Key result
Machine learning models estimate VO2 peak with an error of ~4.2 mL/kg/min.
Why the study?
Peak cardiorespiratory fitness (VO2 peak) is an important prognostic marker in cardiovascular disease, but methods to estimate and predict its change during cardiac rehabilitation using clinical data are needed.
Can machine learning models accurately estimate VO2 peak and predict its response to cardiac rehabilitation in patients with cardiovascular disease?
Cohort (n=162)
No
Can machine learning models accurately estimate VO2 peak and predict its response to cardiac rehabilitation in patients with cardiovascular disease?
Machine learning models using clinical and functional data show promising internal performance for estimating peak cardiorespiratory fitness and its response to cardiac rehabilitation without requiring cardiopulmonary exercise testing.
ML models support non-exercise VO2 peak estimation in cardiac rehab; leaves open prospective validation before clinical adoption.
Background Peak cardiorespiratory fitness (CRF), commonly quantified as peak oxygen consumption (VO 2 peak), is an important prognostic marker in cardiovascular disease (CVD). This study developed machine-learning (ML) models to estimate VO 2 peak from clinical and functional information and to predict its longitudinal change during cardiac rehabilitation (CR). Methods This retrospective longitudinal cohort study included 333 visits from 162 patients with CVD. Task 1 estimated VO 2 peak at eligible visits using 29 clinical and functional candidate predictors without CPET-derived predictors. Task 2 predicted the change in VO 2 peak between consecutive visits (ΔVO 2 peak) using two approaches: Task 2-1 combined clinical, functional, and inter-visit exercise information with the VO 2 peak predicted by Task 1, whereas Task 2-2 combined pre-CR CPET-derived and inter-visit exercise information. Four linear regression approaches and six ML algorithms were evaluated using 5-fold patient-grouped cross-validation. Results In Task 1, CatBoost yielded an RMSE of 4.17 ± 0.24 mL·kg -1 ·min -1 ; the 6-minute walk distance, age, Korean Activity Scale Index, hand grip strength, and body mass index were among the highest-ranked features. In Task 2, CatBoost yielded similar internal prediction errors in the pathway incorporating the Task 1-predicted VO 2 peak (Task 2-1: RMSE, 3.46 ± 0.78 mL·kg -1 ·min -1 ) and the CPET-based pathway (Task 2-2: RMSE, 3.39 ± 0.75 mL·kg -1 ·min -1 ). Recent cardiac intervention in Task 2-1 and measured baseline VO 2 peak in Task 2-2 were the highest-ranked features for predicting ΔVO 2 peak. Conclusions ML models showed promising internal performance for estimating VO 2 peak and predicting its inter-visit change in this single-center cohort. These results are exploratory and do not establish clinical utility or replacement of CPET. Independent multicenter validation and prospective evaluation are required.
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Suh et al. (2026) conducted a cohort in Cardiovascular disease (CVD) (n=162). Machine-learning models (CatBoost) vs. CPET-derived predictors was evaluated on Estimation of VO2 peak and prediction of its longitudinal change (ΔVO2 peak). Machine learning models (CatBoost) estimated VO2 peak with an RMSE of 4.17 ± 0.24 mL·kg-1·min-1 and predicted its inter-visit change with an RMSE of 3.46 ± 0.78 mL·kg-1·min-1.
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