The burden of medication overuse headache and patterns of switching and discontinuation among triptan users: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: A synthesis of real-world discontinuation and switching patterns among triptan users and rates of acute medication use among patients with medication overuse headache (MOH) is needed to better understand the burden among patients with migraine. The study objectives were to: (1) synthesize rates of switching and discontinuation from triptans; (2) characterize acute medication use among patients with MOH; and (3) describe the associated burden. METHODS: A systematic literature review was conducted, under the Preferred Reporting Items for Systematic Review guidelines, using MEDLINE/EMBASE from database inception to July 2019. The search strategy targeted studies of adults with migraine, and included terms related to migraine and its treatment. Continuous variables were summarized using means, standard deviations, and ranges. Dichotomous and categorical variables were summarized using the number and proportion of individuals. RESULTS: Twenty studies were included; seven describing patterns of switching and discontinuation among triptan users, and 13 characterizing triptan overuse among patients with MOH. High rates of switching to non-specific acute medications and low two-year retention rates were reported; among US samples switching to opioids at the first refill (18.2%) or after 1-year (15.5%) was frequent. Compared to persistent use of triptans, switchers experienced greater headache related impact and either no improvement or increased headache-related disability. Rates of medication overuse by agent among patients with MOH varied greatly across the included studies, and only one study described factors associated with the risk of MOH (e.g. duration of medication overuse). Medication agent, increased headache frequency (p = .008), and increased disability (p = .045) were associated with unsuccessful withdrawal; patients overusing triptans were more successful at withdrawal than those overusing opioids or combination analgesics (P < .0001). CONCLUSIONS: The evidence summarized here highlights that rates of WCS are low and many patients turn to other acute medication at their first refill. Patients may experience no improvement in disability when switching from one triptan agent to another, or experience increasing disability and/or increasing migraine frequency when turning to traditional acute treatment for migraine. Variability in health care settings, patient severity, and study design contributed to heterogeneity across the synthesis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".