JOURNAL ARTICLE
RESEARCH SUPPORT, NON-U.S. GOV'T
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Migraine progression in subgroups of migraine based on comorbidities: Results of the CaMEO Study.

Neurology 2019 December 11
OBJECTIVE: To test the hypothesis that statistically defined subgroups of migraine (based on constellations of comorbidities and concomitant conditions; henceforth comorbidities), previously identified using Chronic Migraine Epidemiology and Outcomes (CaMEO) Study data, differ in prognosis, as measured by rates of progression from episodic migraine (EM) to chronic migraine (CM).

METHODS: The onset of CM was assessed up to 4 times over 12 months in individuals with EM and ≥1 comorbidity at baseline, based on constellations of comorbidities (comorbidity classes). The "fewest comorbidities" class served as reference. Individuals completing ≥1 follow-up survey from the web-based CaMEO Study were included. Covariates included sociodemographic variables and headache characteristics. Sex, income, cutaneous allodynia, and medication overuse were modeled as binary variables; age, body mass index, headache-related disability (Migraine Disability Assessment [MIDAS]), and Migraine Symptom Severity Scale as continuous variables. CM onset was assessed using discrete time analysis.

RESULTS: In the final sociodemographic model, all comorbidity classes had significantly elevated hazard ratios (HRs) for risk of progression to CM from EM, relative to fewest comorbidities. HRs for CM onset ranged from 5.34 (95% confidence interval [CI] 3.89-7.33; p ≤ 0.001) for most comorbidities to 1.53 (95% CI 1.17-2.01; p < 0.05) for the respiratory class. After adjusting for headache covariates independently, each comorbidity class significantly predicted CM onset, although HRs were attenuated.

CONCLUSIONS: Subgroups of migraine identified by comorbidity classes at cross-section predicted progression from EM (with ≥1 comorbidity at baseline) to CM. The relationship of comorbidity group to CM onset remained after adjusting for indicators of migraine severity, such as MIDAS.

CLINICALTRIALSGOV IDENTIFIER: NCT01648530.

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