Establishment and validation of risk prediction scoring model of central line-associated bloodstream infection in elderly patients
10.12138/j.issn.1671-9638.20194226
- VernacularTitle:老年患者中央导管相关血流感染风险预测评分模型构建与验证
- Author:
Li-Hong WANG
1
;
Nan WEI
;
Xia ZHAO
;
Jing-Li ZHANG
;
Hui-Jie ZHAO
;
Wen-Hui MA
;
Xu HAN
Author Information
1. 首都医科大学宣武医院医院感染管理处
- Keywords:
central line-associated bloodstream infection;
risk prediction;
risk factor;
elderly patient;
scoring model
- From:
Chinese Journal of Infection Control
2019;18(3):225-231
- CountryChina
- Language:Chinese
-
Abstract:
Objective To establish the risk prediction scoring model of central line-associated bloodstream infection (CLABSI) in elderly patients, provide basis for screening high-risk population, and effectively prevent and control bloodstream infection (BSI) in elderly patients.Methods According to the inclusion and exclusion criteria, data of elderly patients who underwent central catheterization during hospitalization from January 1, 2015 to December 31, 2017 were collected, patients were randomly divided into modeling group and verification group according to the ratio of 7∶3 (random seed was 20180708), risk factors of data of modeling group were distinguished, logistic regression model was constructed. The corresponding score of each risk factor was assigned according to β value, infection risk scoring model was established, prediction accuracy of model was evaluated by receiver operating characteristic (ROC) curve; according to the established infection risk scoring model, cases in validation group were scored, prediction accuracy of model was evaluated by ROC curve. Decision curve was constructed using R software.Results Logistic regression analysis showed that the number of operation≥3 times, length of stay in intensive care unit (ICU) ≥2 days, duration of indwelling central venous catheterization≥7 days, and use of antimicrobial agents were independent risk factors for CLABSI in elderly patients; the corresponding points in risk prediction scoring model were 3, 4, 4, and 9 respectively, and the score of 13-17 points were high-risk population of CLABSI; the area of ROC curve (AUC) was 0.74 in the modeling group; ROC curve was plotted based on the risk score of patients in validation group, AUC was 0.70. The decision curve showed that the net benefit of the risk scoring model was higher in the high risk thershold of 0.01-0.05.Conclusion The established risk prediction scoring model has good discriminant validity and application value, and can be used in the identification of susceptible high risk population of CLABSI in elderly patients, so as to achieve early prevention and control.
