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• W4386775883 abstract "Restenosis is a significant complication of revascularisation treatments in coronary and peripheral arteries, sometimes necessitating repeated intervention. Establishing when restenosis will happen is extremely difficult due to the interplay of multiple variables and factors. Standard clinical and Doppler ultrasound scans surveillance follow-ups are the only tools clinicians can rely on to monitor intervention outcomes. However, implementing efficient surveillance programmes is hindered by healthcare system limitations, patients’ comorbidities and compliance. Predictive models classifying patients according to their risk of developing restenosis over a specific period will allow the development of tailored surveillance, prevention programmes and efficient clinical workflows. This review aims to: 1) summarise the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries, 2) compare their performance in terms of predictive power, 3) provide an outlook for potentially improved predictive models. We carried out a comprehensive literature review by accessing the PubMed/MEDLINE database according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy consisted of a combination of keywords and included studies focusing on predictive models of restenosis published between January 1993 and April 2023. One author independently screened titles, abstracts and checked for eligibility. The rest of the authors independently confirmed and discussed in case of any disagreement. The search of published literature identified 22 studies providing two perspectives - clinical and biomechanical engineering - on restenosis and comprising distinct methodologies, predictors and study designs. We compared predictive models’ performance on discrimination and calibration aspects. We reported the performance of models simulating re-occlusion progression, evaluated by comparison with clinical images. Clinical perspective studies consider only routinely collected patient information as restenosis predictors. Our review reveals that clinical models adopting traditional statistics (n=14) exhibit only modest predictive power. The latter improves when machine learning algorithms (n=4) are employed. The logistic regression models of the biomechanical engineering perspective (n=2) show enhanced predictive power when haemodynamic descriptors linked to restenosis are fused with a limited set of clinical risk factors. Biomechanical engineering studies simulating restenosis progression (n=2) are able to capture its evolution but are computationally expensive and lack risk scoring for individual patients at specific follow-ups. Restenosis predictive models, based solely on routine clinical risk factors and using classical statistics, inadequately predict the occurrence of restenosis. Risk stratification models with increased predictive power can be potentially built by adopting machine learning techniques and incorporating critical information regarding vessel haemodynamics arising from biomechanical engineering analyses." @default.
• W4386775883 created "2023-09-16" @default.
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• W4386775883 date "2023-09-01" @default.
• W4386775883 modified "2023-10-06" @default.
• W4386775883 title "A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries" @default.
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• W4386775883 doi "https://doi.org/10.1016/j.jvssci.2023.100128" @default.
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