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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS066008-04
Application #
8256763
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Hicks, Ramona R
Project Start
2009-05-15
Project End
2013-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
4
Fiscal Year
2012
Total Cost
$330,138
Indirect Cost
$115,763
Name
University of California Los Angeles
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
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