Predicting inferior vena cava filter complications using machine learning.

ABSTRACT:

Objective

Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data.

Methods

The Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or

SUBMITTER: Li B 

PROVIDER: S-EPMC11523346 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature