Evidence map›Paper›PMID 40451853›Full record

ArticleScientific reports2025

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

James J FitzgibbonDivision 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.
Mengyuan RuanDepartment of Surgery, Center for Surgery and Public Health, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA.
Patrick HeindelDivision 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.
Abena Appah-SampongDivision 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.
Tanujit DeyDepartment of Surgery, Center for Surgery and Public Health, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA.
Ali KhanThe Warren Alpert Medical School of Brown University, Providence, RI, USA.
Dirk M HentschelDivision of Renal Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA, USA.
C Keith OzakiDivision 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.
Mohamad A HussainDivision 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. mhussain7@bwh.harvard.edu.

Funding

American Heart Association 23DIVSUP1069428
6 · The paper itself

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.

Indexed as

Arteriovenous Shunt, SurgicalMachine LearningMobile ApplicationsRadial ArteryVascular PatencyAgedFemaleHumansMaleMiddle AgedProspective StudiesRenal DialysisUltrasonographyArteriovenous fistulaClinical risk predictionEnd-stage kidney diseaseHemodialysis accessMachine learning

Identifiers

PMID40451853
PMCPMC12127467

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.