Headache. 2026 Feb 20. doi: 10.1111/head.70062. Online ahead of print.
ABSTRACT
OBJECTIVE: To investigate the ability of artificial intelligence-enabled electrocardiogram (AI-ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI-estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine.
BACKGROUND: Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI-ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging.
METHODS: We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12-lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI-ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New-onset AF was also recorded as a secondary outcome.
RESULTS: A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow-up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new-onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10-year increase in delta age). Moreover, AI-ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥-1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high-risk and low-risk groups for adverse vascular events. Higher AI-ECG AF prediction model output at baseline also predicted a higher likelihood of having new-onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new-onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]).
CONCLUSIONS: For patients with migraine, a higher AI-ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI-ECG models that can distinguish patients between high- and low-risk groups. Our results showed that AI-ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.
PMID:41721210 | DOI:10.1111/head.70062