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人来说，治疗通常包括 只有在经历破坏性疼痛和继发性症状后才能反应性地治疗发作。因为个人 偏头痛的发作对大多数患者来说是不可预测的，流产的药物没有及早或有效地使用， 而且无法制定先发制人地阻止攻击发展的战略。通过将日常风险正规化 在一次攻击中，个体将更好地准备使用现有的流产疗法，并减少痛苦 与任何一次袭击有关。我们的团队之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，这是简单的偏头痛预测模型 这些都是基于日常压力。尽管这些模型前景看好，但它们表现出了几个弱点 阻止它们在临床上的广泛应用。该项目的目标是评估一种新的预测模型，该模型 提高了预测能力。为了实现这一点，已经将几个重要的预测因素添加到 现有模型，新模型的参数将使用贝叶斯估计不断更新。 在新的HAPRED-III模型(目标1)中，预报窗口从24小时减少到12小时，时间 添加了统计预测因子，以及其他预测因子(例如，睡眠、情绪、用药情况、前驱症状 症状和自我预测)将进行测试，以提高性能。为了让模型更容易 部署(目标2)，将检查模型参数的预测值。这些预测者将更好地向 模型参数的先验概率，将减少从以下位置收集数周或数月数据的需要 在产生可靠的预测之前，每一个人。