Sorani Marco D, Hemphill J Claude, Morabito Diane, Rosenthal Guy, Manley Geoffrey T
Program in Biological & Medical Informatics, University of California, San Francisco, USA.
Neurocrit Care. 2007;7(1):45-52. doi: 10.1007/s12028-007-0043-7.
A fundamental purpose of neurocritical care is the management of secondary brain injury. This is often accomplished by monitoring and managing individual patient parameters including physiological vital signs. Yet, the ability to record physiological data exceeds our ability to fully integrate it into patient care. We propose that advances in monitoring must be accompanied by advances in methods of high-frequency, multivariate data analysis that integrate the multiple processes occurring in critically ill patients.
We describe initial work in the emerging field of physiological informatics in critical care medicine. We analyzed data on 23 patients with brain injury from our Neurotrauma and Critical Care Database, which contains more than 20 physiological parameters recorded automatically at one-minute intervals via bedside monitors connected to standard personal computers. We performed exploratory data analysis, studied two patient cases in detail, and implemented a data-driven classification approach using hierarchical clustering.
In this study, we present challenges and opportunities for high-frequency multimodal monitoring to quantitatively detect secondary brain insults, and develop clustering methodology to construct multivariate physiological data "profiles" to classify patients for diagnosis and treatment.
Recording of many physiological variables across multiple patients is feasible and can lead to new clinical insights. Computational and analytical methods previously used primarily for basic science may have clinical relevance and can potentially be adapted to provide physicians with improved ability to integrate complex information for decision making in neurocritical care.
神经重症监护的一个基本目的是处理继发性脑损伤。这通常通过监测和管理包括生理生命体征在内的个体患者参数来实现。然而,记录生理数据的能力超过了我们将其完全整合到患者护理中的能力。我们提出,监测方面的进展必须伴随着高频多变量数据分析方法的进展,这些方法要整合危重症患者中发生的多个过程。
我们描述了重症监护医学中新兴的生理信息学领域的初步工作。我们分析了来自我们的神经创伤与重症监护数据库中23例脑损伤患者的数据,该数据库包含通过连接到标准个人计算机的床边监测仪以一分钟间隔自动记录的20多个生理参数。我们进行了探索性数据分析,详细研究了两个患者病例,并使用层次聚类实施了数据驱动的分类方法。
在本研究中,我们提出了高频多模态监测在定量检测继发性脑损伤方面的挑战和机遇,并开发了聚类方法来构建多变量生理数据“档案”,以对患者进行分类诊断和治疗。
记录多个患者的许多生理变量是可行的,并且可以带来新的临床见解。以前主要用于基础科学的计算和分析方法可能具有临床相关性,并且有可能进行调整,以提高医生整合复杂信息以进行神经重症监护决策的能力。