASSO regression screened out three types of data: baseline characteristics (admission GCS score, age, pupillary reflex), CT characteristics (midline shift, subarachnoid hemorrhage, basal cistern status), blood biochemical markers (monocytic cell count, hemoglobin, blood glucose). The C index, calibration curve and decision curve analyses showed that the model had a good value in the asseeement of TBI patients outcomes. Conclusions We constructed a prediction model based on the baseline characteristics, CT characteristics and blood biochemical test data of TBI patients on emergency admission, which has a good value in the assessment of the 30-day outcomes of TBI patients.

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备注/Memo

备注/Memo:
基金项目：上海市科委基金（19411968200）；上海市浦东新区卫计委联合攻关项目（PD2018D-09）；上海交通大学医学院附属第九人民医院临床研究项目（JYLJ201802）
通讯作者：冯东福，E-mail：feng_df@yahoo.com
更新日期/Last Update: 1900-01-01