Prediction models for treatment response in migraine: a systematic review and meta-analysis

BackgroundMigraine is a complex neurological disorder with significant clinical variability, posing challenges for effective management. Multiple treatments are available for migraine, but individual responses vary widely, making accurate prediction crucial for personalized care. This study aims to examine the use of statistical and machine learning models to predict treatment response in migraine patients.MethodsA systematic review and meta-analysis were conducted to assess the performance and quality of predictive models for migraine treatment response. Relevant studies were identified from databases such as PubMed, Cochrane Register of Controlled Trials, Embase, and Web of Science, up to 30th of November 2024. The risk of bias was evaluated using the PROBAST tool, and adherence to reporting standards was assessed with the TRIPOD + AI checklist.ResultsAfter screening 1,927 documents, ten studies met the inclusion criteria, and six were included in a quantitative synthesis. Key data extracted included sample characteristics, intervention types, response outcomes, modeling methods, and predictive performance metrics. A pooled analysis of the area under the curve (AUC) yielded a value of 0.86 (95% CI: 0.67-0.95), indicating good predictive performance. However, the included studies generally had a high risk of bias, particularly in the analysis domain, as assessed by the PROBAST tool.ConclusionThis review highlights the potential of statistical and machine learning models in predicting treatment response in migraine patients. However, the high risk of bias and significant heterogeneity emphasize the need for caution in interpretation. Future research should focus on developing models using high-quality, comprehensive, and multicenter datasets, rigorous external validation, and adherence to standardized guidelines like TRIPOD + AI. Incorporating multimodal magnetic resonance imaging (MRI) data, exploring migraine symptom-treatment interactions, and establishing uniform methodologies for outcome measures, sample size calculations, and missing data handling will enhance model reliability and clinical applicability, ultimately improving patient outcomes and reducing healthcare burdens.Trial registrationPROSPERO, CRD42024621366.