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Aims: To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET)., Methods and Results: V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]., Conclusion: Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification., Competing Interests: Conflict of interest: B.M.W. has consulted for Change Healthcare. P.D.A. and P.B. are supported by grants from Bayer AG and IBM applying machine learning in cardiovascular disease. P.B. serves as a consultant for Novartis and Prometheus Biosciences. P.T.E. receives sponsored research support from Bayer AG and IBM Research; he has also served on advisory boards or consulted for Bayer AG, MyoKardia, and Novartis. S.A.L. receives sponsored research support from Bristol Myers Squibb/Pfizer, Bayer AG, Boehringer Ingelheim, Fitbit, and IBM, and has consulted for Bristol Myers Squibb/Pfizer, Bayer AG, and Blackstone Life Sciences. S.A.L. is now an employee of Novartis Institute for Biomedical Research. The remaining authors have no disclosures., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Competing Interests: Conflict of interest: None declared.

Competing Interests: Conflict of interest: None declared.

Aims: Accurate interpretation of cardiopulmonary exercise testing (CPET) relies on age, gender, and exercise modality-specific reference values. To date, clinically applicable CPET reference values derived from a source population of endurance athletes (EAs) have been lacking. The purpose of this study was to generate CPET reference values for use in the clinical assessment of EA., Methods and Results: Prospective data accrued during the clinical care of healthy EA were used to derive CPET reference values and to develop novel equations for V˙O2peak. The performance of these equations was compared to the contemporary standard of care equations and assessed in a discrete EA validation cohort. A total of 272 EA (age = 42 ± 15 years, female = 31%, V˙O2peak = 3.6 ± 0.83 L/min) met inclusion criteria and comprised the derivation cohort. V˙O2peak prediction equations derived from general population cohorts described a modest amount of V˙O2peak variability [R2 = 0.58-0.70, root mean square error (RMSE) = 0.46-0.54 L/min] but were mis-calibrated (calibration-in-the-large = 0.45-1.18 L/min) among EA leading to significant V˙O2peak underestimation. Newly derived, externally validated V˙O2peak prediction equations for EA that included age, sex, and height for both treadmill (R2 = 0.74, RMSE = 0.42 L/min) and cycle ergometer CPET (Cycle: R2 = 0.69, RMSE = 0.42 L/min) demonstrated improved accuracy., Conclusion: Commonly used V˙O2peak prediction equations derived from general population cohorts perform poorly among competitive EA. Newly derived CPET reference values including novel V˙O2peak prediction equations may improve the clinical utility of CPET in this rapidly growing patient population., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2021. For permissions, please email: journals.permissions@oup.com.)