Background The mechanisms behind the severe pain of cluster headache remain enigmatic. A distinguishing feature of the attacks is the striking rhythms with which they occur. We investigated whether statistical modelling can be used to describe 24-hour attack distributions and identify differences between subgroups. Methods Common hours of attacks for 351 cluster headache patients were collected. Probability distributions of attacks throughout the day (chronorisk) was calculated. These 24-hour distributions were analysed with a multimodal Gaussian fit identifying periods of elevated attack risk and a spectral analysis identifying oscillations in risk. Results The Gaussian model fit for the chronorisk distribution for all patients reporting diurnal rhythmicity (n = 286) had a goodness of fit R2 value of 0.97 and identified three times of increased risk peaking at 21:41, 02:02 and 06:23 hours. In subgroups, three to five modes of increased risk were found and goodness of fit values ranged from 0.85-0.99. Spectral analysis revealed multiple distinct oscillation frequencies in chronorisk in subgroups including a dominant circadian oscillation in episodic patients and an ultradian in chronic. Conclusions Chronorisk in cluster headache can be characterised as a sum of individual, timed events of increased risk, each having a Gaussian distribution. In episodic cluster headache, attacks follow a circadian rhythmicity whereas, in the chronic variant, ultradian oscillations are dominant reflecting a loss of association with sleep and perhaps explaining observed differences in the effects of specific treatments. The results demonstrate the ability to accurately model chronobiological patterns in a primary headache.
|Tidsskrift||Cephalalgia : an international journal of headache|
|Status||Udgivet - 2018|