Machine learning models incorporating cardiac and respiratory features from the last 30 seconds of warmup and submaximal CPET predicted peak oxygen consumption with a mean absolute percentage error of 10.51%.
Observational (n=327)
No
Does machine learning using cardiorespiratory parameters from submaximal treadmill CPET accurately predict VO2peak in healthy adults?
Machine learning models incorporating respiratory parameters from submaximal treadmill tests can accurately predict VO2peak, offering a feasible alternative to maximal cardiopulmonary exercise testing.
This study investigates the quality of peak oxygen consumption (VO2peak) prediction based on cardiac and respiratory parameters calculated from warmup and submaximal stages of treadmill cardiopulmonary exercise test (CPET) using machine learning (ML) techniques and assesses the importance of respiratory parameters for the prediction outcome. The database consists of the following parameters: heart rate (HR), respiratory rate (RespRate), pulmonary ventilation (VE), oxygen consumption (VO2) and carbon dioxide production (VCO2) obtained from 369 treadmill CPETs. Combinations of features calculated based on the HR, VE and RespRate time-series from different stages of CPET were used to create 11 datasets for VO2peak prediction. Thirteen ML algorithms were employed, and model performances were evaluated using cross-validation with mean absolute percentage error (MAPE), R2 score, mean absolute error (MAE), and root mean squared error (RMSE) calculated after each iteration of the validation. The results demonstrated that incorporating respiratory-based features improves the prediction of VO2peak. The best results in terms of R2 score (0.47) and RMSE (5.78) were obtained for the dataset which included both cardiac- and respiratory-based features from CPET up to 85% of age-predicted HRmax, while the best results in terms of MAPE (10.5%) and MAE (4.63) were obtained for the dataset containing cardiorespiratory features from the last 30 seconds of warmup. The study showed the potential of using ML models based on cardiorespiratory features from submaximal tests for prediction of VO2peak and highlights the importance of the monitoring of respiratory signals, enabling to include respiratory parameters into the analysis. Presented approach offers a feasible alternative to direct VO2peak measurement, especially when specialized equipment is limited or unavailable.
Rosoł et al. (Wed,) conducted a observational in Healthy adults and athletes (n=327). Machine learning models using cardiorespiratory parameters from warmup and submaximal CPET vs. Models without respiratory features was evaluated on Mean absolute percentage error (MAPE) of VO2peak prediction. Machine learning models incorporating cardiac and respiratory features from the last 30 seconds of warmup and submaximal CPET predicted peak oxygen consumption with a mean absolute percentage error of 10.51%.