Forecasting Migraine Attacks

预测偏头痛发作

• 批准号: 10552024
• 负责人: TIMOTHY T HOULE
• 金额: $ 42.2万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10552024
• 关键词: American; Bayesian Forecast; Bayesian Method; Calibration; Clinical; Data; Discrimination; Disease; Equilibrium; Estimation Techniques; Exhibits; Frequencies; Future; Goals; Headache; Hour; Individual; Intercept; Laboratories; Learning; Literature; Machine Learning; Measurement; Migraine; Modeling; Moods; Online Systems; Pain; Parameter Estimation; Participant; Patient Self-Report; Performance; Persons; Pharmaceutical Preparations; Population Heterogeneity; Probability; Procedures; Rain; Research Personnel; Resolution; Risk; Sleep; Specific qualifier value; Statistical Models; Stress; Symptoms; System; Testing; Treatment Effectiveness; Uncertainty; Update; Weather; Weight; base; clinically relevant; diaries; experience; improved; neuromechanism; perceived stress; predictive modeling; prevent; risk prediction; risk prediction model; theories

Project Summary For the millions of individuals who experience migraine each year, treatment typically consists of reactively treating attacks only after experiencing disruptive pain and secondary symptoms. Because individual migraine attacks are unpredictable to most sufferers, abortive medications are not used early or effectively, and strategies to preemptively stop developing attacks cannot be formulated. By formalizing the daily risk for an attack, individuals will be better prepared to use existing abortive therapies and reduce the suffering associated with any single attack. Our team has previously built and tested the Headache Prediction-I (HAPRED-I) and Headache Prediction-II (HAPRED-II) models, which are simple migraine forecasting models that are based on daily stress. Despite their promise, these models exhibit several weaknesses that would prevent them from broad clinical use. The objective of this project is to evaluate a new forecasting model that has improved predictive power. To accomplish this, several important predictors have been added to the existing model, and the parameters of the new model will be continuously updated using Bayesian estimation. In the new HAPRED-III model (Aim 1), the forecasting window is reduced from 24 to 12 hours, temporal statistical predictors have been added, and additional predictors (e.g., sleep, mood, medication use, prodromal symptoms, and self-prediction) will be tested for improved performance. To allow the model to be more easily deployed (Aim 2), predictors of the model parameters will be examined. These predictors will better inform the prior probabilities of the model parameters and will reduce the need to collect weeks or months of data from each individual before generating reliable forecasts.

项目摘要 对于每年经历偏头痛的数百万个人，治疗通常包括 仅在经历破坏性疼痛和继发性症状后才反应治疗攻击。因为个体 偏头痛攻击对大多数患者来说是无法预测的，流产药物不早或有效地使用， 不能制定先发制人停止发展攻击的策略。通过形式化日常风险 攻击，个人将更好地准备使用现有的流产疗法并减少痛苦 与任何一次攻击有关。我们的团队以前已经建立并测试了头痛预测-I （HAPRED-I）和头痛预测-II（HAPRED-II）模型，它们是简单的偏头痛预测模型 基于每日压力。尽管有承诺，这些模型表现出了几个弱点 防止它们广泛使用。该项目的目的是评估一种新的预测模型 提高了预测能力。为此，已经添加了一些重要的预测指标 现有模型以及新模型的参数将使用贝叶斯估算不断更新。 在新的HAPRED-III型号（AIM 1）中，预测窗口从24个小时减少到12小时 已经添加了统计预测因子，并增加了其他预测因子（例如睡眠，情绪，用药，前驱 症状和自我预测）将进行测试以提高性能。使模型更容易 部署（AIM 2）将检查模型参数的预测因子。这些预测因素将更好地告知 模型参数的先前概率，并将减少收集数周或数月数据的需求 每个人都产生可靠的预测。