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

Journal of the American Heart Association, Volume 15, Issue 6 , March 17, 2026. BackgroundCardiorespiratory 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.MethodsData 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.ResultsOptimal models were Bayesian Ridge for the NG and Light Gradient Boosting Machine for the other groups. The mean (SD) R2when 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.