Predicting initiation of preventive migraine medications: exploratory study in a large U.S. medical claims database

Ford, Janet H.; Schroeder, Krista; Buse, Dawn C.; Joshi, Shivang; Gelwicks, Steve; Foster, Stephan L.; Aurora, Sheena K.

doi:10.1080/03007995.2019.1657716

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…Our study also found that the frequency of neurology visits was associated with a higher likelihood of preventive medication prescriptions, which is consistent with other studies 24 . This may be explained by the fact that patients with higher disease severity (and therefore candidates for preventive medication) are more likely to seek frequent care.…”

Section: Discussionsupporting

confidence: 92%

Association between clinician specialty and prescription of preventive medication for young adults with migraine: A retrospective cohort study

Shapiro,

Loder,

Shapiro

2023

Headache

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ObjectiveWe aimed to compare the prescribing patterns of preventive medications between pediatric and adult neurologists for young adults with migraine.BackgroundAlthough preventive medications are effective for adults with migraine, studies in children have failed to demonstrate similar efficacy. As a result, lifestyle modifications and non‐pharmacological interventions are often emphasized in children. It is not known whether young adults are prescribed preventive medications at different rates according to whether they are cared for by an adult or pediatric neurologist.MethodsWe performed a multicenter retrospective cohort analysis of patients with migraine aged 18–25 years who were seen by a pediatric or adult neurologist at Mass General Brigham Hospital between 2017 and 2021. The primary outcome was whether the patient received a prescription for any preventive medication during the study period.ResultsAmong the 767 included patients, 290 (37.8%) were seen by a pediatric neurologist. Preventive medications were prescribed for 131/290 (45.2%; 95% confidence interval [CI]: 39.5%, 51.0%) patients seen by a pediatric neurologist and 206/477 (43.2%; 95% CI: 39.0%, 47.7%) patients seen by an adult neurologist (p = 0.591). In the mixed effects logistic regression model, clinician specialty was not associated with preventive medication use (adjusted odds ratio [AOR] 1.20, 95% CI: 0.62, 2.31). Female sex (AOR 1.69, 95% CI: 1.07, 2.66) and number of visits during the study period (AOR 1.64, 95% CI: 1.49, 1.80) were associated with receiving preventive medication.ConclusionApproximately two fifths of young adults with migraine were prescribed preventive medications, and this proportion did not differ according to clinician specialty. Although these findings suggest that pediatric and adult neurologists provide comparable care, both specialties may be underusing preventive medications in this patient population.

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Section: Discussionsupporting

confidence: 92%

Association between clinician specialty and prescription of preventive medication for young adults with migraine: A retrospective cohort study

Shapiro,

Loder,

Shapiro

2023

Headache

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“…Real-world studies have reported the modality of preventive treatment prescription for general practitioners and neurologists. An observational study in the U.S. included 43,660 migraine patients receiving different preventive drugs in addition to acute treatment [4]. The study identified the main comorbidities associated with preventive drug prescription, such as sleep disorders in females, but there are no data on specific treatments and their effects.…”

Section: Introductionmentioning

confidence: 99%

Failure of preventive treatments in migraine: an observational retrospective study in a tertiary headache center

