[1]冯金周　刘发健　匡永勤　江　华.颅脑损伤院内获得性肺炎预测：一项基于机器学习的PCA-Logistic回归分析模型[J].中国临床神经外科杂志,2019,(01):35-38.[doi:10.13798/j.issn.1009-153X.2019.01.010]
　FENG Jin-zhou,LIU Fa-jian,KUANG Yong-qin,et al.Prediction of hospital-acquired pneumonia after traumatic brain injury based on the PCA-Logistic regression analysis model[J].,2019,(01):35-38.[doi:10.13798/j.issn.1009-153X.2019.01.010]
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颅脑损伤院内获得性肺炎预测：一项基于机器学习的 PCA-Logistic回归分析模型()
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《中国临床神经外科杂志》[ISSN:1009-153X/CN:42-1603/TN]

卷:
期数:
2019年01期
页码:
35-38
栏目:
论著
出版日期:
2019-01-25

文章信息/Info

Title:
Prediction of hospital-acquired pneumonia after traumatic brain injury based on the PCA-Logistic regression analysis model
文章编号:
1009-153X（2019）01-0035-04
作者:
冯金周　刘发健　匡永勤　江　华
Author(s):
FENG Jin-zhou1;  LIU Fa-jian1;  KUANG Yong-qin2;  JIANG Hua3.
1. Department of Neurosurgery, Sichuan Provincial People’s Hospital, Chengdu 610101, China; 2. Department of Neurosurgery, General Hospital, Western Command, PLA, Chengdu 610083, China; 3. Metabolomics and Multidisciplinary Laboratory for Trauma Research,
关键词:
颅脑损伤; 院内获得性肺炎; 主成分分析; Logistic回归分析
Keywords:
Traumatic brain injury;  Hospital-acquired pneumonia;  Principal component analysis;  Logistic regression analysis
分类号:
R651.1+5
DOI:
10.13798/j.issn.1009-153X.2019.01.010
文献标志码:
A
摘要:
目的 探讨PCA-Logistic回归分析模型在颅脑损伤病人院内获得性肺炎（HAP）预测建模中的应用效果。方法 收集2011年12月至2017年11月开颅手术治疗的108例颅脑损伤的相关临床数据，建立PCA-Logistic回归分析模型，利用受试者工作特征（ROC）曲线评估模型预测效果。结果 PCA-Logistic回归分析模型发现影响病人HAP发生的重要临床指标，经ROC曲线评估PCA-Logistic回归分析模型预测HAP结局具有较高的预测效能，灵敏度为83.9%，特异度为94.8%，曲线下面积为0.949。结论 PCA-Logistic回归分析模型可以有效的挖掘颅脑损伤后的临床变量，可建立HAP的预测模型，不规范的肠外营养营养支持可能是影响HAP发生的重要临床因素。
Abstract:
Objective To explore the application of principal components analysis (PCA)-Logistic regression analysis to prediction of the hospital-acquired pneumonia (HAP) in the patients with traumatic brain injury (TBI), and to find the pathophysiological patterns and important risk factors related to their clinical prognoses. Methods Original dataset was constituted by the data of 108 patients with TBI derived from the Database of the Trauma Center of Sichuan Provincial People’s Hospital from 2011 to 2017. The primary outcome was HAP. After the dataset evaluation and cleaning, the PCA-Logistic regression model was built to identify the risk factors related to HAP. Receiver Operating Characteristic (ROC) curve was used to evaluate the PCA-Logistic regression model. Results The PCA-Logistic regression model analysis found the important clinical indicators affecting the patients HAP, and the PCA-Logistic regression model was evaluated by ROC curve. The HAP outcome model had good predictive power (sensitivity, 83.9%; specificity, 94.8%; AUC, 0.949). Conclusions PCA-Logistic regression analysis can effectively mine the clinical variables of the patients with TBI and establish a clinical prognosis prediction model. The abnormal parenteral nutrition support after severe TBI may be an important clinical factor affecting the occurrence of HAP in the patients with TBI.

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备注/Memo

备注/Memo:
doi:10.13798/j.issn.1009-153X.2019.01.010 基金项目：四川省卫生厅科研资助项目（110212，16PJ447） 作者单位：610110 成都，四川省医学科学院·四川省人民医院（东院）神经外科（冯金周、刘发健），创伤与代谢组多学科实验室（江　华）；610083 成都，中国人民解放军西部战区总医院神经外科（匡永勤） 通讯作者：江　华，E-mail：feng720930@126.com
更新日期/Last Update: 2019-01-25