Risk prediction model for in-stent restenosis following PCI: a systematic review

Abstract

IntroductionThe morbidity and mortality rates of coronary heart disease are significant, with PCI being the primary treatment. The high incidence of ISR following PCI poses a challenge to its effectiveness. Currently, there are numerous studies on ISR risk prediction models after PCI, but the quality varies and there is still a lack of systematic evaluation and analysis.MethodsTo systematically retrieve and evaluate the risk prediction models for ISR after PCI. A comprehensive search was conducted across 9 databases from inception to March 1, 2024. The screening of literature and extraction of data were independently carried out by two investigators, utilizing the checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS). Additionally, the risk of bias and applicability were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).ResultsA total of 17 studies with 29 models were included, with a sample size of 175–10,004 cases, and the incidence of outcome events was 5.79%–58.86%. The area under the receiver operating characteristic curve was 0.530–0.953. The top 5 predictors with high frequency were diabetes, number of diseased vessels, age, LDL-C and stent diameter. Bias risk assessment into the research of the risk of higher bias the applicability of the four study better.DiscussionThe overall risk of bias in the current ISR risk prediction model post-PCI is deemed high. Moving forward, it is imperative to enhance study design and specify the reporting process, optimize and validate the model, and enhance its performance.