Estimation of Plasma Volume by Machine Learning to Improve the Interpretation of the Athlete Biological Passport

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ABSTRACT

The identification of confounding factors related to plasma volume (PV) fluctuations is crucial for appropriate qualitative interpretations of Athlete Biological Passport (ABP) profiles. As part of ongoing efforts to remove PV variance from the concentration‐based biomarkers such as hemoglobin concentration ([Hb]), a new machine learning model for blood volume (BV) estimation using a single complete blood count analysis was applied within the ABP framework. Forty existing ABP profiles from elite athletes and healthy control subjects were used. PV was estimated using a machine learning model trained on a previous dataset. First, a visual display of the estimated PV shift was added in overlay of individual profiles. Alternatively, individual [Hb] thresholds were adjusted in a new graphical profile to account for PV variations. Finally, a set of ABP profiles with PV estimations was presented to ABP experts to assess the model's relevance in interpreting hematological data. A moderate correlation was found between measured and estimated PV in both men ( r = 0.40, p < 0.0001) and women ( r = 0.39, p < 0.0001), supporting the validity of the estimation model. In addition, ABP experts favorably assessed the available PV information, particularly the visual representation of PV. This novel estimation model offers distinct advantages (e.g., same biomarkers currently analyzed from routine ABP analyses) and could therefore be of particular interest. Further application of this model in the presence of specific and transient confounding factors may allow to confirm these results.

Abstract

A new machine learning model for plasma volume (PV) estimation was applied within the ABP framework. PV estimation was applied through visual displays or corrected ABP thresholds. With a favorable assessment from ABP experts, these results support further application of this model in the presence of specific confounding factors.

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Author and article information

Contributors

Raphael Faiss:

ORCID: https://orcid.org/0000-0001-6029-9516

[email protected]

Journal

Journal ID (nlm-ta): Drug Test Anal

Journal ID (iso-abbrev): Drug Test Anal

Journal ID (doi): 10.1002/(ISSN)1942-7611

Journal ID (publisher-id): DTA

Title: Drug Testing and Analysis

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 1942-7603

ISSN (Electronic): 1942-7611

Publication date (Electronic): 09 August 2025

Publication date (Print): November 2025

Volume: 17

Issue: 11 ( doiID: 10.1002/dta.v17.11 )

Pages: 2283-2290

Affiliations

[ ¹ ] Research and Expertise in Antidoping Sciences, Institute of Sport Sciences University of Lausanne Lausanne Switzerland

[ ² ] US Anti‐Doping Agency Colorado Springs Colorado USA

[ ³ ] Science and Research Anti Doping Denmark Brøndby Denmark

[ ⁴ ] Aspetar Orthopedic and Sports Medicine Hospital Doha Qatar

[ ⁵ ] Catholic University of the Sacred Heart Rome Italy

Author notes

[*] [* ] Correspondence:

Raphael Faiss ( raphael.faiss@ 123456unil.ch )

Author information

Bastien Krumm https://orcid.org/0000-0002-9132-606X

Raphael Faiss https://orcid.org/0000-0001-6029-9516

Article

Publisher ID: DTA3938 Other ID: 5055889

DOI: 10.1002/dta.3938

PMC ID: 12580168

PubMed ID: 40781891

SO-VID: 484457e2-02f7-468e-a393-d38aefa3e2f3

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date revision received : 25 July 2025

Date received : 13 June 2025

Date accepted : 29 July 2025

Page count

Figures: 5, Tables: 0, Pages: 8, Words: 5400

Custom metadata

source-schema-version-number 2.0

cover-date November 2025

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.6.4 mode:remove_FC converted:02.11.2025

Keywords: athlete biological passport,machine learning,plasma volume

Data availability:

Keywords: athlete biological passport, machine learning, plasma volume