O57: Tailored risk assessment and forecasting in intermittent claudication using machine learning (TRAFIC-ML) Free

Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the initial treatment for patients with intermittent claudication(IC). However, implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. We propose a precise machine learning derived decision support system that aims to provide personalised outcome predictions across different management strategies.

Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample of 10,000 patients based on 255 patients from our vascular centre. The model considered 27 baseline characteristics, compliance to best medical therapy/smoking cessation and treatment strategy. The model was validated using a separate dataset of 254 patients. This model was then used to build a prototype interactive decision support system which was evaluated using calibration curves, decision curve analyses and area under the receiver operator characteristic (AUROC) curves.

The AUROC curves demonstrated excellent discrimination for the risk of progression to chronic limb threatening ischaemia at two years(0.892) and five years(0.866) and likelihood of major adverse cardiovascular events(0.836), major adverse limb events(0.891) and revascularisation(0.896)within 5 years, regardless of the treatment strategy. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analyses confirmed clinical utility. The tool maintained an accuracy of >80% and an effect size of >0.5.

Our decision support system successfully predicts outcomes in IC regardless of treatment strategy, offering potential for improved risk stratification and patient outcomes.