1. Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease
- Author
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Libor Pastika, Arunashis Sau, Konstantinos Patlatzoglou, Ewa Sieliwonczyk, Antônio H. Ribeiro, Kathryn A. McGurk, Sadia Khan, Danilo Mandic, William R. Scott, James S. Ware, Nicholas S. Peters, Antonio Luiz P. Ribeiro, Daniel B. Kramer, Jonathan W. Waks, and Fu Siong Ng
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Abstract The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p
- Published
- 2024
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