Evidence map›Paper›PMID 35642092›Full record

ArticleCephalalgia : an international journal of headache2022

A population-health approach to characterizing migraine by comorbidity: Results from the Mindfulness and Migraine Cohort Study.

Sylvia Ek Sudat, Alice S Jacobson, Andrew L Avins, Richard B Lipton, Alice R Pressman

Abstract read
In one paragraph

Article in Cephalalgia : an international journal of headache, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sylvia Ek SudatSutter Health Center for Health Systems Research, Walnut Creek, CA, USA.
Alice S JacobsonSutter Health Center for Health Systems Research, Walnut Creek, CA, USA.
Andrew L AvinsKaiser Permanente Division of Research, Oakland, CA, USA.
Richard B LiptonAlbert Einstein College of Medicine and the Montefiore Headache Center, Bronx, NY, USA.
Alice R PressmanSutter Health Center for Health Systems Research, Walnut Creek, CA, USA.

Funding

Mindfulness and Migraine: A Randomized Controlled TrialR01AT009081 · NCCIH · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI PRESSMAN, ALICE ROGOT · 2016 to 2018
$1.6M
Genetics and Comorbidity of MigraineR01NS080863 · NINDS · KAISER FOUNDATION RESEARCH INSTITUTE · PI PRESSMAN, ALICE ROGOT · 2012 to 2014
$979k
NCCIH NIH HHS R01 AT009081NINDS NIH HHS R01 NS080863
6 · The paper itself

Abstract

backgroundThe heterogeneity of migraine has been reported extensively, with identified subgroups usually based on symptoms. Grouping individuals with migraine and similar comorbidity profiles has been suggested, however such segmentation methods have not been tested using real-world clinical data.

objectiveTo gain insights into natural groupings of patients with migraine using latent class analysis based on electronic health record-determined comorbidities.

methodsRetrospective electronic health record data analysis of primary-care patients at Sutter Health, a large open healthcare system in Northern California, USA. We identified migraine patients over a five-year time period (2015-2019) and extracted 29 comorbidities. We then applied latent class analysis to identify comorbidity-based natural subgroups.

resultsWe identified 95,563 patients with migraine and found seven latent classes, summarized by their predominant comorbidities and population share: fewest comorbidities (61.8%), psychiatric (18.3%), some comorbidities (10.0%), most comorbidities - no cardiovascular (3.6%), vascular (3.1%), autoimmune/joint/pain (2.2%), and most comorbidities (1.0%). We found minimal demographic differences across classes.

conclusionOur study found groupings of migraine patients based on comorbidity that have the potential to be used to guide targeted treatment strategies and the development of new therapies.

Indexed as

Migraine DisordersMindfulnessCohort StudiesComorbidityHumansRetrospective Studieselectronic health recordHeadachelatent class analysisreal-world data

Identifiers

PMID35642092
PMCPMC9872270

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.