Machine-Learning Analysis Reveals Clusters of Migraine Attacks

A new study using machine-learning analysis on a vast group of migraine patients has identified subgroups with similar clinical and therapeutic response traits, according to Ali Ezzati, MD, the study’s author. Dr. Ezzati, an associate professor of neurology, believes that identifying more homogeneous groups will lead to better treatment efficacy. The current diagnostic criteria for migraines are deficient in categorizing their diversity, leading to suboptimal treatment responses of around 60%. The study reveals that people with depression are less responsive to treatments, particularly prescription medications. The researchers analyzed data from the American Migraine Prevalence and Prevention Study, revealing five groups. Triptans were most commonly used in clusters 2,3, and 5 and less so in cluster 4. Pain freedom at two hours was most common in cluster 1 followed by cluster 2. The findings may lead to more tailored migraine therapies for patients in the future. Catherine Chong, MD, who chaired the session where the research was presented, praised the study and called for further research to identify additional subgroups within the data.