AI Evaluates Migraine Treatment with Big Data

The associate professor of neurology at Mayo Clinic Rochester provided perspective on a new study presented at the 2023 AAN Annual Meeting assessing the treatment effectiveness of real-world patients with migraine.

(Entire Article and Video from Neurology Live)


“In this study, we employed a generalized linear mixed model, specifically a two-level nested logistic regression model, which can be considered a type of machine learning approach as well. But in the future, there are certainly a lot of different types of machine learning applications and AI applications that can be used in this type of data.”

Over the years, the toolbox of therapeutic options for patients with migraine has grown exponentially, headlined by recent calcitonin gene-related peptide (CGRP) antagonists. Using a big-data approach, a recently conducted study simultaneously compared patient-reported treatment effectiveness of 25 acute migraine medications. Across 7 classes of acetaminophen, nonsteroidal anti-inflammatory drugs, triptans, combination analgesics, ergots, anti-emetics, and opioids, 10,842,795 migraine attacks were extracted for data.

Presented at the 2023 American Academy of Neurology (AAN) Annual Meeting, held April 22-27, in Boston, Massachusetts, the study showed that triptans (OR, 4.8), ergots (OR, 3.02), and anti-emetics (OR, 2.67) were the top 3 classes of medications with the highest effectiveness. Led by Chia-Chun Chiang, MD, the OR of treatment effectiveness was assessed through a 2-level nested logistic regression model that was adjusted on concurrent medications and the covariance within the same user.

Chiang, an associate professor of neurology at Mayo Clinic Rochester, sat down at the meeting to discuss the reasons for why her and her colleagues conducted this study, and the significance of the findings. She noted that gepants and ditans were not included because of the relatively lower numbers of usage when data was extracted. Additionally, Chiang spoke on the most notable data points migraine specialists should be aware of, and ways big-data approaches can effectively impact the field as technology continues to advance.

Author: Neurologica

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