Predictive models for lung infections in elderly patient with hip fracture:a systematic review
10.3969/j.issn.1671-8283.2025.02.011
- VernacularTitle:老年髋部骨折患者肺部感染风险预测模型的系统评价
- Author:
Wanjing ZHANG
1
;
Liu YANG
;
Daxue ZHANG
;
Qiuyu HUANG
;
Jinyan CHE
;
Ning ZHANG
;
Shiwei YANG
Author Information
1. 广西中医药大学护理学院,广西 南宁,530200
- Publication Type:Journal Article
- Keywords:
elderly;
hip fracture;
pulmonary infection;
predictive model;
systematic review
- From:
Modern Clinical Nursing
2025;24(2):83-90
- CountryChina
- Language:Chinese
-
Abstract:
Objective To systematically evaluate the published models in prediction of the risk of lung infections in elderly patients with hip fracture so as to provide a guidance for medical workers in selection or development of suitable risk prediction models.Methods Relevant studies were searched from databases including CNKI,Wanfang Data,VIP,SinoMed,PubMed,Web of Science,Cochrane Library,Embase and CINAHL,from the inception to 31st January,2024.Data were extracted from the selected literature and a bias assessment tool of risk predictive model was used to evaluate the risk of bias and applicability of the included literature.Results A total of 1,035 articles were retrieved,of which seven studies involving 13 predictive models were finally included after screening.The sample sizes ranged from 305 to 2,669 cases and lung infection rates ranged from 5.40%to 20.02%.The repeatedly reported predictors included age,gender,chronic obstructive pulmonary disease,hypoproteinaemia,American Society of Anesthesiologists(ASA)Physical Status Classification and white blood cell count.In the 13 models constructed,the reported area under the curve(AUC)of subjects'job characteristics ranged from 0.667 to 0.996.Five out of seven studies had good overall applicability,but all with high risk of bias.Conclusion The predictive models for lung infections in elderly patients with hip fracture are still in the stage of development.Although the predictive models show some predictive performance,however they are still deficient,and all studies have been found with a high risk in bias.
