Predictors of Treatment-response to Caffeine Combination Products, Acetaminophen, Acetylsalicylic Acid (Aspirin), and Nonsteroidal Anti-inflammatory Drugs in Acute Treatment of Episodic Migraine

Ali Ezzati MD; Kristina M. Fanning PhD; Michael L. Reed PhD; Richard B. Lipton MD

Headache. 2023;63(3):342-352.

Abstract and Introduction

Abstract

Objective: To identify predictors of acute treatment optimization for migraine with "over-the-counter" (OTC) or prescription nonsteroidal anti-inflammatory drugs (NSAIDs) as well as other widely used OTCs including acetaminophen, caffeine combination products (CCP), and acetylsalicylic acid (ASA, aspirin) among people with episodic migraine and to develop models that predict treatment response to each class of OTCs.

Background: Efficacy of acute OTC medications for migraine varies greatly. Identifying predictors of treatment response to particular classes of medication is a step toward evidence-based personalized therapy.

Methods: For this prediction model development study, we used data from 2224 participants from the American Migraine Prevalence and Prevention (AMPP) study who were aged ≥18 years, met criteria for migraine, had <15 monthly headache days, and reported being on monotherapy for acute migraine attacks with one of the following classes medications: CCP (N = 711), acetaminophen (N = 643), ASA (N = 110), and prescription or OTC NSAIDs (N = 760). The primary outcome measures of treatment optimization were adequate 2-h pain freedom (2hPF) and adequate 24-h pain relief (24hPR), which were defined by responses of half the time or more to the relevant items on the Migraine Treatment Optimization Questionnaire-6.

Results: The mean (SD) age of the participants was 46.2 (13.1) years, 79.4% (1765/2224) were female, 43.7% (972/2224) reported adequate 2hPF, and 46.1% (1025/2224) reported adequate 24hPR. Those taking CCP had better 2hPF and 24PR outcomes. For those taking NSAIDs, better outcomes were associated with lower average pain intensity (2hPF: odds ratio [OR] 0.89, 95% confidence interval [CI] 0.80–0.99; 24PR: OR 0.86, 95% CI 0.77–0.96), cutaneous allodynia (2hPF: OR 0.92, 95% CI 0.89–0.96; 24PR: OR 0.91, 95% CI 0.87–0.95), depressive symptoms (2hPF: OR 0.95, 95% CI 0.92–0.98; 24PR: OR 0.95, 95% CI 0.91–0.99), and Migraine Disability Assessment Scale (MIDAS) grade (2hPF: OR 0.76, 95% CI 0.64–0.90; 24PR: OR 0.79, 95% CI 0.65–0.95). Adequate 2hPF for those taking CCP was associated with male gender (OR 1.83, 95% CI 1.21–2.77), lower average pain intensity (OR 0.80, 95% CI 0.70–0.91), lower cutaneous allodynia (OR 0.94, 95% CI 0.90–0.97), and lower Migraine Symptom Severity Scale Score (MSSS; OR 0.91, 95% CI 0.86–0.97). Adequate 24hPR for those taking CCP was associated with lower average pain intensity (OR 0.85, 95% CI 0.75–0.96), lower cutaneous allodynia (OR 0.92, 95% CI 0.89–0.96), and lower MIDAS grade (OR 0.81, 95% CI 0.68–0.96). Participants who were married (OR 1.51, 95% CI 1.05–2.19), had lower average pain intensity (OR 0.79, 95% CI 0.70–0.89), lower MSSS (OR 0.93, 95% CI 0.88–0.99), less depression (OR 0.96, 95% CI 0.93–0.99), and lower MIDAS grade (OR 0.72, 95% CI 0.59–0.87) had adequate 2hPF after taking acetaminophen. Participants who were married (OR 1.50, 95% CI 1.02–2.21), had lower pain intensity (OR 0.78, 95% CI 0.69–0.88), less depression (OR 0.95, 95% CI 0.91–0.98) and lower MIDAS grade (OR 0.53, 95% CI 0.42–0.67) had higher 24hPR following use of acetaminophen. A lower MSSS was the only factor associated with higher 2hPF and 24PR after using ASA (OR 0.78, 95% CI 0.67–0.92 and OR 0.79, 95% CI 0.67–0.93). Predictive models had modest performance in identifying responders to each class of OTC.

