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Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study
K. Lawrie, P. Waldauf, P. Balaz, R. Bortel, R. Lacerda, E. Aitken, K. Letachowicz, M. D'Oria, V. Di Maso, P. Stasko, A. Gomes, J. Fontainhas, M. Pekar, A. Srdelic, VAVASC Study Group, S. O'Neill
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články, multicentrická studie, srovnávací studie, práce podpořená grantem
NLK
Directory of Open Access Journals
od 2011
Free Medical Journals
od 2011
PubMed Central
od 2011
Europe PubMed Central
od 2011
ProQuest Central
od 2021-01-01
Open Access Digital Library
od 2011-01-01
Open Access Digital Library
od 2011-01-01
Health & Medicine (ProQuest)
od 2021-01-01
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od 2011
Springer Nature OA/Free Journals
od 2011-12-01
Springer Nature - nature.com Journals - Fully Open Access
od 2011-12-01
- MeSH
- arteriovenózní zkrat MeSH
- Bayesova věta MeSH
- lidé středního věku MeSH
- lidé MeSH
- prospektivní studie MeSH
- ROC křivka MeSH
- senioři MeSH
- strojové učení * MeSH
- ultrasonografie * metody MeSH
- Check Tag
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
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.
3rd Faculty of Medicine Charles University Prague Czech Republic
AdNa s r o Vascular Surgery Clinic Košice Slovak Republic
Centre for Medical Education Queen's University Belfast Belfast UK
Centre for Vascular and Mini invasive Surgery Hospital AGEL Třinec Podlesí Czech Republic
Department of General Surgery Hospital Professor Doutor Fernando Fonseca Amadora Portugal
Department of Nephrology and Transplantation Medicine Wroclaw Medical University Wroclaw Poland
Department of Physiology Faculty of Medicine Masaryk University Brno Czech Republic
Department of Renal Surgery Queen Elizabeth University Hospital Glasgow UK
Department of Transplant Surgery and Regional Nephrology Unit Belfast City Hospital Belfast UK
Division of Vascular Surgery University Hospital Královské Vinohrady Prague Czech Republic
Faculty of Electrical Engineering Czech Technical University Prague Prague Czech Republic
RL Vascular Surgery and Interventional Radiology Private Practice Salvador Brazil
Citace poskytuje Crossref.org
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