Continuous-Signal Driven Predictive Models in Neurological Intensive Care Units

描述（由申请人提供）：在传统的患者管理方案下，大多数医疗干预措施主要是为了应对临床症状或异常的生理值。相反，如果有可靠的、可操作的关键临床事件预测支持，预防性或前瞻性方案可以显著改善结果并降低不断上升的医疗成本。然而，在大多数临床环境中，卫生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医学、生理和生物数据的预测性数据挖掘，为个体患者的关键参数和事件提供可靠的临床预测。作为神经内科重症监护环境中典型的临床预测应用，本项目的主要目的是在一种新的分类器融合框架下，基于集成，证明早期识别两种常见的颅内继发性损伤的有效性，包括急性脑室肿大（脑室增大）和急性颅内压升高（ICP）。我们小组最近在处理连续ICP信号时发现了机器学习算法和新的定量指标。

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.