Simultaneous Comparisons of 25 Acute Migraine Medications Based on 10 Million Users' Self-Reported Records From a Smartphone Application
BACKGROUND AND OBJECTIVES: 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.
METHODS: 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 7 classes-acetaminophen, nonsteroid anti-inflammatory drugs (NSAIDs), triptans, combination analgesics, ergots, antiemetics, 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 used a 2-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, the United Kingdom, and Canada.
RESULTS: 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 antiemetics (mean OR 2.67) were the top 3 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 with 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 (United States 0.849, United Kingdom 0.864, and Canada 0.842), demonstrating the robustness of our analysis.
DISCUSSION: 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 antiemetics are the most effective classes of medications align with the guideline recommendations and offer generalizable insights to complement clinical practice.
CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that for patients with migraine, selected acute medications (e.g., triptans, ergots, antiemetics) are associated with higher odds of user-rated positive response than ibuprofen.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:101 |
|---|---|
| Enthalten in: |
Neurology - 101(2023), 24 vom: 12. Dez., Seite e2560-e2570 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chiang, Chia-Chun [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
362O9ITL9D |
|---|
| Anmerkungen: |
Date Completed 03.01.2024 Date Revised 18.01.2024 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1212/WNL.0000000000207964 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM365215376 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM365215376 | ||
| 003 | DE-627 | ||
| 005 | 20240118232005.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1212/WNL.0000000000207964 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1263.xml |
| 035 | |a (DE-627)NLM365215376 | ||
| 035 | |a (NLM)38030397 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chiang, Chia-Chun |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Simultaneous Comparisons of 25 Acute Migraine Medications Based on 10 Million Users' Self-Reported Records From a Smartphone Application |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 03.01.2024 | ||
| 500 | |a Date Revised 18.01.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a BACKGROUND AND OBJECTIVES: 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 | ||
| 520 | |a METHODS: 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 7 classes-acetaminophen, nonsteroid anti-inflammatory drugs (NSAIDs), triptans, combination analgesics, ergots, antiemetics, 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 used a 2-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, the United Kingdom, and Canada | ||
| 520 | |a RESULTS: 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 antiemetics (mean OR 2.67) were the top 3 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 with 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 (United States 0.849, United Kingdom 0.864, and Canada 0.842), demonstrating the robustness of our analysis | ||
| 520 | |a DISCUSSION: 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 antiemetics are the most effective classes of medications align with the guideline recommendations and offer generalizable insights to complement clinical practice | ||
| 520 | |a CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that for patients with migraine, selected acute medications (e.g., triptans, ergots, antiemetics) are associated with higher odds of user-rated positive response than ibuprofen | ||
| 650 | 4 | |a Journal Article | |
| 650 | 7 | |a Ibuprofen |2 NLM | |
| 650 | 7 | |a WK2XYI10QM |2 NLM | |
| 650 | 7 | |a Acetaminophen |2 NLM | |
| 650 | 7 | |a 362O9ITL9D |2 NLM | |
| 650 | 7 | |a Antiemetics |2 NLM | |
| 650 | 7 | |a Anti-Inflammatory Agents, Non-Steroidal |2 NLM | |
| 650 | 7 | |a Tryptamines |2 NLM | |
| 650 | 7 | |a Analgesics, Opioid |2 NLM | |
| 650 | 7 | |a Aspirin |2 NLM | |
| 650 | 7 | |a R16CO5Y76E |2 NLM | |
| 700 | 1 | |a Fang, Xuemin |e verfasserin |4 aut | |
| 700 | 1 | |a Horvath, Zsolt |e verfasserin |4 aut | |
| 700 | 1 | |a Cadiou, Francois |e verfasserin |4 aut | |
| 700 | 1 | |a Urani, Alexandre |e verfasserin |4 aut | |
| 700 | 1 | |a Poh, Weijie |e verfasserin |4 aut | |
| 700 | 1 | |a Narimatsu, Hiroto |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Yu |e verfasserin |4 aut | |
| 700 | 1 | |a Dodick, David W |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Neurology |d 1951 |g 101(2023), 24 vom: 12. Dez., Seite e2560-e2570 |w (DE-627)NLM000034355 |x 1526-632X |7 nnns |
| 773 | 1 | 8 | |g volume:101 |g year:2023 |g number:24 |g day:12 |g month:12 |g pages:e2560-e2570 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1212/WNL.0000000000207964 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 101 |j 2023 |e 24 |b 12 |c 12 |h e2560-e2570 | ||