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A nomogram for the prediction of response to anti-CGRP mAbs: the CGRP score
Journal article   Open access   Peer reviewed

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

Marina Romozzi, Ammar Lokhandwala, Catello Vollono, David García-Azorín, Giulia Vigani, Francesco De Cesaris, Claudia Altamura, Fabrizio Vernieri, Paolo Calabresi, Sonia Di Tella, …
Journal of headache and pain, v 26(1), 190
01 Sep 2025
DOI: https://doi.org/10.1186/s10194-025-02138-5
PMID: 40890582
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1186/s10194-025-02138-5View
Published, Version of Record (VoR) Open

Abstract

AI Advancements in Headache Research: Navigating the Digital Frontier Medicine Medicine & Public Health Pain Medicine Internal Medicine Neurology
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” ( C GRP mAbs G lobal R esponse P rediction). 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.

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Clinical Neurology
Neurosciences
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