Abstract
Exercise Stress Testing (EST), an inexpensive noninvasive modality, is widely used for preliminary cardiovascular evaluation and ongoing monitoring of cardiac patients. The MET (Metabolic Equivalent of Task) score from EST, indicating physical fitness, is associated with overall mortality risk and is extremely helpful for preoperative evaluation. Artificial Intelligence (AI) advances, particularly Deep Learning (DL) for resting ECG analysis, have significantly improved cardiac diagnostic accuracy.
This study explores AI's ability to predict MET scores from resting ECGs, aiming to streamline fitness assessments and detect early changes in fitness levels.
The current analysis encompasses 45,518 consecutive EST evaluations from 31,532 individual patients from 2017 to 2023. MET scores were automatically computed using an established formula, while interpretations by certified cardiologists provided a benchmark for the AI model. Poor fitness was defined as being within the lowest quartile of MET scores, adjusted for sex and age. A deep learning framework was developed to process a 10-second snippet of resting phase ECG in raw waveform, predicting the likelihood of diminished fitness and thus acting as a binary classifier. The efficacy of this Poor Fitness AI model (PF-AI model) was assessed through metrics such as the area under the receiver operating characteristic curve (AUROC), along with sensitivity, specificity, and accuracy rates.
The average age of the study group was 55±10 years, predominantly male (68.8%, n = 31,337). Among them, 1,152 were identified as having poor fitness. Applying the model to the entire cohort through 5-fold cross-validation yielded a sensitivity of 78%, a specificity of 81%, and an AUROC of 0.87 in predicting poor fitness.
Author notes
Funding Acknowledgements: None.