Conclusion: A large subgroup of people with migraine had an inadequate response to their usual acute OTC migraine treatment 2- and 24-h after dosing. These findings suggest a need to improve OTC treatment for some and to offer prescription acute medications for others. Predictive models identified several factors associated with better treatment-response in each OTC class. Selecting OTC treatment based on factors predictive of treatment optimization might improve patient outcomes.

Introduction

Migraine is a primary headache disorder typically characterized by moderate or severe, unilateral, throbbing headache accompanied by other clinical symptoms such as nausea, vomiting, photophobia, phonophobia, aura and premonitory as well as postdromal clinical symptoms.^[1,2] The heterogeneity of migraine in terms of symptom profiles, clinical course, treatment response and outcome is well known.^[3] This heterogeneity contributes to the substantial unmet treatment needs. Strategies for individualizing treatment for patients may help address these unmet needs.^[4–6]

Pharmacologic treatments of acute migraine include non-prescription (over-the-counter [OTC]) and prescription medications, which could be targeting general pain pathways or migraine-specific pathways.^[7] Virtually everyone with migraine has taken or is currently taking an OTC acute treatment.^[6] Based on the current American Headache Society (AHS) consensus statement, OTCs including nonsteroidal anti-inflammatory drugs (NSAIDs), non-opioid analgesics, acetaminophen, or caffeine combination products (CCP; e.g., acetylsalicylic acid [ASA, aspirin] + acetaminophen + caffeine) are the first recommended line of therapy for treatment of acute attacks with mild-to-moderate pain intensity. Migraine-specific agents such as triptans or dihydroergotamine are recommended for treatment of acute attacks with moderate or severe intensity and those that respond poorly to first-line therapy.^[8] Gepants (the small molecule calcitonin gene-related peptide receptor antagonists) and ditans (5-hydroxytryptamine receptor 1F agonists) are newer classes of migraine-specific treatments, although their availability is limited by insurance regulations. As evident by guidelines and the AHS consensus statement, recommendations for selection and sequencing acute migraine therapies are primarily based on the level of pain intensity at the time of treatment; other associated symptoms and comorbidities are often overlooked. The process for selection of OTCs for treatment of migraine attacks are even less knowledge based, as patients frequently try OTCs prior to consultation with a medical professional and their selection and sequencing of OTCs may be determined by cost, advertisements, and other factors.

Overall, heterogeneity in migraine symptoms and response to treatment can lead to inadequate treatment of disease, decreased patient satisfaction, medication overuse, and increased burden and risk of disease progression.^[9,10] Prior studies suggest that more than half of patients who try a new medication for acute migraine do not respond adequately to the treatment.^[11,12] Accounting for disease heterogeneity and evidence-based selection of medications based on migraines symptoms and other patient characteristics can potentially improve patient outcomes through a personalized treatment approach.^[11]

Our group has previously used data from the American Migraine Prevalence and Prevention (AMPP) study to identify factors associated with response to acute prescription treatments, identifying male gender, higher headache pain intensity, presence of cutaneous allodynia, and depression as factors associated with poor response. In a recent study, we showed that higher headache pain intensity, higher migraine symptoms severity scale score, and more depressive symptoms are factors associated with poorer response to OTC treatment. While models developed in our previous study are useful for prediction of overall response to all classes of OTC medications, the path toward developing personalized treatment approaches for migraine demands developing and validating other types of prediction models. Such models identify predictors of response to each specific class of treatment, predict response to specific regimens of treatment defined by dose or frequency of treatment, and models for predicting response to combination therapies. In this study, we aimed to take the next step toward developing more specific models for predicting personalized treatment response. In the previous study, we identified factors associated with response to individual classes of OTC medications including NSAIDs, ASA, acetaminophen, and CCP. In the present study, models identify factors that predict response to specific classes of OTC medications. We hypothesized that a different combination of features including headache symptoms, comorbidities, and disability would predict major treatment outcomes of freedom from pain and symptom relief for each class of OTC medications. Furthermore, we hypothesized that models developed using these predictors would add incremental value in predicting treatment-response for each class of OTC medication.

