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.
Results: A total of 712 patients were included, 93% were women, and the mean (SD) age was 48 (11.6) years. Eighty-four percent of patients had chronic migraine. ML-based models using headache days/month, migraine days/month and the Headache Impact Test (HIT-6) yielded predictions with an F1 score range of 0.70-0.97 and an area under the receiver-operating curve score range of 0.87-0.98. SHAP (SHapley Additive exPlanations) summary plots and dependence plots were generated to evaluate the relevance of the factors associated with the prediction of the above-mentioned response rates.
Conclusions: Our results show that ML models can predict anti-CGRP response at 6, 9 and 12 months. This study provides a predictive tool that can be used in a real-world setting.
Keywords: CGRP; erenumab; fremanezumab; galcanezumab; machine-learning; migraine; predictor; response.
© 2022 European Academy of Neurology.