Deep learning prediction of peak oxygen uptake in patients with coronary heart disease: a retrospective study.

objectiveTo develop and validate prediction models for peak oxygen uptake (VO₂peak) in patients with coronary heart disease (CHD) using submaximal cardiopulmonary exercise testing (CPET) indicators and deep learning methods.

designRetrospective model development and validation study.

settingCardiac Rehabilitation Centre, Peking University Third Hospital, China.

participantsA total of 10 538 patients with CHD who underwent CPET between January 2014 and December 2019.

methodsClinical data and CPET indicators were collected. Multiple machine learning and deep learning models were developed and compared. Model performance was assessed using R², mean absolute error (MAE), bias, Bland-Altman analysis and SHapley Additive exPlanations (SHAP) feature importance ranking.

resultsThe neural network model achieved the best performance (R² = 0.82, MAE=1.55 mL/kg/min, bias=0.08). XGBoost was the best-performing traditional machine learning model (R² = 0.74). SHAP analysis identified eight top-ranked features, including VO₂@AT, OUES, weight, VE/VCO₂ slope, VE/VCO₂@AT, age, gender and HR@AT.

conclusionThe CPET deep learning model shows potential for predicting VO₂peak in CHD patients, but further external validation and prospective studies are required before clinical application.