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

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.

SUBMITTER: Pavlovic JM 

PROVIDER: S-EPMC6604052 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature