A nomogram for the prediction of response to anti-CGRP mAbs: the CGRP score

Introduction: Real-world studies have explored potential predictors of response to anti-calcitonin gene related peptide (CGRP) monoclonal antibodies (mAbs), though results have remained inconsistent. Machine learning (ML) algorithms are becoming increasingly relevant in migraine research, offering a data-driven approach to identifying predictors of response to preventive treatments. To maximize their potential, a clinically applicable and user-oriented framework is needed to promote the use of these algorithms in research and, eventually, as supportive tools in clinical practice. Methods: This prospective cohort study included adults with migraine treated with anti-CGRP mAbs (anti-ligand and receptor) at two headache centers. Responders were defined as patients achieving ≥ 50% reduction in monthly headache days (MHDs) at 12 months. A logistic regression model was trained (80%) and tested (20%) using 11 baseline variables, including age, sex, migraine subtype, medication overuse, MHDs, and disability scores. Model performance was evaluated using accuracy, precision, recall, and F1-score. A nomogram was created for future research and clinical application. The model was then validated against an external test cohort treated with anti-CGRP mAbs. Results: Among 429 patients, 310 completed twelve months of treatment, with 236 (55.0%) classified as responders. The external test set included 109 patients. The ML model achieved an overall average weighted F1-score of 70.5% between the two test sets, with good performance in identifying “responders” (precision: 0.75, recall: 0.84, F1-score: 0.79). The model yielded predictions with an overall accuracy of 74% when tested against an external test cohort. Chronic migraine status, older age, and lower baseline MHDs were associated with higher response likelihood. Medication overuse and frequent analgesic use were negatively associated with response. The nomogram provided a clinically interpretable tool to estimate response probability, providing a total score named “CGRP Score” (CGRP mAbs Global Response Prediction). Conclusion: This ML-based predictive score achieved a good performance in identifying responders to anti-CGRP mAbs. The nomogram has the potential to be a practical, user-friendly tool for supporting clinical decision-making after validation. Supplementary information: The online version contains supplementary material available at 10.1186/s10194-025-02138-5.