Tailored risk assessment and forecasting in intermittent claudication.

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 pro

SUBMITTER: Ravindhran B 

PROVIDER: S-EPMC10898330 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature