Forecasting Migraine Attacks

预测偏头痛发作

• 批准号: 10366149
• 负责人: TIMOTHY T HOULE
• 金额: $ 42.28万
• 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/10366149
• 关键词: American; Bayesian Forecast; Bayesian Method; Clinical; Data; Discrimination; Disease; Equilibrium; Estimation Techniques; Exhibits; Frequencies; Future; Goals; Headache; Hour; Individual; Intercept; Laboratories; Lead; Learning; Literature; Machine Learning; Measurement; Migraine; Modeling; Moods; Online Systems; Pain; Participant; Patient Self-Report; Performance; Persons; Pharmaceutical Preparations; Population Heterogeneity; Probability; Procedures; Rain; Research Personnel; Resolution; Risk; Sleep; 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; 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.

项目总结