Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app - James J. Fitzgibbon; Mengyuan Ruan; Patrick Heindel; Abena Appah-Sampong; Tanujit Dey; Ali Khan; Dirk M. Hentschel; C. Keith Ozaki; Mohamad A. Hussain (Scientific Reports)
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.