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Abstract and Introduction
Methods
Results
Discussion
References

Table 1. Sample characteristics for the whole population and stratified by "over-the-counter" (OTC) medication class among participants with migraine on acute monotherapy with OTCs or nonsteroidal anti-inflammatory drugs.
Descriptive statistics	Total		NSAIDs		CCP		ASA		Acetaminophen
	N = 2224		N = 760		N = 711		N = 110		N = 643
	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Age, years	46.2	13.1	44.04	12.26	46.1	12.4	53.56	14.0	47.5	14.2
Female, %	79.4		80.8		80.6		56.4		80.2
Race, White, %	88.5		89.2		91.0		84.8		90.0
Race, Black, %	5.7		5.1		4.5		8.6		0.9
Have insurance, % (yes)	83.5		85.0		84.7		83.5		81.7
Body mass index, kg/m²	28.5	7.5	28.6	7.5	28.0	6.6	28.2	6.8	29.2	8.3
Pain severity (1–10)	8.1	1.5	8.1	1.5	8.4	1.3	7.8	1.7	8.0	1.6
Monthly headache days (q2/3)	2.6	2.5	2.5	2.3	3.0	2.7	2.3	2.6	2.4	2.4
Migraine Symptom Severity Score	17.0	3.0	17.1	2.9	17.5	2.9	16.3	3.1	16.6	3.2
MIDAS	7.5	10.8	7.5	11.8	7.8	10.4	7.6	14.6	7.0	9.1
PHQ-9 for depression (1–27)	5.1	5.3	5.2	5.3	4.5	4.9	4.6	4.5	5.7	5.8
Cutaneous allodynia	4.2	4.3	4.3	4.3	4.3	4.4	3.16	3.7	3.9	4.4
Adequate pain freedom 2 h, %	43.7		43.2		47.8		41.8		40.1
Adequate pain relief 24 h, %	46.1		44.9		49.8		43.5		45.5

Abbreviations: ASA, acetylsalicylic acid; CCP, caffeine combination products; MIDAS, Migraine Disability Assessment Scale; NSAIDs, nonsteroidal anti-inflammatory drugs; PHQ-9, nine-item Physician Health Questionnaire.

Table 2. Estimates, standard errors, and predictors of 2-h pain freedom in the stepwise logistic regression analysis.
Treatment class	Predictor	B	SE	Wald	p	Exp(B) (95% CI)
NSAIDs	Pain intensity	−0.12	0.05	4.46	0.035	0.89 (0.80–0.99)
	Cutaneous allodynia	−0.07	0.02	12.74	<0.001	0.92 (0.89–0.96)
	PHQ-9	−0.04	0.02	7.83	0.005	0.95 (0.92–0.98)
	MIDAS grade	−0.27	0.09	9.96	0.002	0.76 (0.64–0.90)
	Constant	1.40	0.44	10.02	0.002	4.08
CCP	Pain intensity	−0.22	0.07	10.59	0.001	0.80 (0.70–0.91)
	Cutaneous allodynia	−0.06	0.02	9.06	0.003	0.94 (0.90–0.97)
	Male sex	0.61	0.21	8.27	0.004	1.83 (1.21–2.77)
	MSSS	−0.09	0.03	7.83	0.005	0.91 (0.86–0.97)
	Constant	2.51	0.77	10.54	0.001	12.32
Acetaminophen	Male sex	−0.45	0.23	3.91	0.048	0.63 (0.40–0.99)
	Married	0.41	0.19	4.89	0.027	1.51 (1.05–2.19)
	Pain intensity	−0.23	0.06	13.82	<0.001	0.79 (0.70–0.89)
	MSSS	−0.07	0.03	5.19	0.023	0.93 (0.88–0.99)
	PHQ-9	−0.04	0.02	4.33	0.037	0.96 (0.93–0.99)
	MIDAS grade	−0.33	0.10	10.66	0.001	0.72 (0.59–0.87)
	Constant	3.15	0.62	25.69	<0.001	23.40
ASA	MSSS	−0.24	0.08	8.75	0.003	0.78 (0.67–0.92)
ASA	Constant	3.45	1.30	7.01	0.008	31.42

