Background and purpose: Several variables have been reported to be associated with anti-calcitonin gene-related peptide (CGRP) receptor or ligand antibody response, but with differing results. Our objective was to determine whether machine-learning (ML)based models can predict 6-, 9-and 12-month responses to anti-CGRP receptor or ligand therapies among migraine patients. Methods:We performed a multicenter analysis of prospectively collected data from patients with migraine receiving anti-CGRP therapies. Demographic and clinical variables were collected. Response rates in the 30% to 50% range, or at least 30%, in the 50% to 75% range, or at least 50%, and response rate of at least 75% regarding the reduction in the number of headache days per month at 6, 9 and 12 months were calculated. A sequential forward feature selector was used for variable selection and ML-based predictive models for the response to anti-CGRP therapies at 6, 9 and 12 months, with model accuracy not less than 70%, were generated.

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