Continuous-Signal Driven Predictive Models in Neurological Intensive Care Units

描述（由申请人提供）：大多数医疗干预措施主要用于应对常规患者管理方案下的临床症状或异常生理值。相比之下，预防性或主动性协议可以大大改善结果，并降低不断上升的医疗保健成本，如果支持的关键临床事件的可靠和可行的预测。然而，在大多数临床环境中，医疗保健专业人员最多只能访问到目前为止的数据。我们研究的长期目标是通过对异质性医学、生理和生物数据的预测数据挖掘，提供个体患者关键参数和事件的可靠临床预测。作为神经重症监护环境中的典型临床预测应用，该项目的主要目的是证明早期识别两种常见颅内继发性损伤（包括急性脑室扩大）的有效性（脑室扩大）和颅内压（ICP）急性升高的基础上整合，在一种新的分类器融合框架下，机器学习算法和我们小组最近在处理连续ICP信号中发现的新的定量指标。 预测脑室扩大和ICP升高的需要在神经重症监护室（NICU）中特别相关，在NICU中，使用一系列连续监测的信号来支持对处于急性期的复杂和严重神经障碍的患者的管理。这些重症患者容易受到多种形式的延迟但可治疗的继发性损伤。因此，在出现临床症状之前早期检测二次损伤与支持采用积极的患者管理直接相关，其有效性可在后续随机临床试验中得到证明。 我们提出了两个目标，首先建立一个通用框架，支持将连续的生理信号纳入一个预测模型，包括一组分类器。这些分类器相对于感兴趣的时间以不同的时间间隔隔开，并且它们的结果被融合以提供改进的预测。第二个目标包括两个子目标，以建立两个原型预测有用的神经重症监护环境。第一个预测涉及脑室扩大的早期检测，第二个预测涉及急性ICP升高的预测，这两种都是创伤性和出血性脑损伤后继发性损伤的常见形式。 如果成功的话，ICP升高和脑室扩大的预测可以导致从传统的反应性患者管理到更积极主动的管理方案的潜在范式转变，并改善患者的预后，提高神经重症监护的效率。 公共卫生相关性：该项目的目标是首先开发一个通用框架，用于支持将连续生理信号纳入可用于提供临床预测的预测模型。然后，我们将研究两个典型的预测，是非常有用的管理脑损伤患者在神经重症监护病房的性能。第一个预测涉及脑室扩大的早期检测，第二个预测涉及急性ICP升高的预测，这两种都是创伤性和出血性脑损伤后继发性损伤的常见形式。因此，他们的成功预测，可以导致一个范式的转变，从传统的反应性病人管理，以更积极的管理协议，改善病人的结果，提高效率，在神经重症监护。

DESCRIPTION (provided by applicant): Most medical interventions are primarily prescribed to respond to clinical symptoms or abnormal physiological values under conventional patient management protocols. In contrast, prophylactic or proactive protocols can substantially improve outcomes and decrease escalating costs of healthcare if supported by reliable and actionable forecasts of critical clinical events. However, in most clinical environments, health care professionals are only able to access data that are, at best, up to the present time. Long-term goal of our research is to provide reliable clinical forecasts of individual patient's critical parameters and events by predictive data mining of heterogeneous medical, physiological, and biological data. As a prototypical clinical forecast application in a neurological intensive care environment, the main objective of the proposed project is to demonstrate the efficacy of earlier recognition of two common intracranial secondary insults including acute ventriculomegaly (enlargement of the brain ventricles) and acute elevation of intracranial pressure (ICP) based on integrating, under a novel classifier fusion framework, machine learning algorithms and novel quantitative metrics that were recently discovered by our group in processing continuous ICP signals. The need for forecasting ventriculomegaly and elevated ICP is particularly relevant in a neurological intensive care unit (NICU) where an array of continuously monitored signals are used to support management of patients of complex and severe neurological disorders at their acute phase. These critically ill patients are susceptible to many forms of delayed but treatable secondary injuries. Therefore, an early detection of developing secondary insults prior to clinical symptoms is directly relevant to support the adoption of proactive patient management whose efficacy can be demonstrated in a follow-up randomized clinical trial. We propose two aims to first build a general framework supporting incorporation of continuous physiological signals into a predictive model that comprise of a set of classifiers. These classifiers are spaced at different time intervals relative to the time of interest and their results are fused to provide an improved forecast. The second aim includes two sub aims to build two protypical forecasts useful in a neurocritical care environment. The first forecast concerns early detection of ventriculomegaly and the second forecast concerns prediction of acute ICP elevation, both of which are common forms of secondary insult after traumatic and hemorrhagic brain injury. If successful, the forecast of ICP elevation and ventriculomegaly can lead to a potential paradigam shift from conventional reactive patient management to a more proactive management protocol and improve patient outcome and enhance the efficiency in neurocritical care. PUBLIC HEALTH RELEVANCE: The objectives of this project are to first develop a generic framework for supporting incorporation of continuous physiological signals into a predictive model that can be used for providing clinical forecasts. Then we will investigate the performance of two protypical forecasts that are very useful in managing brain injury patients in a neurocritical care unit. The first forecast concerns early detection of ventriculomegaly and the second forecast concerns prediction of acute ICP elevation, both of which are common forms of secondary insult after traumatic and hemorrhagic brain injury. Therefore, their successful forecast can lead to a paradigam shift from conventional reactive patient management to a more proactive management protocol and improve patient outcome and enhance the efficiency in neurocritical care.