Development of a claims-based algorithm to identify potentially undiagnosed chronic migraine patients. (April 2019)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Cephalalgia. Volume 39:Number 4(2019)
• Page Start: 465
• Page End: 476
• Publication Date: 2019-04
• Subjects: Chronic migraine -- diagnosis predictors -- case-finding tool -- health claims data; Headache -- Periodicals; 616.8491
• DOI: 10.1177/0333102418825373
• Languages: English
• ISSNs: 0333-1024

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.