A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries
Objective: Restenosis is a significant complication of revascularization 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 cli...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | JVS - Vascular Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666350323000329 |
| _version_ | 1797372009464725504 |
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| author | Federica Ninno, MPhil Janice Tsui, MD Stavroula Balabani, PhD Vanessa Díaz-Zuccarini, PhD |
| author_facet | Federica Ninno, MPhil Janice Tsui, MD Stavroula Balabani, PhD Vanessa Díaz-Zuccarini, PhD |
| author_sort | Federica Ninno, MPhil |
| collection | DOAJ |
| description | Objective: Restenosis is a significant complication of revascularization 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 programs is hindered by health care 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 programs, and efficient clinical workflows. This review aims to: (1) summarize the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries; (2) compare their performance in terms of predictive power; and (3) provide an outlook for potentially improved predictive models. Methods: 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 (PRISMA) 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 and 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 reocclusion progression, evaluated by comparison with clinical images. Results: 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 hemodynamic 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. Conclusions: 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 hemodynamics arising from biomechanical engineering analyses. |
| first_indexed | 2024-03-08T18:30:10Z |
| format | Article |
| id | doaj.art-dee09c287edb4671b4d75dd25569da9a |
| institution | Directory Open Access Journal |
| issn | 2666-3503 |
| language | English |
| last_indexed | 2024-03-08T18:30:10Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JVS - Vascular Science |
| spelling | doaj.art-dee09c287edb4671b4d75dd25569da9a2023-12-30T04:44:55ZengElsevierJVS - Vascular Science2666-35032023-01-014100128A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteriesFederica Ninno, MPhil0Janice Tsui, MD1Stavroula Balabani, PhD2Vanessa Díaz-Zuccarini, PhD3Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom; Wellcome-EPSRC Centre for Interventional Surgical Sciences, London, United KingdomDepartment of Vascular Surgery, Royal Free Hospital NHS Foundation Trust, London, United Kingdom; Division of Surgery & Interventional Science, Department of Surgical Biotechnology, Faculty of Medical Sciences, University College London, Royal Free Campus, London, United KingdomWellcome-EPSRC Centre for Interventional Surgical Sciences, London, United Kingdom; Department of Mechanical Engineering, University College London, London, United KingdomWellcome-EPSRC Centre for Interventional Surgical Sciences, London, United Kingdom; Department of Mechanical Engineering, University College London, London, United Kingdom; Correspondence: Vanessa Diaz-Zuccarini, PhD, Torrington Place, UCL Mechanical Engineering, London, WC1E 7JEObjective: Restenosis is a significant complication of revascularization 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 programs is hindered by health care 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 programs, and efficient clinical workflows. This review aims to: (1) summarize the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries; (2) compare their performance in terms of predictive power; and (3) provide an outlook for potentially improved predictive models. Methods: 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 (PRISMA) 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 and 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 reocclusion progression, evaluated by comparison with clinical images. Results: 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 hemodynamic 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. Conclusions: 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 hemodynamics arising from biomechanical engineering analyses.http://www.sciencedirect.com/science/article/pii/S2666350323000329Coronary artery diseasePeripheral arterial diseasePredictive modelsRestenosisRisk factors |
| spellingShingle | Federica Ninno, MPhil Janice Tsui, MD Stavroula Balabani, PhD Vanessa Díaz-Zuccarini, PhD A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries JVS - Vascular Science Coronary artery disease Peripheral arterial disease Predictive models Restenosis Risk factors |
| title | A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries |
| title_full | A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries |
| title_fullStr | A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries |
| title_full_unstemmed | A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries |
| title_short | A systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries |
| title_sort | systematic review of clinical and biomechanical engineering perspectives on the prediction of restenosis in coronary and peripheral arteries |
| topic | Coronary artery disease Peripheral arterial disease Predictive models Restenosis Risk factors |
| url | http://www.sciencedirect.com/science/article/pii/S2666350323000329 |
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