Risk-Prediction Model of Restenosis after Endovascular Treatment for Peripheral Arterial Disease: A Systematic Review and Meta-analysis.

Abstract

Background: Peripheral artery disease (PAD) patients after endovascular treatment (EVT) have a relatively high restenosis rate. However, this risk can be mitigated through precise risk assessment and individualized self-management intervention plans. Moreover, the number of predictive models for restenosis risk in PAD patients after EVT is gradually increasing, yet these results of study exhibit certain discrepancies, raising uncertainties regarding the quality and applicability in clinical practice and future research.

Objective: The objective of this study was to systematically evaluate risk-predictive models for restenosis in patients with PAD after EVT.

Methods: A systematic review and meta-analysis of predictive model construction and validation using observational studies was undertaken. The China National Knowledge Infrastructure, China Science and Technology Journal Database, Wanfang Database, SinoMed, PubMed, Web of Science, Embase, and the Cochrane Library were searched from inception to January 1, 2024. Two researchers independently conducted literature screening and data extraction, encompassing study design, data sources, outcome definition, sample size, predictive factors, model development, and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used for risk of bias and applicability assessment of the models.

Results: A total of 4275 studies were retrieved, ultimately resulting in the inclusion of 7 articles comprising 7 predictive models for restenosis in PAD patients after EVT, with a restenosis incidence ranging from 21.8% to 39.7%. The total sample size of the included models ranged from 137 to 1578 cases, with logistic regression analysis being the most commonly used modeling method. All models were built using R software. Only 2 models underwent external validation, and the reported area under the curve ranged from 0.728 to 0.864. The summary area-under-the-curve statistic was 0.80 (95% confidence interval [CI], 0.74-0.86), with an approximate prediction interval of 0.80 (95% CI, 0.62-0.91) . The number of included predictive factors ranged from 3 to 10, with the most common factors being age, Trans-Atlantic Inter-Society Consensus Ⅱ classification, hypertension, diabetes, high-sensitivity C-reactive protein, and surgical approach. All studies exhibited high risk of bias, primarily attributed to inappropriate sources of data and poor reporting of the analysis domain.

Conclusion: Predictive models for restenosis after EVT in PAD patients demonstrate overall good predictive performance but are still in the developmental stage with higher risk of bias. Future studies should follow the TRIPOD statement, focusing on the development of new models with larger samples, rigorous study designs, and multicenter external validation.

Clinical impact: This systematic review adheres to the PRISMA 2020 statement, offering the most recent systematic assessment of risk prediction models for restenosis following endovascular treatment in peripheral arterial disease.The newly developed PROBAST tool was employed to assess the risk of bias and the applicability of the existing evidence.This review emphasizes the practical utility, limitations of the current evidence, and recommendations for future research, with the goal of providing valuable information for clinicians and patients in their decision-making process, while also supporting the advancement of future research endeavors.