To develop a multimodality model to predict vascular risks in patients with migraine based on comprehensive clinical information, including artificial intelligence (AI)-electrocardiogram (ECG) output, echocardiogram parameters, and migraine characteristics.

Patients with migraine have higher risks for stroke and other adverse vascular events. We have conducted prior analyses on AI-ECG algorithms, echocardiogram parameters, and detailed migraine characteristics separately to predict vascular events. However, the relative importance and combined effects of each modality have not been established.

We included 297 patients with migraine, of whom 247 were female and 109 had migraine with aura. The mean age was 39.0±13.3 and follow-up period was 68.9±60.9 months. Thirty-five developed the composite endpoint (11.8%). Those with a risk score ≥15 had significantly shorter vascular event-free survival (HR [95% CI], 3.85 [1.90–7.81]; p<0.001) compared to those scored <15. Furthermore, comorbidities (HR, 1.17 [1.06–1.30]; p=0.003) and migraine characteristics (HR, 1.34 [1.05–1.71]; p=0.018) were significantly predictive of the composite endpoint, whereas other categories were not.