Exploring the real-world: migraine and its comorbidities

Abstract

This thesis investigates how real-world data can be used in migraine research to answer clinically relevant questions about migraine types, outcomes, and comorbidities, while improving standardization and rigor. It particularly addresses challenges raised by the arrival of CGRP(R)-targeted monoclonal antibodies (CGRP(R)-mAbs), the need for accurate differentiation between migraine types (including migraine with aura), and reliable measurement of migraine endpoints in routine care.

The work is organized in two parts. Part I (Chapters 2–7) focuses on monitoring migraine using standardized digital and real-world approaches to support questions around CGRP(R)-mAbs, including validating an electronic headache diary, improving practical identification of visual aura, providing guidance for real-world evidence study design, evaluating switching strategies between CGRP(R)-mAb classes, and assessing long-term cardiovascular safety signals. It also considers study design trade-offs between placebo and active comparators.

Part II (Chapters 8–10) examines migraine comorbidities—affective disorders and visual snow—and how trigger and lifestyle factors relate to short-term (24-hour) forecasting of migraine attack risk, highlighting both the potential and limitations of predictive models.