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 the incorporation 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 i ncludes 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 ehance the efficiency in a neurocritical care.
The objectives of this project are to first develop a generic framework for supporting incorparation of continous physiological signals into a predictive model that can be used for providing clinical forecasts. Then we will investigate the performance of two protypical forecaststs 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 ehance the efficiency in a neurocritical care.
|Connolly, Mark; He, Xing; Gonzalez, Nestor et al. (2014) Reproduction of consistent pulse-waveform changes using a computational model of the cerebral circulatory system. Med Eng Phys 36:354-63|
|Asgari, Shadnaz; Vespa, Paul; Hu, Xiao (2013) Is there any association between cerebral vasoconstriction/vasodilatation and microdialysis Lactate to Pyruvate ratio increase? Neurocrit Care 19:56-64|
|Kim, Sunghan; Hamilton, Robert; Pineles, Stacy et al. (2013) Noninvasive intracranial hypertension detection utilizing semisupervised learning. IEEE Trans Biomed Eng 60:1126-33|
|Scalzo, Fabien; Alger, Jeffry R; Hu, Xiao et al. (2013) Multi-center prediction of hemorrhagic transformation in acute ischemic stroke using permeability imaging features. Magn Reson Imaging 31:961-9|
|Scalzo, Fabien; Liebeskind, David; Hu, Xiao (2013) Reducing false intracranial pressure alarms using morphological waveform features. IEEE Trans Biomed Eng 60:235-9|
|Kasprowicz, Magdalena; Bergsneider, Marvin; Czosnyka, Marek et al. (2012) Association between ICP pulse waveform morphology and ICP B waves. Acta Neurochir Suppl 114:29-34|
|Asgari, Shadnaz; Gonzalez, Nestor; Subudhi, Andrew W et al. (2012) Continuous detection of cerebral vasodilatation and vasoconstriction using intracranial pulse morphological template matching. PLoS One 7:e50795|
|Kim, Sunghan; Scalzo, Fabien; Bergsneider, Marvin et al. (2012) Noninvasive intracranial pressure assessment based on a data-mining approach using a nonlinear mapping function. IEEE Trans Biomed Eng 59:619-26|
|Hamilton, Robert B; Baldwin, Kevin; Vespa, Paul et al. (2012) Subpeak regional analysis of intracranial pressure waveform morphology based on cerebrospinal fluid hydrodynamics in the cerebral aqueduct and prepontine cistern. Conf Proc IEEE Eng Med Biol Soc 2012:3935-8|
|Hu, Xiao; Hamilton, Robert; Baldwin, Kevin et al. (2012) Automated extraction of decision rules for predicting lumbar drain outcome by analyzing overnight intracranial pressure. Acta Neurochir Suppl 114:207-12|
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