RESUMO
BACKGROUND: Anti-CGRP monoclonal antibodies (anti-CGRP MAbs) are approved and available treatments for migraine prevention. Patients do not respond alike and many countries have reimbursement policies, which hinder treatments to those who might respond. This study aimed to investigate clinical factors associated with good and excellent response to anti-CGRP MAbs at 6 months. METHODS: European multicentre, prospective, real-world study, including high-frequency episodic or chronic migraine (CM) patients treated since March 2018 with anti-CGRP MAbs. We defined good and excellent responses as ≥50% and ≥75% reduction in monthly headache days (MHD) at 6 months, respectively. Generalised mixed-effect regression models (GLMMs) were used to identify variables independently associated with treatment response. RESULTS: Of the 5818 included patients, 82.3% were females and the median age was 48.0 (40.0-55.0) years. At baseline, the median of MHD was 20.0 (14.0-28.0) days/months and 72.2% had a diagnosis of CM. At 6 months (n=4963), 56.5% (2804/4963) were good responders and 26.7% (1324/4963) were excellent responders. In the GLMM model, older age (1.08 (95% CI 1.02 to 1.15), p=0.016), the presence of unilateral pain (1.39 (95% CI 1.21 to 1.60), p<0.001), the absence of depression (0.840 (95% CI 0.731 to 0.966), p=0.014), less monthly migraine days (0.923 (95% CI 0.862 to 0.989), p=0.023) and lower Migraine Disability Assessment at baseline (0.874 (95% CI 0.819 to 0.932), p<0.001) were predictors of good response (AUC of 0.648 (95% CI 0.616 to 0.680)). These variables were also significant predictors of excellent response (AUC of 0.691 (95% CI 0.651 to 0.731)). Sex was not significant in the GLMM models. CONCLUSIONS: This is the largest real-world study of migraine patients treated with anti-CGRP MAbs. It provides evidence that higher migraine frequency and greater disability at baseline reduce the likelihood of responding to anti-CGRP MAbs, informing physicians and policy-makers on the need for an earlier treatment in order to offer the best chance of treatment success.
RESUMO
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.