Altering physiological networks using drugs: steps towards personalized physiology.
Altering physiological networks using drugs: steps towards personalized physiology.
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DOI：
10.1186/1755-8794-6-s2-s7
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发表时间：
2013
影响因子：
2.7
通讯作者：
Butte AJ
中科院分区：
文献类型：
--
作者：
Grossman AD;Cohen MJ;Manley GT;Butte AJ
个性化医疗的兴起提醒我们，每个病人都必须作为一个个体来对待。做出治疗决策的一个因素是每个患者的生理状态，但相关状态的定义和可视化状态相关生理变化的方法很少。我们从生理数据中构建了相关网络，以证明与重症监护室中升压药使用相关的变化。我们收集了29个生理变量在一分钟的时间间隔从19个创伤患者在重症监护病房的学术医院和分组每分钟的数据作为接收或不接收升压。对于每一组，我们构建了生理变量对的斯皮尔曼相关网络。为了可视化药物相关的变化，我们将网络分为三个部分：不变的网络，具有变化的相关性符号的连接网络，以及仅存在于一组中的连接网络。在29个生理测量之间的可能的406个连接中，在三个分量网络中的每一个中存在64、39和48个连接。静态网络证实了预期的生理关系，而具有改变的相关性符号的关联网络表明了由于药物引起的假定变化。仅与升压素相关的网络提示了可能值得研究的新关系。我们证明了使用相关网络可视化生理关系提供了对潜在生理状态的洞察，同时也表明当药物的存在定义状态时，许多这些关系会发生变化。这种方法应用于有针对性的实验可以改变重症监护患者的监测和治疗方式。
The rise of personalized medicine has reminded us that each patient must be treated as an individual. One factor in making treatment decisions is the physiological state of each patient, but definitions of relevant states and methods to visualize state-related physiologic changes are scarce. We constructed correlation networks from physiologic data to demonstrate changes associated with pressor use in the intensive care unit. We collected 29 physiological variables at one-minute intervals from nineteen trauma patients in the intensive care unit of an academic hospital and grouped each minute of data as receiving or not receiving pressors. For each group we constructed Spearman correlation networks of pairs of physiologic variables. To visualize drug-associated changes we split the networks into three components: an unchanging network, a network of connections with changing correlation sign, and a network of connections only present in one group. Out of a possible 406 connections between the 29 physiological measures, 64, 39, and 48 were present in each of the three component networks. The static network confirms expected physiological relationships while the network of associations with changed correlation sign suggests putative changes due to the drugs. The network of associations present only with pressors suggests new relationships that could be worthy of study. We demonstrated that visualizing physiological relationships using correlation networks provides insight into underlying physiologic states while also showing that many of these relationships change when the state is defined by the presence of drugs. This method applied to targeted experiments could change the way critical care patients are monitored and treated.