Sex related cardiovascular risk is non-dichotomous: artificial intelligence enhanced electrocardiography reveals continuum of risk in females

Abstract Background Females are typically underserved in cardiovascular medicine and often considered lower risk of cardiovascular disease. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. Purpose We aimed to develop an artificial intelligence-enhanced ECG (AI-ECG)-derived continuous sex score that can capture the continuum of risk and sex phenotypes within each sex, with the particular goal of addressing the female disadvantage. Methods We trained a convolution neural network to identify sex using the 12-lead ECG. The derivation cohort was 1,163,401 ECGs from 189,538 subjects from a USA secondary care hospital cohort and validated inthe UK Biobank (UKB). The model outputs a continuous value from 0 to 1 for sex prediction. The AI-ECG sex discordance score is the difference between predicted sex and biological sex. Results AI-ECG accurately identified sex in both the USA cohort (AUROC 0.943 (0.942-0.943)) and UKB datasets (n = 42,386, AUROC 0.971 (0.969-0.972)). In explainability analyses we found QRS duration, T wave morphology, QT interval, heart rate and QRS voltage as the most important factors in contributing to AI-ECG sex identification. In females only but not in males, sex discordance score was associated with a covariate-adjusted increased risk of cardiovascular death in outpatients with normal ECGs from the USA cohort, (Females hazard ratio (HR) 1.64 (1.18-2.29) p = 0.003, Males HR 1.04 (0.66-1.63), p = 0.86). This pattern persisted in the UKB: Females HR 1.39 (1.08-1.78) p = 0.01, Males HR 0.97 (0.77-1.22), p = 0.80, Fig 1). In phenome-wide association studies, we found females with a higher sex score discordance were more likely to have heart failure or myocardial infarction. Covariate-adjusted cox models confirmed the association of sex score discordance with increased future heart failure (HR 1.21 (1.07-1.37) p = 0.002 and future myocardial infarction (HR 1.32 (1.11-1.56) p = 0.001). In age- and body size-adjusted analyses, females with higher sex discordance score had increased left ventricular mass, and chamber volumes as well as increased muscle mass, reduced body fat percentage (Fig 2). In a genome-wide association study of sex discordance score we identified variants adjacent to IGF1R and NDRG4 in females, which have been previously associated with LV mass and sex hormone levels (Fig 2). Conclusion We describe sex discordance score as a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. Sex discordance score may help uncover potential underlying mechanisms of cardiovascular disease among females, and future clinical studies are needed to determine its potential utility in identification of females at higher risk of cardiovascular events. Rather than exacerbating biases, AI-ECG has the potential to reduce female cardiovascular health inequalities.