Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app

Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app

Abstract The goal of this study was to expand our previously created prediction tool (PREDICT-AVF) and web app by estimating long-term primary and secondary patency of radiocephalic AVFs. The data source was 911 patients from PATENCY-1 and PATENCY-2 randomized controlled trials, which enrolled patie...

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Bibliographic Details
Main Authors: James J. Fitzgibbon, Mengyuan Ruan, Patrick Heindel, Abena Appah-Sampong, Tanujit Dey, Ali Khan, Dirk M. Hentschel, C. Keith Ozaki, Mohamad A. Hussain
Format: Article
Language:English
Published: Nature Portfolio 2025-06-01
Series:Scientific Reports
Subjects:
Hemodialysis access
End-stage kidney disease
Clinical risk prediction
Machine learning
Arteriovenous fistula
Online Access:https://doi.org/10.1038/s41598-025-04310-y
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https://doi.org/10.1038/s41598-025-04310-y

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