Utility of machine-learning based models to predict 30%, 50% and 75% response to anti-CGRP response in patients with migraine: a multicenter Spanish study

Abstract text (incl. figure legends and references)

Objective: To date, several variables have been associated with anti-CGRP receptor or ligand-antibody response with disparate results. Our objective is to determine whether machine learning (ML)-based models can predict 6, 9 and 12 months response to anti-CGRP receptor or ligand therapies among migraine patients.

Methods: We performed a multicenter analysis of a prospectively collected data cohort of patients with migraine from 8 tertiary hospitals receiving anti-CGRP therapies. Demographic and clinical variables were collected. Response rate defined in the 30% to 50% range-or at least 30%-, in the 50% to 75% range-or at least 50%-, and response rate over 75% reduction in the number of headache days per month at 6, 9 and 12 months. A sequential forward feature selector was used for variable selection and ML-based predictive model response to anti-CGRP therapies at 6, 9 and 12 months, with models" accuracy not less than 70%, were generated.

Results: A total of 712 patients were included,93% women, aged 48 years(SD=11.7). Eighty-three percent had chronic migraine. ML models using headache days/month, migraine days/month and HIT-6 variables yielded predictions with a F1 score range of 0.70-0.97 and AUC (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: According to our study, ML models can predict anti-CGRP response at 6, 9 and 12 months using commonly collected clinical variables. This study provides a useful predictive tool to be used in a real-world setting.