Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.

ABSTRACT:

Background

Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS.

Methods and results

The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models

SUBMITTER: Li B 

PROVIDER: S-EPMC11646515 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature