Back to search

Article

Predicting Long-Term Patency of Radiocephalic Arteriovenous Fistulas with Machine Learning and the PREDICT-AVF Web App

2024-05-30

Abstract excerpt

<title>Abstract</title> <p>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 ultrasou...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
2db13896-76a2-53e9-8703-23c3942013bd
DOI
10.21203/rs.3.rs-4389336/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Predicting Long-Term Patency of Radiocephalic Arteriovenous Fistulas with Machine Learning and the PREDICT-AVF Web AppDOI 10.21203/rs.3.rs-4389336/v1
Primary Patency Success of Arteriovenous Shunts in Hemodialysis Patients: A 28-month Prospective Study at Al-Thawra Modern General Hospital, YemenSemantic proximity
Nomogram-Based Prediction of the Risk of AVF Maturation :A Retrospective StudySemantic proximity
Patient-specific computational haemodynamics associated with surgical creation of an arteriovenous fistulaSemantic proximity
Construction of a nomogram prediction model for the complications of iliofemoral vascular intervention accessSemantic proximity
Analysis of risk factors for failure of autologous arteriovenous endovascular fistula in patients on maintenance hemodialysisSemantic proximity
Short-term and long-term outcome prediction for patients with coronary artery disease using machine learning and comprehensive multi-center patient dataSemantic proximity
Intimal CD31-positive relative surfaces are associated with the dysfunction of autologous arteriovenous fistulas in patients receiving dialysisSemantic proximity
Machine Learning for Predicting Delayed Graft Function and Graft Survival After Kidney Transplantation: A Systematic Review and Meta-AnalysisSemantic proximity
Comparing Machine Learning Approaches for Predicting CFD-Derived Stroke Risk Indicators in Atrial Fibrillation PatientsSemantic proximity
Is AV fistula patency associated with angiotensin-converting enzyme (ACE) polymorphism and ACE inhibitor intake?Semantic proximity
Medium and Long Term Time-to-Event Outcomes After Elective Fenestrated and Branched Endovascular Repair of Complex Abdominal and Thoracoabdominal Aortic Aneurysms: A Contemporary Systematic ReviewSemantic proximity
Construction and Validation of a Multimodal Imaging and Machine Learning–Based Prognostic Prediction Model for Aneurysmal Subarachnoid Hemorrhage: A Multicenter Retrospective Cohort Study (TRIPOD-AI Compliant)Semantic proximity
Select a neighboring publication to make it the new centre.