Incident hypertension and hypertension-related outcomes can be predicted by an artificial intelligence-enhanced electrocardiography (AI-ECG) model, AI-ECG risk estimator to predict incident hypertension (AIRE-HTN), results from a cohort study show. These data were reported by Arunashis Sau, PhD, of the National Heart and Lung Institute, Imperial College London, England, and colleagues, in a manuscript published online in JAMA: Cardiology. Roughly 1 in 3 adults worldwide experiences hypertension. Adverse outcomes from hypertension can be mitigated by early lifestyle interventions and treatments. AI-ECG has been helpful in identifying subclinical diseases and may be able to assist clinicians in predicting and diagnosing hypertension. This externally validated, prognostic cohort study was conducted at Beth Israel Deaconess Medical Center (BIDMC) in Boston. The UK Biobank externally validated the study using a UK-based cohort. Routinely collected ECGs between 2014 and 2023 from BIDMC patients were used to train and test the AIRE-HTN. The algorithm was trained to risk stratify patients for hypertension-associated adverse events. Results were validated using the UK data between 2014 and 2022. A total of 1,163,401 ECGs from 189 539 patients (mean age=57.7 years; 52.1% female) at BIDMC trained the AIRE-HTN. Incident hypertension was evaluated in 19,423 BIDMC patients. AIRE-HTN was tested on 65 610 ECGs from same number of participants in the UK Biobank (mean age=65.4 years; 51.5% female). In the UK Biobank, 35,806 patients were evaluated for incident hypertension. The AIRE-HTN predicted incident hypertension (BIDMC: n = 6446 [33%] events; C index=0.70; 95% confidence interval [C]=0.69-0.71; UK Biobank: n = 1532 [4%] events; C index=0.70; 95% CI=0.69-0.71). AIRE-HTN helped predict incident hypertension in addition to existing clinical risk factors (continuous net reclassification index, BIDMC: 0.44; 95% CI=0.33-0.53; UK Biobank: 0.32; 95% CI=0.23-0.37). The AIRE-HTN score in adjusted Cox models was an independent predictor of cardiovascular death (hazard ratio [HR] per standard deviation=2.24; 95% CI=1.67-3.00) and stratified risk for heart failure (HR=2.60; 95% CI=2.22-3.04), myocardial infarction (HR=3.13; 95% CI=2.55-3.83), ischemic stroke (HR=1.23; 95% CI=1.11-1.37) and chronic kidney disease (HR=1.89; 95% CI=1.68-2.12), beyond traditional risk factors. Overall, this prognostic study revealed the potential for AIRE-HTN to aid in predicting hypertension and hypertension-associated adverse events. Source: Sau A, Barker J, Pastika L, et al. Artificial Intelligence-Enhanced Electrocardiography for Prediction of Incident Hypertension. JAMA Cardiol. 2025 Jan (Article in Press) Image Credit: New Africa – stock.adobe.com