Evidence map›Paper›PMID 41345783›Full record

ArticleNPJ digital medicine2025

Predicting 1-year successful clinical use of an arteriovenous access for hemodialysis using machine learning.

Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S Lee, Leen Al-Omran, Duminda N Wijeysundera, Mohamad A Hussain, Ori D Rotstein, Elisa Greco, Charles de Mestral and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 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. Article
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

13 authors.

Ben LiDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Naomi EisenbergDivision of Vascular Surgery, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada.
Derek BeatonData Science & Advanced Analytics, Unity Health Toronto, University of Toronto, Toronto, ON, Canada.
Douglas S LeeDivision of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada.
Leen Al-OmranSchool of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Duminda N WijeysunderaInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.
Mohamad A HussainDivision of Vascular and Endovascular Surgery and the Center for Surgery and Public Health, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Ori D RotsteinDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Elisa GrecoDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Charles de MestralDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Muhammad MamdaniInstitute of Medical Science, University of Toronto, Toronto, ON, Canada.
Graham Roche-NagleDepartment of Surgery, University of Toronto, Toronto, ON, Canada.
Mohammed Al-OmranDepartment of Surgery, University of Toronto, Toronto, ON, Canada. mohammed.al-omran@unityhealth.to.

Funding

Brigham and Women's Hospital Heart and Vascular Center Faculty AwardCIHR 202210MFE-489827-89477Humacyte, Inc V-012 Trial; V-004 TrialOntario Ministry of Health Clinician Investigator ProgramPSI Foundation R22-45Schwartz Reisman Institute for Technology and Society at the University of Toronto Graduate FellowshipVascular Therapies ACCESS-2 TrialVenoStent, Inc SAVE-FistulaS Trial
6 · The paper itself

Abstract

Arteriovenous (AV) access is important to support long-term hemodialysis; however, a significant proportion fail due to inadequate maturation or other complications. Tools that can predict long-term AV access success may guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year successful clinical use of an AV access using pre-operative data. The Vascular Quality Initiative (VQI) was used to identify patients who underwent surgical AV fistula/graft creation between 2011-2024. We identified 111 pre-operative demographic, clinical, and anatomic features. Six ML models were trained with 10-fold cross-validation. Overall, 59,674 patients underwent AV access creation and 28,304 (47.4%) had 1-year successful clinical use of their index AV access. The best prediction model was XGBoost, achieving an AUROC of 0.90. In comparison, logistic regression had an AUROC of 0.70. The XGBoost model accurately predicted 1-year successful clinical use of an AV access for hemodialysis, performing better than logistic regression.

Identifiers

PMID41345783
PMCPMC12779959

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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.