Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app.
Authors
Affiliations (6)
Affiliations (6)
- Division of Vascular and Endovascular Surgery, Department of Surgery, Shapiro Cardiovascular Centre, Brigham and Women's Hospital/Harvard Medical School, 5th Floor, Suite 5-078A, 75 Francis Street, Boston, MA, 02115, USA.
- Department of Surgery, Center for Surgery and Public Health, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA.
- The Warren Alpert Medical School of Brown University, Providence, RI, USA.
- Division of Renal Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA.
- Division of Vascular and Endovascular Surgery, Department of Surgery, Shapiro Cardiovascular Centre, Brigham and Women's Hospital/Harvard Medical School, 5th Floor, Suite 5-078A, 75 Francis Street, Boston, MA, 02115, USA. [email protected].
- Department of Surgery, Center for Surgery and Public Health, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA. [email protected].
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 patients undergoing new radiocephalic AVF creation with prospective longitudinal follow up and ultrasound measurements. Models were built using a combination of baseline characteristics and post-operative ultrasound measurements to estimate patency up to 2.5 years. Discrimination performance was assessed, and an interactive web app was created using the most robust model. At 2.5 years, the unadjusted primary and secondary patency (95% CI) was 29% (26-33%) and 68% (65-72%). Models using baseline characteristics generally did not perform as well as those using post-operative ultrasound measurements. Overall, the Cox model (4-6 weeks ultrasound) had the best discrimination performance for primary and secondary patency, with an integrated Brier score of 0.183 (0.167, 0.199) and 0.106 (0.085, 0.126). Expansion of the PREDICT-AVF web app to include prediction of long-term patency can help guide clinicians in developing comprehensive end-stage kidney disease Life-Plans with hemodialysis access patients.