1 Department of Clinical Physiology and Nuclear Medicine, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.
2 Danish Headache Center, Department of Neurology, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark.
Cephalalgia. 2018 Dec;38(14):2058-2067. doi: 10.1177/0333102418769955. Epub 2018 Apr 10.
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.
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.
The Gaussian model fit for the chronorisk distribution for all patients reporting diurnal rhythmicity (n = 286) had a goodness of fit R 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.
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.
丛集性头痛剧烈疼痛的机制仍然很神秘。发作的一个显著特征是其发生的惊人节律。我们研究了统计建模是否可用于描述 24 小时发作分布，并识别亚组之间的差异。
收集了 351 例丛集性头痛患者的常见发作时间。计算了全天发作的概率分布（chronorisk）。使用多峰高斯拟合分析这些 24 小时分布，以确定高发作风险期，并进行频谱分析以识别风险波动。
对于所有报告昼夜节律性（n=286）的患者，高斯模型对 chronorisk 分布的拟合具有 0.97 的拟合优度 R 值，并确定了三个风险增加高峰时间，分别为 21:41、02:02 和 06:23 小时。在亚组中，发现了三到五个风险增加模式，拟合优度值范围为 0.85-0.99。频谱分析显示，chronorisk 在亚组中存在多种不同的振荡频率，包括发作性患者的主导昼夜节律性和慢性患者的超昼夜节律性。
丛集性头痛的 chronorisk 可以被描述为个体、定时发作的增加风险的总和，每个发作都有一个高斯分布。在发作性丛集性头痛中，发作遵循昼夜节律性，而在慢性丛集性头痛中，超昼夜节律性占主导地位，这反映了与睡眠的关联丧失，也许可以解释观察到的特定治疗效果的差异。这些结果证明了准确建模原发性头痛中生物钟模式的能力。
