Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study

Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study

Abstract The Arteriovenous Access Stage (AVAS) classification simplifies information about suitability of vessels for vascular access (VA). It’s been previously validated in a clinical study. Here, AVAS performance was tested against multiple ultrasound mapping measurements using machine learning. A...

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Main Authors: Katerina Lawrie, Petr Waldauf, Peter Balaz, Radoslav Bortel, Ricardo Lacerda, Emma Aitken, Krzysztof Letachowicz, Mario D’Oria, Vittorio Di Maso, Pavel Stasko, Antonio Gomes, Joana Fontainhas, Matej Pekar, Alena Srdelic, VAVASC Study Group, Stephen O’Neill
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Arteriovenous access
Classification system
Dialysis
Mapping
Random forest
Renal replacement therapy
Online Access:https://doi.org/10.1038/s41598-025-86456-3
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Summary:Abstract The Arteriovenous Access Stage (AVAS) classification simplifies information about suitability of vessels for vascular access (VA). It’s been previously validated in a clinical study. Here, AVAS performance was tested against multiple ultrasound mapping measurements using machine learning. A prospective multicentre international study (NCT04796558) with patient recruitment from March 2021-July 2024. Demographics, risk factors, vessels parameters, types of predicted and created VA (pVA, cVA) were collected. We modelled pVA and cVA using the Random Forest algorithm. Model performance was estimated and compared using Bayesian generalized linear models. ROC AUC with 95% credible intervals was the performance metric. 1151 patients were included. ROC AUC for pVA prediction by AVAS was 0.79 (0.77;0.82) and by mapping was 0.85 (0.83;0.88). ROC AUC for cVA prediction by AVAS was 0.71 (0.69;0.74) and by mapping was 0.8 (0.78;0.83). Using AVAS with other parameters increased the ROC AUC to 0.87 for pVA (0.84;0.89) and 0.82 (0.79;0.84) for cVA. Using mapping with other parameters increased the ROC AUC to 0.88 for pVA (0.86;0.91) and 0.85 (0.83;0.88) for cVA. Multiple mapping measurements showed higher performance at VA prediction than AVAS. However, AVAS is simpler and quicker, so may be preferable for routine clinical practice.
ISSN:2045-2322

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