Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity: Protocol for a Retrospective and Prospective Multicenter Cohort Study
Notice bibliographique
Résumé
Background: Cardiorespiratory fitness (CRF) is a key predictor of cardiovascular and other health-related diseases in individuals with obesity. CRF is most accurately assessed through maximal exercise testing with advanced gas-analysis equipment (maximum volume of oxygen [VO2max]); however, this approach is time-consuming, costly, and requires specialized expertise. Therefore, submaximal tests and self-reported physical activity levels have been used to develop predictive algorithms to estimate CRF, yet they often performed poorly in individuals with low CRF levels, such as patients with obesity, because they are predominantly developed using data from healthy populations. Studies using machine learning (ML) models based on VO2max data from patients with obesity appear to be lacking in the literature. ML models based on routinely collected clinical measures may offer a more practical and potentially accurate way to estimate CRF, reducing time, costs, and clinical burden. Objective: The primary aim of this study is to use multicenter, longitudinal, real-world clinical data from a uniquely characterized population with obesity to develop and validate a clinically relevant ML model for estimating CRF and to compare its performance with the gold standard of VO2max testing. Methods: A retrospective data set combining assessments of VO2max tests and clinical parameters from adult patients with severe obesity BMI (≥40.0 kg/m2 or 35.0-39.9 kg/m2 with at least 1 obesity-related comorbidity) from Vestfold Hospital Trust, Muritunet Rehabilitation Institution, and Norwegian School of Sport Sciences will be the foundation for developing the ML model. The clinically relevant ML model for estimating CRF will be presented as a web application, allowing easy access and interaction. The model's estimations will be compared against direct VO2max measurements obtained from medical equipment across institutions as part of a prospective validation. Ethical approval has been obtained for the use of 2 databases in the initial model development; approval for the remaining data and prospective phase is pending. Results: Vestfold Hospital Trust, the Norwegian School of Sport Medicine, and Muritunet Rehabilitation Institution conducted more than 2623 VO₂max tests and collected clinical parameters from 1279 adults with severe obesity during 2013-2025, both before, during, and after lifestyle interventions. The first scientific publication of the clinically relevant ML model is expected to be published in 2026. The results of the overall project are expected to be completed in 2028. The project was awarded salary funding in two rounds from Vestfold Hospital (October and December 2023) and salary funding from the Research Council of Norway (October 2024). In addition, the project received allocated supervision hours from InnoMed Norway (April 2024). Conclusions: This project aims to develop a clinically relevant ML model, which serves as a cost-effective tool for CRF estimation in individuals with obesity, improving accessibility to this important health marker. To our knowledge, this is the first initiative in Norway to estimate CRF in individuals with obesity using ML, based on a unique clinical database. The project carries substantial societal value and holds national and international relevance for health care practice and patient outcomes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,031 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».