Objectives:To systematically evaluate the risk prediction models of device-related infection(DRI)in cardiac implantable electronic device(CIED)patients after implantation,and to provide reference for clinical practice and future scientific research.Methods:A computer-based search was conducted from 2 December 2023 through PubMed,Embase,Web of Science,Cochrane Library,CINAHL,China Biology Medicine,China National Knowledge Infrastructure,Wanfang,and VIP database to identify literatures related to the risk prediction model of DRI in patients with CIED.Two researchers independently screened the literature,extracted data,and completed the risk of bias and applicability evaluation of the included literature.Results:A total of 16 studies were included.The overall applicability of the models was good,but the risk of bias was high,and the area under the curve(AUC)of the receiver operating characteristic(ROC)ranged from 0.67 to 0.96.Eleven studies completed internal validation,and five studies underwent external validation.Bag and or electrode reset/device upgrade,renal insufficiency or renal failure,age,implantable cardioverter-defibrillators(ICD)or cardiac resynchronization therapy(CRT),and use of anticoagulant medications were valid predictors of device infection in CIED patients.Conclusions:The overall performance of the DRI-related risk prediction models for CIED patients is good,with good applicability but high risk of bias.It is necessary to improve the quality of the study in terms of data sources,variable screening,and model evaluation,to conduct prospective cohort studies,to improve the external validation of the existing models,and to actively develop local prediction model tools applicable to Chinese population.
NETL
NSTL
万方数据