Note: Predictors in models include age, sex, marital status (married or not), body mass index, having health insurance (yes/no), average headache pain intensity (range: 0–10), Migraine Symptom Severity Scale Score (MSSS; range 0–21), Migraine Disability Assessment Test (MIDAS; categorical); cutaneous allodynia (12-item Allodynia Symptom Checklist; range 0–24), depression (nine-item Physician Health Questionnaire [PHQ-9]; range 0–27), and current use of preventive medication (yes/no). Variables that were not significantly associated with outcomes were excluded from the final model presented in this table.
Abbreviations: ASA, acetylsalicylic acid; CCP, caffeine combination products; CI, confidence interval; NSAIDs, nonsteroidal anti-inflammatory drugs; SE, standard error.

Table 3. Estimates, standard errors, and predictors of 24-h pain relief in the stepwise logistic regression analysis.
Treatment class	Predictor	B	SE	Wald	p	Exp(B) (95% CI)
NSAIDs	Pain intensity	−0.15	0.06	6.66	0.010	0.86 (0.77–0.96)
	Cutaneous allodynia	−0.09	0.02	14.79	<0.001	0.91 (0.87–0.95)
	PHQ-9	−0.04	0.02	5.54	0.019	0.95 (0.91–0.99)
	MIDAS grade	−0.23	0.10	5.76	0.016	0.79 (0.65–0.95)
	Constant	1.12	0.45	6.05	0.014	3.07
CCP	Pain intensity	−0.16	0.07	5.96	0.015	0.85 (0.75–0.96)
	Cutaneous allodynia	−0.08	0.02	12.42	<0.001	0.92 (0.89–0.96)
	MIDAS grade	−0.20	0.08	5.99	0.014	0.81 (0.68–0.96)
	Constant	1.18	0.53	4.85	0.028	3.26
Acetaminophen	Married	0.40	0.19	4.24	0.039	1.50 (1.02–2.21)
	Pain intensity	−0.24	0.06	15.74	<0.001	0.78 (0.69–0.88)
	PHQ-9	−0.05	0.02	6.54	0.010	0.95 (0.91–0.98)
	MIDAS grade	−0.62	0.12	26.30	<0.001	0.53 (0.42–0.67)
	Constant	1.53	0.50	9.12	0.003	4.64
ASA	MSSS	−0.23	0.08	7.76	0.005	0.79 (0.67–0.93)
ASA	Constant	2.91	1.32	4.83	0.028	18.40

Table 4. Performance of models in prediction of treatment-response at 2 and 24 h.
Outcome	Monotherapy class	Sensitivity, % (95% CI)	Specificity, % (95% CI)	PPV, % (95% CI)	NPV, % (95% CI)	Base rate, %^a
2hPF	NSAIDs	45.1 (39.6–50.7)	76.2 (71.8–80.1)	59.0 (53.9–63.9)	64.6 (62.0–67.1)	43.2
	CCP	55.8 (50.4–61.2)	68.5 (63.5–73.2)	61.9 (57.6–66.0)	62.9 (59.6–66.0)	47.8
	Acetaminophen	37.2 (31.3–43.4)	84.4 (80.4–87.9)	61.5 (54.7–68.0)	66.7 (64.4–69.0)	40.1
24hPR	NSAIDs	48.4 (43.0–53.8)	67.5 (62.8–72.0)	54.8 (50.4–59.1)	61.7 (57.7–64.5)	44.9
	CCP	61.3 (56.0–66.4)	52.7 (47.3–57.9)	56.2 (52.8–59.6)	57.9 (53.8 + 61.7)	49.7
	Acetaminophen	43.9 (38.0–50.0)	74.9 (70.1–79.3)	57.5 (51.2–62.8)	63.4 (60.6–66.1)	43.5

