Forecasting Migraine Attacks and the Utility of Identifying Triggers

Abstract

A substantial pool of candidate trigger factors could be considered in the creation of forecasting models. However, because mechanistic information about causal factors that precede a migraine attack is not well understood, and such factors are difficult to measure, empirical models that are based on trigger factors that are merely associated with the onset of headache activity are likely to be the focus of forecasting efforts in the near future. Of such factors, stress has considerable empirical support and has been used to successfully forecast future headache attacks within individuals over time. However, at present, existing models possess only modest levels of discrimination and lack strong resolution in generated predictions. Current headache forecasting models represent an important first step in accurately predicting future headache activity. However, to utilize these models in a preemptive treatment paradigm where the risk of headache is treated prior to the actual experience of pain, these models must achieve greater precision with good calibration and generate predictions that are clinically actionable by individuals in their real-time home environments.

Patterns of Perceived Stress Throughout the Migraine Cycle: A Longitudinal Cohort Study Using Daily Prospective Diary Data

“…Despite this, there is still an incomplete understanding of both the clinical manifestations of migraine and the underlying disease biology. The relationship of migraine to psychosocial stress has long been explored 11‐18 . Psychosocial stress is typically measured in 1 of the 2 ways: (1) as the experience of perceived stress, which is a subjective experience that is thought to occur when perceived demands exceed perceived resources; or (2) as the experience of stressors, which are often categorized as major life events or minor life events (also referred to as daily hassles) 14 .…”

“…Psychosocial stress is typically measured in 1 of the 2 ways: (1) as the experience of perceived stress, which is a subjective experience that is thought to occur when perceived demands exceed perceived resources; or (2) as the experience of stressors, which are often categorized as major life events or minor life events (also referred to as daily hassles) 14 . Perceived stress, either as a static or dynamic variable, is thought to trigger attacks and has been a target of validated biobehavioral interventions for decades 11‐18 . Recent advances in the ability to collect and analyze high‐resolution, longitudinal, individually reported patient data 19 have allowed for greater insight into the relationship between perceived stress ratings (both static and dynamic) and migraine attacks.…”

Predicting the Future of Migraine Attack Prediction

“…As a result, formal systems of forecasting have developed in diverse scientific fields. 5 Forecasting models rely on predictors identified based on hypothesized mechanisms (eg, inflammatory markers predict Alzheimer's disease) or empirical observation (eg, menses triggers migraine). 4,6 Empirical observation may lead to mechanistic hypotheses that can subsequently be tested.…”

Development and Internal Validation of a Multivariable Prediction Model for Individual Episodic Migraine Attacks Based on Daily Trigger Exposures

“…8,9 Though trigger factors have been formally studied for at least 60 years, 3 and certain transient exposures (eg, psychological stress, changes in sleep, and menstrual cycles) are commonly thought to bring about migraine attacks, as of yet there is limited work toward assessing their predictive utility. 1,[10][11][12] Predictive modeling of migraines is promising not only for the longer term goal of applying real-time migraine prediction for individual patients, but also in the near term to set a baseline for predictive accuracy and to understand the relevance of different factors to this end. 13 The current best effort toward migraine prediction based on self-reported data, by Houle et al, used a model based on stressful event frequency and current headache status, and estimated an out-of-sample concordance statistic (area under the receiver-operating characteristic curve) of 0.65.…”

“…11 This work laid important groundwork for future migraine prediction studies and sparked several commentaries or re-analyses describing the importance of and room for improvement in headache forecasting. 1,12,14 It also brought to light key areas for methodological improvement in predicting migraine days. For one, as the authors acknowledged, it would be intuitively useful to focus on prediction of new attacks during non-headache times (rather than predicting next-day headache regardless of current headache status).…”