Simultaneous Comparisons of 25 Acute Migraine Medications Based on 10 Million Users' Self-Reported Records From a Smartphone Application
Bibliographic record
Abstract
<h3>Background</h3> Many acute treatment options exist for migraine. However, large-scale, head-to-head comparisons of treatment effectiveness from real-world patient experience reports are lacking. <h3>Methods</h3> This is a retrospective analysis of 10,842,795 migraine attack records extracted from an e-diary smartphone application between June 30, 2014, and July 2, 2020. We analyzed 25 acute medications among seven classes- acetaminophen, NSAIDs, triptans, combination analgesics, ergots, anti-emetics, and opioids. Gepants and ditan were not included in this analysis. Different doses and formulations of each medication, according to the generic names, were combined in this analysis. We employed a two-level nested logistic regression model to analyze the odds ratio (OR) of treatment effectiveness of each medication by adjusting concurrent medications and the covariance within the same user. Subgroup analyses were conducted for users in the United States (US), the United Kingdom (UK), and Canada (CAN). <h3>Results</h3> Our final analysis included 4,777,524 medication-outcome pairs from 3,119,517 migraine attacks among 278,006 users. Triptans (mean OR 4.8), ergots (mean OR 3.02), and anti-emetics (mean OR 2.67) were the top three classes of medications with the highest effectiveness, followed by opioids (mean OR 2.49), NSAIDs (other than ibuprofen, mean OR 1.94), combination analgesics (acetaminophen/acetylsalicylic acid/caffeine) (OR 1.69, 95% CI 1.67-1.71), others (OR 1.49, 95% CI 1.47-1.50), and acetaminophen (OR 0.83, 95% CI 0.83-0.84), using ibuprofen as the reference. Individual medications with the highest ORs were eletriptan (OR 6.1, 95% CI 6.0-6.3), zolmitriptan (OR 5.7, 95% CI 5.6-5.8), and sumatriptan (OR 5.2, 95% CI 5.2-5.3). The ORs of acetaminophen, NSAIDS, combination analgesics and opioids were mostly around or less than 1, suggesting similar or lower reported effectiveness compared to ibuprofen. The ORs for 24 medications, except that of acetylsalicylic acid, achieved statistical significance with p <0.0001, and our nested logistic regression model achieved an area under the curve (AUC) of 0.849. Country-specific subgroup analyses revealed similar ORs of each medication and AUC (US 0.849, UK 0.864, and CAN 0.842), demonstrating the robustness of our analysis. <h3>Discussion</h3> Using a big-data approach, we analyzed patient-generated real-time records of 10 million migraine attacks and conducted simultaneous head-to-head comparisons of 25 acute migraine medications. Our findings that triptans, ergots and anti-emetics are the most effective classes of medications align with the guideline recommendations and offer generalizable insights to complement clinical practice. <h3>Classification of Evidence:</h3> This study provides Class IV evidence that for patients with migraine, selected acute medications (e.g., triptans, ergots, anti-emetics) are associated with higher odds of user-rated positive response than ibuprofen.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".