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Tailored risk assessment and forecasting in intermittent claudication

Ravindhran, Bharadhwaj; Prosser, Jonathon; Lim, Arthur; Lathan, Ross; Hitchman, Louise; Mishra, Bhupesh; Smith, George; Carradice, Daniel; Thakker, Dhaval; Chetter, Ian

Authors

Bharadhwaj Ravindhran

Jonathon Prosser

Arthur Lim

Ross Lathan

Louise Hitchman



Abstract

Background
Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies.
Methods
Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset.
Results
The bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression.
Conclusion
The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.

Citation

Ravindhran, B., Prosser, J., Lim, A., Lathan, R., Hitchman, L., Mishra, B., …Chetter, I. (2024). Tailored risk assessment and forecasting in intermittent claudication. BJS Open, 8(1), Article zrad166. https://doi.org/10.1093/bjsopen/zrad166

Journal Article Type Article
Acceptance Date Dec 14, 2023
Online Publication Date Feb 27, 2024
Publication Date Feb 1, 2024
Deposit Date Jan 5, 2024
Publicly Available Date Feb 2, 2025
Journal BJS Open
Print ISSN 2474-9842
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 8
Issue 1
Article Number zrad166
DOI https://doi.org/10.1093/bjsopen/zrad166
Keywords Patient compliance; Heart disease risk factors; Calibration; Exercise therapy; Limb; Intermittent claudication; Risk assessment; Roc curve; Guidelines; Revascularization; Cardiovascular event; Stratification; Patient-focused outcomes; Chronic limb-threate
Public URL https://hull-repository.worktribe.com/output/4500487
Additional Information Accepted not yet published

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0

Copyright Statement
© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.




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