Using ECG Machine Learning for Detection of Cardiovascular Disease in African American Men and Women: the Jackson Heart Study

Background Almost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in barbershops would facilitate secondary prevention of CVD. We sought to investigate the cross-sectional association of prevalent CVD and sex with global electrical heterogeneity (GEH) and develop a tool for CVD detection. Methods Participants from the Jackson Heart Study (JHS) with analyzable ECGs (n=3,679; age, 62±12 years; 36% men) were included. QRS, T, and spatial ventricular gradient (SVG) vectors’ magnitude and direction, and traditional metrics were measured on 12-lead ECG. Linear regression and mixed linear models with random intercept were adjusted for cardiovascular risk factors, sociodemographic and anthropometric characteristics, type of median beat, and mean RR’ intervals. Random forests, convolutional neural network, and lasso models were developed in 80%, and validated in 20% samples. Results In fully adjusted models, women had a smaller spatial QRS-T angle (−12.2(−19.4 to-5.1)°; P =0.001), SAI QRST (−29.8(−39.3 to −20.3) mV*ms; P <0.0001), and SVG elevation (−4.5(−7.5 to −1.4)°; P =0.004) than men, but larger SVG azimuth (+16.2(10.5-21.9)°; P <0.0001), with a significant random effect between families (+20.8(8.2-33.5)°; P =0.001). SAI QRST was larger in women with CVD as compared to CVD-free women or men (+15.1(3.8-26.4) mV*ms; P =0.009). Men with CVD had smaller T area [by 5.1 (95%CI 1.2-9.0) mV*ms] than CVD-free men, but there were no differences when comparing women with CVD to CVD-free women. Machine-learning detected CVD with ROC AUC 0.69-0.74; plug-in-based model included only age and QRS-T angle. Conclusions GEH varies by sex. Sex modifies an association of GEH with CVD. Automated CVD detection is feasible. ### Competing Interest Statement The authors have declared no competing interest. ### Clinical Trial not a clinical trial; observational cohort ### Funding Statement The Jackson Heart Study (JHS) is supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I) and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute on Minority Health and Health Disparities (NIMHD). This work was supported by HL118277 (LGT). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All study participants provided written informed consent before entering the JHS study. This study was approved by the Oregon Health & Science University (OHSU) Institutional Review Board. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The JHS data are available through the National Heart, Lung, and Blood Institute Biological Specimen and Data Repository Information Coordinating Center (BioLINCC) and the National Center of Biotechnology Information database of Genotypes and Phenotypes (dbGaP).