JMIR medical informatics · 2026
AI-Powered Resting 12-Lead Electrocardiogram Algorithm for Predicting Low Peak Oxygen Consumption: Development and Validation Study.
СтатьяВалидационное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Huang Shu-Chun, Fu Tieh-Cheng, Liou Michelle, Huang Yu-Chieh, Chang Sing-Ya, Huang Guan-Yi, Su Hong-Ren
Аннотация
BACKGROUND: Low peak oxygen consumption (V̇O2) is associated with higher cardiovascular and all-cause mortality, while improvements in peak V̇O2 reduce this risk. Although early detection allows timely intervention, practical screening tools remain lacking. As electrocardiograms (ECGs) reflect both cardiac and age-related changes, they may offer a viable screening approach.
OBJECTIVE: This study aimed to detect low peak V̇O2 using resting 12-lead ECGs analyzed by a trained neural network.
METHODS: The low peak V̇O2 estimation model was developed using data from 965 individuals (n=540, 56% with cardiac or pulmonary disorders) mainly at Chang Gung Memorial Hospital, Linkou, and validated in an independent cohort of 242 individuals (n=194, 80% with cardiac disorders) at the Keelung branch. Resting ECGs were recorded immediately before cardiopulmonary exercise testing. Low peak V̇O2 was defined as a peak V̇O2 of <14 mL/kg/min.
RESULTS: The mean peak V̇O2 was 17.5 (SD 6.1) and 15.4 (SD 3.9) mL/kg/min in the training and validation datasets, with 27% (261/965) and 38% (92/242) classified as low peak V̇O2, respectively. Wavelet analysis improved model accuracy, underscoring its value for feature extraction. Three input models were compared: (1) individual characteristics (IC; age, sex, BMI, resting heart rate, and heart rate variability), (2) ECG alone, and (3) ECG plus IC. ECG alone outperformed IC, and combining both yielded the highest accuracy. For low peak V̇O2 prediction, ECG plus IC achieved mean area under the curve, precision, and recall values of 0.89, 0.72, and 0.72 in cross-validation, and 0.87, 0.67, and 0.61 in external validation.
CONCLUSIONS: An artificial intelligence-driven ECG-based algorithm showed strong potential for screening low peak V̇O2, enabling early identification of individuals with low peak V̇O2 and facilitating timely clinical intervention.
OBJECTIVE: This study aimed to detect low peak V̇O2 using resting 12-lead ECGs analyzed by a trained neural network.
METHODS: The low peak V̇O2 estimation model was developed using data from 965 individuals (n=540, 56% with cardiac or pulmonary disorders) mainly at Chang Gung Memorial Hospital, Linkou, and validated in an independent cohort of 242 individuals (n=194, 80% with cardiac disorders) at the Keelung branch. Resting ECGs were recorded immediately before cardiopulmonary exercise testing. Low peak V̇O2 was defined as a peak V̇O2 of <14 mL/kg/min.
RESULTS: The mean peak V̇O2 was 17.5 (SD 6.1) and 15.4 (SD 3.9) mL/kg/min in the training and validation datasets, with 27% (261/965) and 38% (92/242) classified as low peak V̇O2, respectively. Wavelet analysis improved model accuracy, underscoring its value for feature extraction. Three input models were compared: (1) individual characteristics (IC; age, sex, BMI, resting heart rate, and heart rate variability), (2) ECG alone, and (3) ECG plus IC. ECG alone outperformed IC, and combining both yielded the highest accuracy. For low peak V̇O2 prediction, ECG plus IC achieved mean area under the curve, precision, and recall values of 0.89, 0.72, and 0.72 in cross-validation, and 0.87, 0.67, and 0.61 in external validation.
CONCLUSIONS: An artificial intelligence-driven ECG-based algorithm showed strong potential for screening low peak V̇O2, enabling early identification of individuals with low peak V̇O2 and facilitating timely clinical intervention.