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Machine phenotyping of cluster headache and its response to verapamil.

丛集性头痛的机器表型分型及其对维拉帕米的反应。

  • 影响因子:7.52
  • DOI:10.1093/brain/awaa388
  • 作者列表:"Tso AR","Brudfors M","Danno D","Grangeon L","Cheema S","Matharu M","Nachev P
  • 发表时间:2021-03-03
Abstract

:Cluster headache is characterized by recurrent, unilateral attacks of excruciating pain associated with ipsilateral cranial autonomic symptoms. Although a wide array of clinical, anatomical, physiological, and genetic data have informed multiple theories about the underlying pathophysiology, the lack of a comprehensive mechanistic understanding has inhibited, on the one hand, the development of new treatments and, on the other, the identification of features predictive of response to established ones. The first-line drug, verapamil, is found to be effective in only half of all patients, and after several weeks of dose escalation, rendering therapeutic selection both uncertain and slow. Here we use high-dimensional modelling of routinely acquired phenotypic and MRI data to quantify the predictability of verapamil responsiveness and to illuminate its neural dependants, across a cohort of 708 patients evaluated for cluster headache at the National Hospital for Neurology and Neurosurgery between 2007 and 2017. We derive a succinct latent representation of cluster headache from non-linear dimensionality reduction of structured clinical features, revealing novel phenotypic clusters. In a subset of patients, we show that individually predictive models based on gradient boosting machines can predict verapamil responsiveness from clinical (410 patients) and imaging (194 patients) features. Models combining clinical and imaging data establish the first benchmark for predicting verapamil responsiveness, with an area under the receiver operating characteristic curve of 0.689 on cross-validation (95% confidence interval: 0.651 to 0.710) and 0.621 on held-out data. In the imaged patients, voxel-based morphometry revealed a grey matter cluster in lobule VI of the cerebellum (-4, -66, -20) exhibiting enhanced grey matter concentrations in verapamil non-responders compared with responders (familywise error-corrected P = 0.008, 29 voxels). We propose a mechanism for the therapeutic effect of verapamil that draws on the neuroanatomy and neurochemistry of the identified region. Our results reveal previously unrecognized high-dimensional structure within the phenotypic landscape of cluster headache that enables prediction of treatment response with modest fidelity. An analogous approach applied to larger, globally representative datasets could facilitate data-driven redefinition of diagnostic criteria and stronger, more generalizable predictive models of treatment responsiveness.

摘要

: 丛集性头痛的特征是反复发作,与同侧颅内自主神经症状相关的剧烈疼痛的单侧发作。虽然大量的临床、解剖学、生理学和遗传学数据已经为关于潜在病理生理学的多种理论提供了信息,但缺乏全面的机制理解一方面阻碍了新治疗方法的发展,另一方面也阻碍了对已确定治疗方法的预测反应特征的识别。发现一线药物维拉帕米仅对所有患者的一半有效,并且在剂量递增数周后,使得治疗选择既不确定又缓慢。在这里,我们使用常规获得的表型和MRI数据的高维模型来量化维拉帕米反应性的可预测性,并阐明其神经依赖性,在2007年至708年间,在国家神经病学和神经外科医院评估了2017例丛集性头痛患者的队列。我们从结构化临床特征的非线性降维导出丛集性头痛的简洁潜在表征,揭示了新的表型集群。在患者的子集中,我们显示基于梯度升压机的单独预测模型可以从临床 (410例患者) 和成像 (194例患者) 特征预测维拉帕米反应性。结合临床和影像学数据的模型建立了预测维拉帕米反应性的第一个基准,交叉验证的受试者工作特征曲线下面积为0.689 (95% 置信区间: 0.651至0.710),保留数据为0.621。在成像的患者中,基于体素的形态测量显示小脑小叶VI中的灰质簇 (-4, -66, -20) 显示与响应者相比,维拉帕米无响应者的灰质浓度增强 (家庭误差校正p   =   0.008,29体素)。我们提出了维拉帕米治疗效果的机制,该机制借鉴了已识别区域的神经解剖学和神经化学。我们的结果揭示了丛集性头痛表型景观中先前未被识别的高维结构,能够以适度的保真度预测治疗反应。应用于更大的、具有全球代表性的数据集的类似方法可以促进诊断标准的数据驱动的重新定义和治疗反应性的更强、更通用的预测模型。

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影响因子:2.68
发表时间:2021-02-01
DOI:10.1080/14656566.2020.1814255
作者列表:["Sawada H","Oeda T","Kohsaka M","Tomita S","Umemura A","Park K","Yamamoto K","Kiyohara K"]

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影响因子:2.06
发表时间:2021-03-24
DOI:10.1007/s11033-021-06299-9
作者列表:["Louvrier A","Terranova L","Meyer C","Meyer F","Euvrard E","Kroemer M","Rolin G"]

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