Prediction of medication overuse in patients with migraine using cox regression and machine learning: a real-world cohort
1
Issued Date
2026-12-01
Resource Type
ISSN
11292369
eISSN
11292377
Scopus ID
2-s2.0-105030313603
Pubmed ID
41580605
Journal Title
Journal of Headache and Pain
Volume
27
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Headache and Pain Vol.27 No.1 (2026)
Suggested Citation
Aramruang T., Numthavaj P., Looareesuwan P., Anothaisintawee T., Kunakorntham P., Pattanaprateep O., Dejthevaporn C., Thakkinstian A. Prediction of medication overuse in patients with migraine using cox regression and machine learning: a real-world cohort. Journal of Headache and Pain Vol.27 No.1 (2026). doi:10.1186/s10194-026-02269-3 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115273
Title
Prediction of medication overuse in patients with migraine using cox regression and machine learning: a real-world cohort
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Medication overuse (MO) is a critical issue for patients with migraine, contributing to chronification and medication overuse headache (MOH). Predicting those at risk is essential for effective management. This study aims to develop and compare time-to-event prediction models for MO/MOH among patients with migraine, using a cohort from electronic health records (EHRs). Methods: A prevalent new-user design of real-world data cohort of patients with migraine conducted at Ramathibodi Hospital, Thailand, from January 2010 to December 2023. The cohort was constructed using EHR data and incorporated common predictors related to the patient, physician, and treatment. Three time-to-event models were developed: Cox proportional hazards (CPH), random survival forests (RSF), and extreme gradient boosting (XGBoost). Model performance was evaluated on a hold-out testing dataset using discrimination and calibration. Variable importance in the machine learning models was assessed using Shapley Additive Explanations. Results: The study included 13,082 patients with migraine, with 3,456 identified as experiencing MO/MOH, indicating an incidence rate [95% confidence interval (CI)] of 56.31 (54.44–58.21) per 1,000 patient-years. On the testing dataset, the RSF model achieved a concordance index (C-index) of 0.645 (95% CI: 0.643–0.647), slightly outperforming the CPH model’s C-index of 0.635 (95% CI: 0.634–0.636). Additionally, the RSF model recorded the lowest integrated brier score (IBS) of 0.193 (95% CI: 0.192–0.194), compared to 0.195 (95% CI: 0.194–0.196) for the CPH model. The XGBoost model demonstrated lower performance, with a C-index of 0.611 (95% CI: 0.609–0.613) and an IBS of 0.197 (95% CI: 0.195–0.199). Across all models, clinic type, physician position, and history of MO/MOH were significant predictors. Conclusions: Using a real-world, EHR-derived cohort, we developed time-to-event prediction models incorporating multi-domain predictors to predict MO/MOH in patients with migraine. Although the models demonstrated only modest discrimination, their performance highlights the potential of CPH and machine learning algorithms in this context. External validation and the incorporation of additional clinical predictors, particularly those embedded in unstructured data, are needed.