Chronorisk in cluster headache: A tool for individualised therapy?. (December 2018)

• Record Type: Journal Article
• Authors: Barloese, Mads; Haddock, Bryan; Lund, Nunu T; Petersen, Anja; Jensen, Rigmor
• Is Part Of: Cephalalgia. Volume 38:Number 14(2018)
• Page Start: 2058
• Page End: 2067
• Publication Date: 2018-12
• Subjects: Cluster headache -- chronobiology -- epidemiology -- autonomic nervous system -- sleep; Headache -- Periodicals; 616.8491
• DOI: 10.1177/0333102418769955
• Languages: English
• ISSNs: 0333-1024

Abstract

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 R 2 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.