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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Nature Portfolio
2025-06-01
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| Online Access: | https://doi.org/10.1038/s41598-025-04310-y |
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| author | James J. Fitzgibbon Mengyuan Ruan Patrick Heindel Abena Appah-Sampong Tanujit Dey Ali Khan Dirk M. Hentschel C. Keith Ozaki Mohamad A. Hussain |
| author_facet | James J. Fitzgibbon Mengyuan Ruan Patrick Heindel Abena Appah-Sampong Tanujit Dey Ali Khan Dirk M. Hentschel C. Keith Ozaki Mohamad A. Hussain |
| author_sort | James J. Fitzgibbon |
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| description | 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. |
| format | Article |
| id | doaj-art-c2a2539bfc474fd4a35dc3e4a2337358 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-c2a2539bfc474fd4a35dc3e4a23373582025-08-20T02:03:35ZengNature PortfolioScientific Reports2045-23222025-06-011511710.1038/s41598-025-04310-yPredicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web appJames J. Fitzgibbon0Mengyuan Ruan1Patrick Heindel2Abena Appah-Sampong3Tanujit Dey4Ali Khan5Dirk M. Hentschel6C. Keith Ozaki7Mohamad A. Hussain8Division of Vascular and Endovascular Surgery, Department of Surgery, Shapiro Cardiovascular Centre, Brigham and Women’s Hospital/Harvard Medical SchoolDepartment of Surgery, Center for Surgery and Public Health, Brigham and Women’s Hospital/Harvard Medical SchoolDivision of Vascular and Endovascular Surgery, Department of Surgery, Shapiro Cardiovascular Centre, Brigham and Women’s Hospital/Harvard Medical SchoolDivision of Vascular and Endovascular Surgery, Department of Surgery, Shapiro Cardiovascular Centre, Brigham and Women’s Hospital/Harvard Medical SchoolDepartment of Surgery, Center for Surgery and Public Health, Brigham and Women’s Hospital/Harvard Medical SchoolThe Warren Alpert Medical School of Brown UniversityDivision of Renal Medicine, Brigham and Women’s Hospital/Harvard Medical SchoolDivision of Vascular and Endovascular Surgery, Department of Surgery, Shapiro Cardiovascular Centre, Brigham and Women’s Hospital/Harvard Medical SchoolDivision of Vascular and Endovascular Surgery, Department of Surgery, Shapiro Cardiovascular Centre, Brigham and Women’s Hospital/Harvard Medical SchoolAbstract 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.https://doi.org/10.1038/s41598-025-04310-yHemodialysis accessEnd-stage kidney diseaseClinical risk predictionMachine learningArteriovenous fistula |
| spellingShingle | James J. Fitzgibbon Mengyuan Ruan Patrick Heindel Abena Appah-Sampong Tanujit Dey Ali Khan Dirk M. Hentschel C. Keith Ozaki Mohamad A. Hussain Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app Scientific Reports Hemodialysis access End-stage kidney disease Clinical risk prediction Machine learning Arteriovenous fistula |
| title | Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app |
| title_full | Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app |
| title_fullStr | Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app |
| title_full_unstemmed | Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app |
| title_short | Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app |
| title_sort | predicting long term patency of radiocephalic arteriovenous fistulas with machine learning and the predict avf web app |
| topic | Hemodialysis access End-stage kidney disease Clinical risk prediction Machine learning Arteriovenous fistula |
| url | https://doi.org/10.1038/s41598-025-04310-y |
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