Forecasting Individual Headache Attacks Using Perceived Stress: Development of a Multivariable Prediction Model for Persons With Episodic Migraine

Abstract

Objective: To develop and validate a prediction model that forecasts future migraine attacks for an individual headache sufferer.

Background: Many headache patients and physicians believe that precipitants of headache can be identified and avoided or managed to reduce the frequency of headache attacks. Of the numerous candidate triggers, perceived stress has received considerable attention for its association with the onset of headache in episodic and chronic headache sufferers. However, no evidence is available to support forecasting headache attacks within individuals using any of the candidate headache triggers.

Methods: This longitudinal cohort with forecasting model development study enrolled 100 participants with episodic migraine with or without aura, and N = 95 contributed 4626 days of electronic diary data and were included in the analysis. Individual headache forecasts were derived from current headache state and current levels of stress using several aspects of the Daily Stress Inventory, a measure of daily hassles that is completed at the end of each day. The primary outcome measure was the presence/absence of any headache attack (head pain > 0 on a numerical rating scale of 0-10) over the next 24 h period.

Results: After removing missing data (n = 431 days), participants in the study experienced a headache attack on 1613/4195 (38.5%) days. A generalized linear mixed-effects forecast model using either the frequency of stressful events or the perceived intensity of these events fit the data well. This simple forecasting model possessed promising predictive utility with an AUC of 0.73 (95% CI 0.71-0.75) in the training sample and an AUC of 0.65 (95% CI 0.6-0.67) in a leave-one-out validation sample. This forecasting model had a Brier score of 0.202 and possessed good calibration between forecasted probabilities and observed frequencies but had only low levels of resolution (ie, sharpness).

Conclusions: This study demonstrates that future headache attacks can be forecasted for a diverse group of individuals over time. Future work will enhance prediction through improvements in the assessment of stress as well as the development of other candidate domains to use in the models.

Keywords: forecasting; migraine; preemptory; stress; trigger.

Figure 1

The utility of the forecasting model to discriminate between future headache and non-headache attacks is displayed using ROC curves. The three stress models based on three different subscales of the Daily Stress Inventory are depicted using dark lines. The internal validation of each of these models is displayed using dotted lines. Each of the models forecasted headaches with similar utility, and exhibited similar internal validation performance.

Figure 2

The calibration and resolution of the selected AIR model is displayed using a calibration plot (large plot) and the histogram (small plot). The similarity of the forecasted (x-axis) versus actual probabilities (y-axis) indicates a well-calibrated model, while the high frequency of forecasts near the base rate of the sample indicates that the model does not have a high degree of resolution.