et al. 2020

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Background: Although the criteria for acute migraine treatment and prevention have been well described, there are still unmet needs, general underuse and low benefits of preventive drugs. The aim of the present study was to retrospectively observe the short-term effect of preventive treatment in a cohort of migraine patients attending a tertiary headache center, using data from electronic medical records. Methods: This was an observational retrospective cohort study based on data collected in a tertiary headache center. Data were extracted from an electronic dataset collected from January 2009 to December 2019. The main selection criteria were as follows: age of 18-75 years; diagnosis of migraine without aura (MO), migraine with aura (MA) or chronic migraine (CM); a control visit 3 months after the first access; and prescription of preventive treatment with level of evidence 1 as reported by Italian guidelines. As the primary outcome, we considered the change in the frequency of headache at the follow-up visit. Then, as secondary outcome measures, we used disability scores, intensity of headache, and allodynia. As predictive factors, we considered age, migraine duration, sex, headache frequency, allodynia, anxiety and depression at baseline, and comorbidity with fibromyalgia. Results: Among the 6430 patients screened, 2800 met the selection criteria, 1800 returned to the follow-up visit, 550 withdrew because of adverse events, and 1100 were included the analysis. One hundred thirty-four patients had a frequency reduction of 50% or more. Flunarizine was used for less severe migraine, with a better effect compared to those of other drugs (odds ratio: 1.48; p: 0.022). Low headache frequency and absent or mild allodynia predicted a better outcome. Conclusions: The mild effect of preventive drugs on migraine features and even the number of patients who were lost to follow-up or dropped out because of adverse events confirm that in severe and chronic patients, the first line of prevention can only delay a more focused therapeutic approach.

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“…Preventative migraine medications are often underutilized, leaving patients at-risk of medication overuse, disease progression, higher disability and increased healthcare costs [5][6][7][8][9]. Daily or weekly analgesic use, in fact, disproportionately increases the risk for chronic migraine as documented by a large prospective population survey [10].…”

Section: Literature Surveymentioning

confidence: 99%

Knowledge Data Analysis on Migraine Headaches by Using Optimal Classifiers

Kannan¹,

Thangarasu²

2022

INDJCSE

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Machine learning (ML) is largely used to develop automatic predictors in migraine classification but automatic predictors for medication overuse (MO) in migraine are still in their infancy. This research work finds Naïve Bayes and Naïve Bayes Updateable classifier of statistical learning are producing an efficient results compare with functional learning models. In Statistical learning Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 93.50% of accuracy; In Functional learning, Quadratic Discriminant Analysis is having 93% of accuracy which is highest efficient outcome compare with other functional models. In Statistical learning Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 0.93 of PPV; In Functional learning, Quadratic Discriminant Analysis is having highest PPV which is 0.93 of PPV. In Statistical learning Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 0.94 of TPR; In Functional learning, Quadratic Discriminant Analysis is having highest TPR which is 0.93 of TPR compare with other functional learning. In Statistical learning Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 0.99 of AUCROC; In Functional learning, Quadratic Discriminant Analysis and Multi-Layer Perceptron are having highest AUCROC and same AUCROC value which is 0.97 of AUCROC compare with other functional learning. In Statistical learning Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 0.97 of AUCPRC; In Functional learning, Quadratic Discriminant Analysis is having highest AUCPRC value which is 0.93 of AUCPRC Compare with other functional learning. In Statistical learning Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 0.93 of F1-Score;. In Functional learning, Quadratic Discriminant Analysis is having highest F1-Score value which is 0.93 of F1-Score value Compare with other functional learning. In Statistical learning Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 0.89 of k value; In Functional learning, Quadratic Discriminant Analysis is having highest k value which is 0.88 of k value Compare with other functional learning. In Statistical learning, Naïve Bayes and Naïve Bayes Updateable algorithms are having same as well as highest efficient outcome which is 0.90 of phi coefficient value; In Functional learning, Quadratic Discriminant Analysis is having highest phi coefficient value which is 0.88 of phi coefficient value Compare with other functional learning. This work finds that statistical learning models produce better outcome with lowest errors compare with functional model

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Predicting initiation of preventive migraine medications: exploratory study in a large U.S. medical claims database

Cited by 8 publications

References 51 publications

Association between clinician specialty and prescription of preventive medication for young adults with migraine: A retrospective cohort study

Association between clinician specialty and prescription of preventive medication for young adults with migraine: A retrospective cohort study

Failure of preventive treatments in migraine: an observational retrospective study in a tertiary headache center

Knowledge Data Analysis on Migraine Headaches by Using Optimal Classifiers

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