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

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: 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.