Abbreviations: CCP, caffeine combination products; CI, confidence interval; 2hPF, 2-h pain freedom; 24hPR, 24-h pain relief; NPV, negative predictive value; NSAIDs, nonsteroidal anti-inflammatory drugs; PPV, positive predictive value.
^aBase rate = pain freedom at each timeframe based on actual outcome.

Authors and Disclosures

Ali Ezzati MD¹, Kristina M. Fanning PhD², Michael L. Reed PhD³ and Richard B. Lipton MD¹

¹Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
²Mist Research, Wilmington, North Carolina, USA
³Vedanta Research, Chapel Hill, North Carolina, USA

Correspondence
Ali Ezzati, Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical Center, 1225 Morris Park Avenue, Bronx, NY 10461, USA. Email: ali.ezzati@einsteinmed.edu

Author Contributions
Study concept and design: Ali Ezzati, Richard B. Lipton. Acquisition of data: Richard B. Lipton, Michael L. Reed. Analysis and interpretation of data: Ali Ezzati, Kristina M. Fanning, Michael L. Reed, Richard B. Lipton. Drafting of the manuscript: Ali Ezzati, Richard B. Lipton. Revising it for intellectual content: Ali Ezzati, Kristina M. Fanning, Michael L. Reed, Richard B. Lipton. Final approval of the completed manuscript: Ali Ezzati, Kristina M. Fanning, Michael L. Reed, Richard B. Lipton.

Conflict of Interest
Ali Ezzati receives research support from the following sources unrelated to this manuscript: the National Institute of Health (NIA K23 AG063993); the Alzheimer's Association (2019-AACSF-641329), and Cure Alzheimer Fund. He serves as consultant, advisory board member, or has received honoraria from: PCORI Health Care Horizon Scanning System, GlaxoSmithKline, Mist Research, BioDelivery Sciences International Inc. Dr. Ezzati is the founder of Neurodiction, LLC, which has no financial ties with this work. Kristina M. Fanning is the managing director of MIST Research, LLC which received grants from the National Headache Foundation in addition to funding from Allergan, Amgen, Dr. Reddy's Laboratories/Promius, and Eli Lilly via collaboration with Vedanta Research. Michael L. Reed is the managing Director of Vedanta Research, which has received research funding from Allergan, Amgen, Dr. Reddy's Laboratories/Promius, and Eli Lilly, and grants from the National Headache Foundation. Richard B. Lipton receives research support from the NIH and the FDA as well as the the National Headache Foundation and the Marx Foundation. He serves on the editorial board of Neurology, senior advisor to Headache, and associate editor to Cephalalgia. He has reviewed for the NIA and NINDS, holds stock options in Biohaven Holdings and Manistee; serves as consultant, advisory board member, or has received honoraria from: Abbvie (Allergan), American Academy of Neurology, American Headache Society, Amgen, Avanir, Biohaven, Biovision, Boston Scientific, CoolTech, Dr. Reddy's (Promius), Electrocore, Eli Lilly, eNeura Therapeutics, GlaxoSmithKline, Grifols, Lundbeck (Alder), Pfizer, Teva, Trigemina, Vector, Vedanta. He receives royalties from Wolff's Headache 7th and 8th Edition, Oxford Press University, 2009, Wiley and Informa.

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Predictors of Treatment-response to Caffeine Combination Products, Acetaminophen, Acetylsalicylic Acid (Aspirin), and Nonsteroidal Anti-inflammatory Drugs in Acute Treatment of Episodic Migraine

Abstract and Introduction

Abstract

Introduction

Tables

Tables

Tables

Tables

References

Authors and Disclosures

Authors and Disclosures

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