Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

Jun 21, 2022European journal of neurology

Using machine learning to predict migraine patient response to two types of anti-CGRP antibody treatments in Spain

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Abstract

A total of 712 patients were included, with 84% experiencing chronic migraine.

Machine-learning models demonstrated an F1 score range of 0.70-0.97 for predicting response to anti-CGRP therapies.
The area under the receiver-operating curve scores ranged from 0.87 to 0.98, indicating strong predictive accuracy.
Key variables influencing predictions included headache days per month, migraine days per month, and the Headache Impact Test (HIT-6).
Predictions were made for response rates at 6, 9, and 12 months post-therapy.

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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.

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Machine‐learning‐based approach for predicting response to anti‐calcitonin gene‐related peptide (CGRP) receptor or ligand antibody treatment in patients with migraine: A multicenter Spanish study

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