Prediction models for identifying medication overuse or medication overuse headache in migraine patients: a systematic review
Issued Date
2024-10-04
Resource Type
eISSN
11292377
Scopus ID
2-s2.0-85205605931
Pubmed ID
39363297
Journal Title
The journal of headache and pain
Volume
25
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
The journal of headache and pain Vol.25 No.1 (2024) , 165
Suggested Citation
Aramruang T., Malhotra A., Numthavaj P., Looareesuwan P., Anothaisintawee T., Dejthevaporn C., Sirirutbunkajorn N., Attia J., Thakkinstian A. Prediction models for identifying medication overuse or medication overuse headache in migraine patients: a systematic review. The journal of headache and pain Vol.25 No.1 (2024) , 165. doi:10.1186/s10194-024-01874-4 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101570
Title
Prediction models for identifying medication overuse or medication overuse headache in migraine patients: a systematic review
Corresponding Author(s)
Other Contributor(s)
Abstract
BACKGROUND: Migraine is a debilitating neurological disorder that presents significant management challenges, resulting in underdiagnosis and inappropriate treatments, leaving patients at risk of medication overuse (MO). MO contributes to disease progression and the development of medication overuse headache (MOH). Predicting which migraine patients are at risk of MO/MOH is crucial for effective management. Thus, this systematic review aims to review and critique available prediction models for MO/MOH in migraine patients. METHODS: A systematic search was conducted using Embase, Scopus, Medline/PubMed, ACM Digital Library, and IEEE databases from inception to April 22, 2024. The risk of bias was assessed using the prediction model risk of bias assessment tool. RESULTS: Out of 1,579 articles, six studies with nine models met the inclusion criteria. Three studies developed new prediction models, while the remaining validated existing scores. Most studies utilized cross-sectional and prospective data collection in specific headache settings and migraine types. The models included up to 53 predictors, with sample sizes from 17 to 1,419 participants. Traditional statistical models (logistic regression and least absolute shrinkage and selection operator regression) were used in two studies, while one utilized a machine learning (ML) technique (support vector machines). Receiver operating characteristic analysis was employed to validate existing scores. The area under the receiver operating characteristic (AUROC) for the ML model (0.83) outperformed the traditional statistical model (0.62) in internal validation. The AUROCs ranged from 0.84 to 0.85 for the validation of existing scores. Common predictors included age and gender; genetic data and questionnaire evaluations were also included. All studies demonstrated a high risk of bias in model construction and high concerns regarding applicability to participants. CONCLUSION: This review identified promising results for MO/MOH prediction models in migraine patients, although the field remains limited. Future research should incorporate important risk factors, assess discrimination and calibration, and perform external validation. Further studies with robust designs, appropriate settings, high-quality and quantity data, and rigorous methodologies are necessary to advance this field.