Dumkrieger Gina M, Ishii Ryotaro, Goadsby Peter J
Mayo Clinic, Phoenix, Arizona, USA.
Kyoto Prefectural University of Medicine, Kyoto, Japan.
Headache. 2025 Jan;65(1):132-142. doi: 10.1111/head.14782. Epub 2024 Jul 30.
To explore hidden Markov models (HMMs) as an approach for defining clinically meaningful headache-frequency-based groups in migraine.
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.
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.
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.
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.
探讨隐马尔可夫模型（HMMs）作为一种在偏头痛中基于头痛频率定义具有临床意义分组的方法。
已知偏头痛患者的每月头痛频率会随时间变化。这种变化尚未得到充分描述，并且在将个体分类为慢性或发作性偏头痛时也未得到很好的考虑，而这一诊断可能对个体产生重大影响。本研究调查了偏头痛人群中报告的头痛频率变化，并提出了一种在考虑自然变化的同时按频率对个体进行分类的模型。
美国偏头痛研究注册库（ARMR）是一项针对美国成年偏头痛患者的纵向多中心研究。研究参与者完成季度问卷和每日头痛日记。一系列隐马尔可夫模型被应用于根据ARMR日记数据计算出的每月头痛频率数据。
每月头痛频率的变化往往较小，47%的转变导致变化0或1天。相当一部分月份（24%）反映为每日头痛，曾报告每日头痛的个体可能会持续报告每日头痛。一个具有四个状态的隐马尔可夫模型，其每月头痛频率平均排放分别为3.52（95%预测区间[PI]0 - 8）、10.10（95%PI 4 - 17）、20.29（95%PI 12 - 28）以及恒定的每月28天，在所测试的模型中拟合最佳。在逐月的头痛频率转变中，12%跨越了慢性偏头痛15天头痛日的临界值。在隐马尔可夫模型下，这些转变中有38.7%涉及隐马尔可夫状态的变化，其余61.3%的时间里，慢性偏头痛分类的变化并未伴随着隐马尔可夫状态的变化。
该模型第二和第三状态之间的划分与当前发作性/慢性的区分最为紧密一致，尽管各状态之间存在有意义的重叠，这支持了灵活性的需求。隐马尔可夫模型在考虑频率自然变化的同时，根据头痛频率对个体进行分类具有吸引人的特性。这个基于经验得出的模型可能提供一种比使用单一临界值更稳定且信息丰富的分类方法。
