Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

Critical Care : the Official Journal of the Critical Care Forum
Mitchell J CohenGeoffrey T Manley

Abstract

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identifie...Continue Reading

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Citations

Sep 24, 2011·Journal of Neurotrauma·P David AdelsonUNKNOWN Pediatric TBI Demographics and Clinical Assessment Working Group
Jul 17, 2013·BMC Medical Genomics·Adam D GrossmanAtul J Butte
Mar 20, 2010·Critical Care : the Official Journal of the Critical Care Forum·Timothy G Buchman
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Oct 13, 2012·Journal of the American Medical Informatics Association : JAMIA·David M MasloveHenry J Lowe
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Jul 30, 2011·Journal of Critical Care·Rami NamasYoram Vodovotz
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