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Background: Fatal coronary heart disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic artificial intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts. Objectives: To develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs. Methods: An FCHD single-lead ("lead I" from 12-lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. Eighty percent of the data (5-fold cross-validation) was used for training and 20% as a holdout. Cox proportional hazards (CPH) models incorporating ECG-AI predictions with age, sex, and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Lifetime Cohort Study participants. The correlation and concordance of the predictions were assessed using Pearson correlation (R), Spearman correlation (ρ), and Cohen's kappa. Results: The ECG-AI and CPH models resulted in AUC = 0.76 and 0.79, respectively, on the 20% holdout and AUC = 0.85 and 0.87 on the Atrium Health Wake Forest Baptist external validation data. There was moderate-strong positive correlation between predictions (R = 0.74, ρ = 0.67, and κ = 0.58) when tested on the 243 paired ECGs. The clinical (lead I) and Apple Watch predictions led to the same low/high-risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in an R = 0.81, ρ = 0.76, and κ = 0.78. Conclusion: Risk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with lead I of a 12-lead ECG.
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INTRODUCTION: Diabetes mellitus type 2 (T2DM) significantly increase the risk of cardiovascular (CV) disease morbidity and mortality. This study aimed to evaluate the potential of some novel anthropometric indices and adipocytokines to evaluate CV risk among T2DM patients. METHODS: A total of 112 patients (men, 57; women, 55) with T2DM visiting Family Medicine and Endocrine counseling in the area of Health centers of Sarajevo Canton were included in this study. The sera samples were analyzed for fasting blood glucose (FBG), HbA1c, lipid profile parameters, adiponectin, and resistin levels. The Adiponectin/Resistin Index (A/R Index) was estimated using the formula. The novel anthropometric measurements, including the Conicity index (CI), Lipid Accumulation Product (LAP), visceral adiposity index (VAI), abdominal volume index (AVI), and Body adiposity index (BAI) were estimated. The 10-year risk for coronary heart disease (CHD) and fatal coronary heart disease (fCHD) is calculated by using UKPDS Risk software. RESULTS: The adiponectin was shown as a statistically significant negative association with CHD in female subjects, and the A/R index as a statistically significant association with CHD and fCHD in male subjects. The AVI is superior to the CI, LAP, VAI, and BAI in assessing cardiometabolic risk in T2DM patients. CONCLUSIONS: Our study indicated that measuring adiponectin and A/R index, together with measuring AVI as a measure of general volume, can be used as surrogates in the evaluation of high cardiovascular risk among T2DM patients.