Chronorisk in cluster headache: A tool for individualised therapy?

TY - JOUR

T1 - Chronorisk in cluster headache

T2 - A tool for individualised therapy?

AU - Barloese, Mads

AU - Haddock, Bryan

AU - Lund, Nunu T

AU - Petersen, Anja

AU - Jensen, Rigmor

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

U2 - 10.1177/0333102418769955

DO - 10.1177/0333102418769955

M3 - Journal article

C2 - 29635936

SN - 0333-1024

VL - 38

SP - 2058

EP - 2067

JO - Cephalalgia : an international journal of headache

JF - Cephalalgia : an international journal of headache

IS - 14

ER -