冠心病患者并发心力衰竭风险预测模型的系统评价

BackgroundHeart failureHF is a major chronic condition that significantly impacts global health. Coronary heart diseaseCHD is the leading cause of HF. Developing risk prediction models for HF in patients with CHD is crucial for enabling healthcare professionals to identify high-risk populations and implement timely interventions. ObjectiveTo systematically evaluate risk prediction models for HF with CHD in Chinese patientsserving as a reference for the developmentselectionand dissemination of relevant predictive models. MethodsCNKIWanfang DataVIPSinoMedPubMedEmbaseWeb of Science and the Cochrane Library were searched for relevant studies on risk prediction models for HF with CHD in Chinese patients up to October 2024. Two reviewers independently screened the literatureextracted dataand assessed the risk of bias and applicability of the included studies using the Prediction Model Risk of Bias Assessment Tool PROBAST. ResultsA total of 27 studies were includedreporting 64 risk prediction models. The area under the receiver operating characteristicROC curveAUC for these models ranged from 0.511 to 0.989with 63 models achieving an AUC>0.7indicating good predictive performance. HoweverPROBAST assessment revealed that all 27 studies had a high risk of bias and low applicability. Key predictive factors included ageleft ventricular ejection fractionhistory of diabeteshistory of hypertensionNT-proBNPand Gensini score. ConclusionThe stability and external validity of existing risk prediction models for HF with CHD in Chinese patients require further validation through prospectivelarge-scale studies. Future model development should adhere strictly to PROBAST guidelines to ensure the design and implementation of high-qualitygeneralizable predictive models.