Development and Internal Validation of a Multivariable Prediction Model for Individual Episodic Migraine Attacks Based on Daily Trigger Exposures.
Development and Internal Validation of a Multivariable Prediction Model for Individual Episodic Migraine Attacks Based on Daily Trigger Exposures.
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开发并内部验证基于患者自我预测和暴露于常见触发因素的新发偏头痛天数的多变量预测模型。每日偏头痛发作风险的准确实时预测可以帮助发作性偏头痛患者在易受影响的时间段靶向预防药物，并有助于减轻疾病负担。人们对常见偏头痛触发因素的预测效用知之甚少。我们通过在线论坛招募患有阵发性偏头痛的成年人，参与一项为期90天的前瞻性每日日记队列研究，该研究通过iPhone定制研究应用程序进行。每天晚上，参与者回答有关偏头痛发生和潜在预测因素的问题，包括压力、睡眠、咖啡因和酒精的摄入、月经和自我预测。我们开发并估计了新发作偏头痛日与健康日风险的多变量多水平逻辑回归模型，并使用重复交叉验证对模型进行了内部验证。我们有178名参与者完成了研究，并有资格进行初步分析，其中包括1870例偏头痛事件。我们发现，咖啡因摄入量的减少、头痛自我预测的概率较高、压力水平较高以及月经开始后2天内的次数与第二天偏头痛的风险呈正相关。多变量模型预测偏头痛风险仅略好于偶然性（人内c统计量：0.56,95% CI: 0.54, 0.58）。在这项研究中，基于自我预测或自我报告的常见触发因素暴露，无法预测间歇性偏头痛发作。为了建立临床上有用的偏头痛预测模型，需要提高数据收集的准确性和广度。
To develop and internally validate a multivariable predictive model for days with new-onset migraine headaches based on patient self-prediction and exposure to common trigger factors. Accurate real-time forecasting of one’s daily risk of migraine attack could help episodic migraine patients to target preventive medications for susceptible time periods and help decrease the burden of disease. Little is known about the predictive utility of common migraine trigger factors. We recruited adults with episodic migraine through online forums to participate in a 90-day prospective daily-diary cohort study conducted through a custom research application for iPhone. Every evening, participants answered questions about migraine occurrence and potential predictors including stress, sleep, caffeine and alcohol consumption, menstruation, and self-prediction. We developed and estimated multivariable multilevel logistic regression models for the risk of a new-onset migraine day vs a healthy day and internally validated the models using repeated cross-validation. We had 178 participants complete the study and qualify for the primary analysis which included 1870 migraine events. We found that a decrease in caffeine consumption, higher self-predicted probability of headache, a higher level of stress, and times within 2 days of the onset of menstruation were positively associated with next-day migraine risk. The multivariable model predicted migraine risk only slightly better than chance (within-person C-statistic: 0.56, 95% CI: 0.54, 0.58). In this study, episodic migraine attacks were not predictable based on self-prediction or on self-reported exposure to common trigger factors. Improvements in accuracy and breadth of data collection are needed to build clinically useful migraine prediction models.
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