Principal component analysis -Logistic regression model in predicting acquired pneumonia in patients with craniocerebral injury
10.3760/cma.j.issn.1671-8925.2018.12.008
- VernacularTitle:PCA-Logistic回归模型预测颅脑损伤患者临床预后的应用研究
- Author:
Jinzhou FENG
1
,
2
;
Fajian LIU
;
Yongqin KUANG
;
Hua JIANG
Author Information
1. 610110 成都,四川省医学科学院·四川省人民医院(东院)神经外科
2. 610110 成都,四川省医学科学院·四川省人民医院(东院)创伤与代谢组多学科实验室
- Keywords:
Hospital-acquired pneumonia;
Severe traumatic brain injury;
Principal component analysis;
Logistic regression
- From:
Chinese Journal of Neuromedicine
2018;17(12):1234-1240
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the principal component analysis (PCA)-Logistic regression model in predicting hospital-acquired pneumonia (HAP) in patients with craniocerebral injury, and find the influencing factors of mortality and HAP occurrence in patients with craniocerebral injury. Methods One hundred and eight patients with craniocerebral injury, admitted to our hospital from December 2011 to November 2017, were chosen in our hospital. Clinical diagnoses, 36 treatment indicators and laboratory results were constituted the original data set; 12 principal components with cumulative contribution>2/3 were extracted as independent variables, and mortality and HAP occurrence were as dependent variables to establish PCA-Logistic regression model. Receiver operating characteristic (ROC) curve was applied to forecast performance of PCA-R model. Results PCA-Logistic regression showed that 4 principal components had significant influence in mortality, and 5 principal components had significant influence in HAP outcomes. Open craniocerebral injury and coagulation changes were the clinical indexes with the highest coefficient when mortality was the outcome index. Gender and parenteral nutrition were the clinical indexes with the highest coefficient when HAP was outcome index. PCA-R model was able to identify the risk factors and forecast the clinical outcomes (HAP, sensitivity:83.9％, specificity: 94.8％, area under the curve [AUC]: 0.949; mortality, sensitivity: 92.3％, specificity:93.7％, AUC: 0.983). Conclusion PCA-Logistic regression model can effectively mine the clinical variables of patients with craniocerebral injury; insufficiency of blood perfusion after severe craniocerebral injury is an important factor affecting the survival of patients, and abnormal nutritional support may be an important clinical factor affecting the occurrence of HAP in patients.
