Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions.
Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions.
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DOI：
10.1038/s41598-021-87304-w
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发表时间：
2021-04-08
影响因子：
4.6
通讯作者：
Lascelles BDX
中科院分区：
文献类型：
--
作者：
Xu Z;Laber E;Staicu AM;Lascelles BDX
骨关节炎 (OA) 是一种通常与疼痛相关的慢性疾病，影响着大约 14% 的人口，并且患病率不断增加。全球人口老龄化使得治疗骨关节炎相关疼痛以及保持活动能力成为公共卫生优先事项。骨关节炎影响所有哺乳动物，使用自发动物模型是改善转化疼痛研究和开发有效治疗策略的一种有前景的方法。加速度计是收集动物高频活动数据的常用工具，用于研究治疗对疼痛相关活动模式的影响。最近人们对使用它们来了解人类疼痛状况的治疗效果越来越感兴趣。然而，不同科目的活动模式差异很大；此外，治疗的效果可能会表现为活动次数的增加或减少，或者以更微妙的方式表现出来，例如某些类型活动的频率变化。我们使用零膨胀泊松隐半马尔可夫模型来表征活动模式，并随后根据活动水平或活动类型频率的变化得出治疗效果的估计量。我们使用来自自然发生的猫科动物 OA 相关疼痛模型的数据，展示了我们的模型的应用及其相对于传统分析方法的进步。
Osteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.
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影响因子：
1.8
作者：
HANUSHEK, EA
通讯作者：
HANUSHEK, EA
DOI：
10.1098/rsif.2017.0885
发表时间：
2018-03
期刊：
Journal of the Royal Society, Interface
影响因子：
--
作者：
Huang Q;Cohen D;Komarzynski S;Li XM;Innominato P;Lévi F;Finkenstädt B
通讯作者：
Finkenstädt B
影响因子：
4.5
作者：
Douc, R;Moulines, T;Rydén, T
通讯作者：
Rydén, T
影响因子：
1.7
作者：
Lascelles, B. Duncan X.;Hansen, Bernard D.;Smith, Eric S.
通讯作者：
Smith, Eric S.
影响因子：
2
作者：
DeSantis, StaciaM.;Bandyopadhyay, Dipankar
通讯作者：
Bandyopadhyay, Dipankar