Advancing toward precision migraine treatment: Predicting responses to preventive medications with machine learning models based on patient and migraine features

We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine-specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

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Publication Details

Journal
Headache The Journal of Head and Face Pain
Published
2024-08-23
DOI
https://doi.org/10.1111/head.14806
Citations
25
Primary Topic
Migraine and Headache Studies
Type
article
Field-Weighted Citation Impact
9.64

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article

Advancing toward precision migraine treatment: Predicting responses to preventive medications with machine learning models based on patient and migraine features

Narayan Kissoon, Todd J. Schwedt, Ann Murray, Imon Banerjee et al.
25 citations
Headache The Journal of Head and Face Pain
Migraine and Headache Studies
9.64
article

Advancing toward precision migraine treatment: Predicting responses to preventive medications with machine learning models based on patient and migraine features

Narayan Kissoon, Todd J. Schwedt, Ann Murray, Imon Banerjee, Monique M. Montenegro, Brandon Jones, Chieh‐Ju Chao, Han Wang, Mark Whealy, Gina Dumkrieger, Chia‐Chun Chiang, F. Michael Cutrer, Yi‐Chieh Chen, Liguo Wang, Heather A. Ouellette, Krista M. Burke, Jennifer I. Stern
article en
25 citations

Abstract

We developed an accurate prediction model for CGRP mAbs treatment response, leveraging detailed migraine features gathered from a headache questionnaire before starting treatment. Employing the same methods, the model performances for other medications were less impressive, though similar to the machine learning models reported in the literature for other diseases. This may be due to CGRP mAbs being migraine-specific. Incorporating medical comorbidities, genomic, and imaging factors might enhance the model performance. We demonstrated that migraine characteristics are important in predicting treatment responses and identified the most crucial predictors for each of the seven types of preventive medications. Our results suggest that precision migraine treatment is feasible.

Headache The Journal of Head and Face PainVol. 64(9)
University of Minnesota (US), Mayo Clinic (US), WinnMed (US), Mayo Clinic Health System (US), Mayo Clinic in Arizona (US), Mayo Clinic Hospital (US), Mayo Clinic in Florida (US), Arizona State University (US)
American Heart Association, Amgen, American Brain Foundation
Good health and well-being
Openalex Percentile: Top 1%
Migraine and Headache Studies
9.64
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