Flexible modeling of headache frequency fluctuations in migraine with hidden Markov models.

Objective: To explore hidden Markov models (HMMs) as an approach for defining clinically meaningful headache-frequency-based groups in migraine.

Background: Monthly headache frequency in patients with migraine is known to vary over time. This variation has not been completely characterized and is not well accounted for in the classification of individuals as having chronic or episodic migraine, a diagnosis with potentially significant impacts on the individual. This study investigated variation in reported headache frequency in a migraine population and proposed a model for classifying individuals by frequency while accounting for natural variation.

Methods: The American Registry for Migraine Research (ARMR) was a longitudinal multisite study of United States adults with migraine. Study participants completed quarterly questionnaires and daily headache diaries. A series of HMMs were fit to monthly headache frequency data calculated from the diary data of ARMR.

Results: Changes in monthly headache frequency tended to be small, with 47% of transitions resulting in a change of 0 or 1 day. A substantial portion (24%) of months reflected daily headache with individuals ever reporting daily headache likely to consistently report daily headache. An HMM with four states with mean monthly headache frequency emissions of 3.52 (95% Prediction Interval [PI] 0-8), 10.10 (95% PI 4-17), 20.29 (95% PI 12-28), and constant 28 days/month had the best fit of the models tested. Of sequential month-to-month headache frequency transitions, 12% were across the 15-headache days chronic migraine cutoff. Under the HMM, 38.7% of those transitions involved a change in the HMM state, and the remaining 61.3% of the time, a change in chronic migraine classification was not accompanied by a change in the HMM state.

Conclusion: A divide between the second and third states of this model aligns most strongly with the current episodic/chronic distinction, although there is a meaningful overlap between the states that supports the need for flexibility. An HMM has appealing properties for classifying individuals according to their headache frequency while accounting for natural variation in frequency. This empirically derived model may provide an informative classification approach that is more stable than the use of a single cutoff value.