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Principal Investigator: TIMOTHY T HOULE
Organization: MASSACHUSETTS GENERAL HOSPITAL
Fiscal Year: 2024
Award: $406,662
Funding agency: National Institute of Neurological Disorders and Stroke
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.
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