Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG.

Background: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal. Methods: Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023. Findings: ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14-2.71), 4.23 (3.74-4.78), and 11.75 (10.2-13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age. Interpretation: ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired. Funding: Anumana.

Building the evidence for hepatitis B vaccination programs for students and researchers working with biological samples in Indian Institutes of Higher Education.

BACKGROUND: Individuals working with biological samples in Indian universities are at risk for occupational exposure to hepatitis B virus (HBV) and may not be vaccinated. AIM: We documented the need for HBV vaccination in students and others, developed an institutional HBV vaccination program, delivered HBV vaccines, and then assessed the determinants of vaccine uptake. METHODS: Over a year, we conducted a prospective cohort study documenting the need for HBV vaccination in people working with biological materials in a major Indian institution, developed a HBV vaccination program, delivered HBV vaccines, and assessed determinants of vaccine uptake. In August 2018, a needs assessment determined exposure to blood, body fluids, and other potentially infectious material in the research setting, followed in September by a cross-sectional survey on HBV vaccination status. Institutional approval for vaccination followed in October, and vaccine clinics began in February 2019. In September, a follow-up survey investigated determinants of vaccine uptake. RESULTS: A total of 185 people participated in the baseline HBV vaccination status survey. Only 26% of students, staff, and faculty were fully vaccinated for HBV. Over 70% of the target group came forward for vaccination and >90% completed all doses. Getting vaccinated with peers strongly influenced vaccine uptake, as did availability of free vaccine, onsite clinics, and reminders. CONCLUSION: HBV vaccination programs for individuals at occupational risk are needed in Indian academic institutions beyond medical schools as part of institutional biosafety programs.