March 6, 2026Open Access

Peak Oxygen Uptake Prediction From Resting and Submaximal Variables of Cardiopulmonary Exercise Testing

Key Result

Using demographic, resting, and submaximal variables improved peak oxygen uptake prediction (R² up to 0.796 in normal group) during cardiopulmonary exercise testing.

Key Points

This research aims to predict peak oxygen uptake using submaximal variables during cardiopulmonary exercise testing to assess cardiorespiratory fitness.
Collected data from 13,535 cardiopulmonary exercise tests.
Divided participants into normal group (NG) and other group (OG).
Trained regression models using demographic, resting, and submaximal variables.
Performed stratified 5-fold cross-validation for model evaluation.
Bayesian Ridge model was optimal for NG and Light Gradient Boosting Machine for OG.
R² values improved significantly when including submaximal variables: 0.796 for NG, 0.732 for OG, 0.761 for NG+OG.
Oxygen consumption and forced expiratory volume positively impacted model accuracy, while minute ventilation at second threshold negatively affected predictions.

Structured PICO

Can peak oxygen uptake be accurately predicted from resting and submaximal variables during cardiopulmonary exercise testing?

Population

13,535 cardiopulmonary exercise testings from patients divided into a normal group (n=1,076) and other group (n=9,823)

Intervention

Prediction models (Bayesian Ridge, Light Gradient Boosting Machine) using demographic, resting, and submaximal variables from cardiopulmonary exercise testing

Comparator

Prediction models using only demographic and resting variables

Outcome

Maximum oxygen consumption (peak oxygen uptake) prediction accuracy (measured by R²)surrogate

Peak oxygen uptake can be accurately predicted using submaximal exercise variables, offering a way to assess prognostic markers in patients unable to achieve maximal exhaustion during CPET.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Background Cardiorespiratory fitness, as measured by peak oxygen uptake during cardiopulmonary exercise testing, is a prognostic indicator. We aim to predict peak oxygen uptake from submaximal variables on cardiopulmonary exercise testing to assess cardiorespiratory fitness when maximal exertion is not possible. Methods Data from 13535 cardiopulmonary exercise testings were collected, and patients were divided into a normal group (NG; n=1076) and other group (OG; n=9823). Regression models to predict maximum oxygen consumption were trained and evaluated on the NG, OG, and combined groups (NG+OG) using stratified 5‐fold cross‐validation. We trained different models using demographic, resting and submaximal variables. Results Optimal models were Bayesian Ridge for the NG and Light Gradient Boosting Machine for the other groups. The mean (SD) R 2 when using demographic and rest variables was 0.690 (0.027) for the NG, 0.546 (0.012) for the OG, and 0.562 (0.015) for the NG+OG. When using demographic, rest and submaximal variables, performance increased to 0.796 (0.020) for the NG, 0.732 (0.009) for the OG, and 0.761 (0.008) for the NG+OG. Oxygen consumption at the first ventilatory threshold, minute ventilation at the second ventilatory threshold, and forced expiratory volume in 1 second were important features across the models trained with rest and submaximal variables. Minute ventilation at the second ventilatory threshold had negative effects, while oxygen consumption at the first ventilatory threshold and forced expiratory volume in 1 second had positive effects on maximum oxygen consumption prediction. In exploratory analyses, the inclusion of chronotropic index improved model performance. Conclusions Our peak oxygen uptake prediction model demonstrated strong performance using submaximal exercise variables. This methodology offers a means to assess prognostic markers for individuals who might not achieve maximal exhaustion during cardiopulmonary exercise testing.