Predicting recurrent interventions after radiocephalic arteriovenous fistula creation with machine learning and the PREDICT-AVF web app.
Journal:
The journal of vascular access
PMID:
38143431
Abstract
OBJECTIVE: Kidney Disease Outcomes Quality Initiative (KDOQI) guidelines discourage ongoing access salvage attempts after two interventions prior to successful use or more than three interventions per year overall. The goal was to develop a tool for prediction of radiocephalic arteriovenous fistula (AVF) intervention requirements to help guide shared decision-making about access appropriateness.
Authors
Keywords
Adult
Aged
Arteriovenous Shunt, Surgical
Canada
Clinical Decision-Making
Decision Support Techniques
Female
Graft Occlusion, Vascular
Humans
Machine Learning
Male
Middle Aged
Mobile Applications
Predictive Value of Tests
Prospective Studies
Radial Artery
Recurrence
Renal Dialysis
Risk Assessment
Risk Factors
Time Factors
Treatment Outcome
United States
Upper Extremity
Vascular Patency
Veins