Evidence map›Paper›PMID 30854881›Full record

Observational studyCephalalgia : an international journal of headache2019

Development of a claims-based algorithm to identify potentially undiagnosed chronic migraine patients.

Jelena M Pavlovic, Justin S Yu, Stephen D Silberstein, Michael L Reed, Steve H Kawahara, Robert P Cowan, Firas Dabbous, Karen L Campbell, Anand R Shewale, Riya Pulicharam and 3 more

Open access · greenAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Cephalalgia : an international journal of headache, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.5field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 1 country.

Jelena M Pavlovic1 Montefiore Headache Center, Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA.
Justin S Yu2 Allergan plc, Irvine, CA, USA.
Stephen D Silberstein3 Jefferson Headache Center, Philadelphia, PA, USA.
Michael L Reed4 Vedanta Research, Chapel Hill, NC, USA.
Steve H Kawahara5 DaVita Medical Group, El Segundo, CA, USA.
Robert P Cowan6 Stanford University School of Medicine, Stanford, CA, USA.
Firas Dabbous7 Independent consultant, La Jolla, CA, USA.
Karen L Campbell2 Allergan plc, Irvine, CA, USA.
Anand R Shewale2 Allergan plc, Irvine, CA, USA.
Riya Pulicharam5 DaVita Medical Group, El Segundo, CA, USA.
Jonathan W Kowalski2 Allergan plc, Irvine, CA, USA.
Hema N Viswanathan2 Allergan plc, Irvine, CA, USA.
Richard B Lipton8 Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.
Allergan (United States) · USAlbert Einstein College of Medicine · USHeadache Care Center · USStanford University · US

Funding

The relation of migraine to daily hormone patterns and menopausal stageK23AG049466 · NIA · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI PAVLOVIC, JELENA · 2016 to 2021
$918k
NIA NIH HHS K23 AG049466
6 · The paper itself

Abstract

objectiveTo develop a claims-based algorithm to identify undiagnosed chronic migraine among patients enrolled in a healthcare system.

methodsAn observational study using claims and patient survey data was conducted in a large medical group. Eligible patients had an International Classification of Diseases, Ninth/Tenth Revision (ICD-9/10) migraine diagnosis, without a chronic migraine diagnosis, in the 12 months before screening and did not have a migraine-related onabotulinumtoxinA claim in the 12 months before enrollment. Trained clinicians administered a semi-structured diagnostic interview, which served as the gold standard to diagnose chronic migraine, to enrolled patients. Potential claims-based predictors of chronic migraine that differentiated semi-structured diagnostic interview-positive (chronic migraine) and semi-structured diagnostic interview-negative (non-chronic migraine) patients were identified in bivariate analyses for inclusion in a logistic regression model.

resultsThe final sample included 108 patients (chronic migraine = 64; non-chronic migraine = 44). Four significant predictors for chronic migraine were identified using claims in the 12 months before enrollment: ≥15 versus <15 claims for acute treatment of migraine, including opioids (odds ratio = 5.87 [95% confidence interval: 1.34-25.63]); ≥24 versus <24 healthcare visits (odds ratio = 2.80 [confidence interval: 1.08-7.25]); female versus male sex (odds ratio = 9.17 [confidence interval: 1.26-66.50); claims for ≥2 versus 0 unique migraine preventive classes (odds ratio = 4.39 [confidence interval: 1.19-16.22]). Model sensitivity was 78.1%; specificity was 72.7%.

conclusionsThe claims-based algorithm identified undiagnosed chronic migraine with sufficient sensitivity and specificity to have potential utility as a chronic migraine case-finding tool using health claims data. Research to further validate the algorithm is recommended.

Indexed as

AlgorithmsAdultChronic DiseaseDatabases, FactualFemaleHumansInsurance Claim ReviewMaleMiddle AgedMigraine Disorderscase-finding toolChronic migrainediagnosis predictorshealth claims data

Identifiers

PMID30854881
PMCPMC6604052
OpenAlexW2921459216

What OpenQuestion holds

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Registered trials

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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.