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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

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Project description:IntroductionNeuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.MethodsWe trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74).ResultsCM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine.ConclusionThe brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.

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

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