Risk prediction models for periprosthetic joint infection after total joint arthroplasty:a systematic evaluation
10.12138/j.issn.1671-9638.20257254
- VernacularTitle:全关节置换术后假体周围感染风险预测模型的系统评价
- Author:
Jiao SHAN
1
;
Wei HUAI
;
Xiaoyuan BAO
;
Meng JIN
;
Yulong CAO
;
Hong LI
Author Information
1. 首都医科大学附属北京积水潭医院医院感染管理处,北京 100035
- Publication Type:Journal Article
- Keywords:
total joint arthroplasty;
periprosthetic joint infection;
risk;
prediction model;
systematic evaluation;
PJI
- From:
Chinese Journal of Infection Control
2025;24(8):1066-1074
- CountryChina
- Language:Chinese
-
Abstract:
Objective To systematically evaluate the research progress of risk prediction models for periprosthetic joint infection(PJI)after total joint arthroplasty(TJA),analyze the limitations of current researches,and propose optimized suggestions.Methods Chinese and English databases such as PubMed,Embase,Web of Science,Co-chrane Library,SinoMed,Wanfang Database,VIP Database,and CNKI were retrieved systematically.The re-trieved period was from the establishment of each database to August 31,2024.Two researchers independently screened literatures and extracted data according to the CHARMS checklist,and the risk of bias in the included studies was evaluated by the PROBAST tool.Results A total of 14 studies were included in this study,involving 17 prediction models.The most common predictors included history of diabetes mellitus,obesity(body mass index[BMI]≥30 kg/m2),advanced age(≥65 years old),history of traumatic fracture,and prolonged operation time(≥2 hours).All of the included studies had high risks of bias,mainly study subject selection bias(such as single-center sample)and statistical analysis bias(such as unadjusted confounding factors).Conclusion Most of the cur-rently published risk prediction models for PJA after TJA have good predictive performance,however,there are sig-nificant limitations in the research design,especially in the insufficient control of bias risk.Future research needs to focus on improving methodological design,including adoption of prospective multi-center studies,definition of standardized predictive variables,and sufficient adjustment of confounding factors.
