OBJECTIVES: Migraine, a highly prevalent and debilitating primary headache disorder, imposes significant socio-economic and personal impacts, which are exacerbated by misdiagnosis and delayed diagnosis. This systematic review of literature aims to synthesize evidence on migraine misdiagnosis and diagnostic delays to inform better disease management.

METHODS: A systematic search following the PRISMA guidelines was conducted in Embase^® and MEDLINE^® using the interfaces Embase.com and PubMed. Real world studies published in English language in the last 10 years (January 2014 – May 2024), with documented cases of migraine misdiagnosis as other disease or vice versa and/or delays in migraine diagnosis were included. No geographical or any other restrictions were applied.

RESULTS: A total of 884 records were retrieved from the two biomedical databases. After deduplication, title/abstract and full-text screening, 108 publications met the inclusion criteria. Further, linking of publications and bibliographic searching of relevant systematic reviews led to 98 unique extractions comprising 63 observational studies and 35 case reports. Misdiagnosis of migraine was reported in 89 studies, four studies reported diagnostic delays, and five reported both these scenarios. Thirty-seven studies reported false positive findings, where hemicrania continua (n=4), stroke (n=3), and epilepsy (n=3) were primarily misdiagnosed as migraine. Further, 56 studies reported false negative diagnosis, where migraine was predominantly misdiagnosed as transient ischemic attack (n=13), multiple sclerosis (n=10), and stroke (n=8). Misdiagnosis and delayed diagnosis not only divert resources inefficiently but also delays the administration of appropriate treatment. This adversely affects patients' quality of life, leads to poorer treatment outcomes, and increases the overall cost burden.

CONCLUSIONS: The review found that misdiagnoses of migraine was widely prevalent, emphasizing the necessity for advanced and precise diagnostic methods, and vigilant utilization of ICHD-3 guidelines. Data-driven algorithms utilizing artificial intelligence to perform predictive analytics can be adopted to enhance diagnostic accuracy, minimize diagnostic delays, and ultimately improve patient outcomes.