Chen Qiuyi, Zhang Jiarun, Cao Baicheng, Hu Yihan, Kong Yazhuo, Li Bin, Liu Lu
Department of Acupuncture and Moxibustion, Beijing Key Laboratory of Acupuncture Neuromodulation, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China.
Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100101, China.
J Headache Pain. 2025 Feb 12;26(1):32. doi: 10.1186/s10194-025-01972-x.
Migraine 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.
A 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.
After 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.
This 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.
PROSPERO, CRD42024621366.
偏头痛是一种复杂的神经系统疾病，临床变异性显著，给有效管理带来挑战。偏头痛有多种治疗方法，但个体反应差异很大，因此准确预测对于个性化治疗至关重要。本研究旨在探讨使用统计和机器学习模型来预测偏头痛患者的治疗反应。
进行了一项系统评价和荟萃分析，以评估偏头痛治疗反应预测模型的性能和质量。从PubMed、Cochrane对照试验注册库、Embase和Web of Science等数据库中检索相关研究，检索截至2024年11月30日。使用PROBAST工具评估偏倚风险，并使用TRIPOD+AI清单评估报告标准的依从性。
在筛选了1927篇文献后，有10项研究符合纳入标准，其中6项纳入了定量综合分析。提取的关键数据包括样本特征、干预类型、反应结果、建模方法和预测性能指标。曲线下面积（AUC）的汇总分析得出的值为0.86（95%CI：0.67-0.95），表明具有良好的预测性能。然而，根据PROBAST工具评估，纳入的研究普遍存在较高的偏倚风险，尤其是在分析领域。
本综述强调了统计和机器学习模型在预测偏头痛患者治疗反应方面的潜力。然而，高偏倚风险和显著的异质性强调了解释时需谨慎。未来的研究应侧重于使用高质量、全面和多中心数据集开发模型，进行严格的外部验证，并遵循TRIPOD+AI等标准化指南。纳入多模态磁共振成像（MRI）数据、探索偏头痛症状与治疗的相互作用，以及建立统一的结局测量、样本量计算和缺失数据处理方法，将提高模型的可靠性和临床适用性，最终改善患者结局并减轻医疗负担。
PROSPERO，CRD42024621366。
