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, Jonathan W. Kowalski, Hema N. Viswanathan, Richard B. Lipton

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Objective: To develop a claims-based algorithm to identify undiagnosed chronic migraine among patients enrolled in a healthcare system. Methods: An 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. Results: The 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%. Conclusions: The 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.

Original languageEnglish (US)
JournalCephalalgia
DOIs
StatePublished - Jan 1 2019

Fingerprint

Migraine Disorders
International Classification of Diseases
Odds Ratio
Confidence Intervals
Interviews
Logistic Models
Delivery of Health Care
Sex Ratio
Opioid Analgesics
Observational Studies

Keywords

  • case-finding tool
  • Chronic migraine
  • diagnosis predictors
  • health claims data

ASJC Scopus subject areas

  • Clinical Neurology

Cite this

Development of a claims-based algorithm to identify potentially undiagnosed chronic migraine patients. / Pavlovic, Jelena M.; Yu, Justin S.; Silberstein, Stephen D.; Reed, Michael L.; Kawahara, Steve H.; Cowan, Robert P.; Dabbous, Firas; Campbell, Karen L.; Shewale, Anand R.; Pulicharam, Riya; Kowalski, Jonathan W.; Viswanathan, Hema N.; Lipton, Richard B.

In: Cephalalgia, 01.01.2019.

Research output: Contribution to journalArticle

Pavlovic, JM, Yu, JS, Silberstein, SD, Reed, ML, Kawahara, SH, Cowan, RP, Dabbous, F, Campbell, KL, Shewale, AR, Pulicharam, R, Kowalski, JW, Viswanathan, HN & Lipton, RB 2019, 'Development of a claims-based algorithm to identify potentially undiagnosed chronic migraine patients', Cephalalgia. https://doi.org/10.1177/0333102418825373
Pavlovic, Jelena M. ; Yu, Justin S. ; Silberstein, Stephen D. ; Reed, Michael L. ; Kawahara, Steve H. ; Cowan, Robert P. ; Dabbous, Firas ; Campbell, Karen L. ; Shewale, Anand R. ; Pulicharam, Riya ; Kowalski, Jonathan W. ; Viswanathan, Hema N. ; Lipton, Richard B. / Development of a claims-based algorithm to identify potentially undiagnosed chronic migraine patients. In: Cephalalgia. 2019.
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abstract = "Objective: To develop a claims-based algorithm to identify undiagnosed chronic migraine among patients enrolled in a healthcare system. Methods: An 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. Results: The 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{\%}. Conclusions: The 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.",
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AU - Silberstein, Stephen D.

AU - Reed, Michael L.

AU - Kawahara, Steve H.

AU - Cowan, Robert P.

AU - Dabbous, Firas

AU - Campbell, Karen L.

AU - Shewale, Anand R.

AU - Pulicharam, Riya

AU - Kowalski, Jonathan W.

AU - Viswanathan, Hema N.

AU - Lipton, Richard B.

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