Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F25%3A43928001" target="_blank" >RIV/00064173:_____/25:43928001 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/25:00384280 RIV/00216224:14110/25:00140514 RIV/00216208:11120/25:43928001 RIV/00023001:_____/25:00085357
Result on the web
<a href="https://doi.org/10.1038/s41598-025-86456-3" target="_blank" >https://doi.org/10.1038/s41598-025-86456-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s41598-025-86456-3" target="_blank" >10.1038/s41598-025-86456-3</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning validation of the AVAS classification compared to ultrasound mapping in a multicentre study
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30201 - Cardiac and Cardiovascular systems
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Scientific Reports
ISSN
2045-2322
e-ISSN
2045-2322
Volume of the periodical
15
Issue of the periodical within the volume
January
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
2538
UT code for WoS article
001401998100028
EID of the result in the Scopus database
2-s2.0-85216440001