Research
Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis
- † Equal contributors
1 Department of Surgery, University of California, 505 Parnassus Avenue, San Francisco, CA 94143, USA
2 Department of Bioengineering, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
3 Department of Neurosurgery, University of California, 505 Parnassus Avenue, San Francisco, CA 94143, USA
4 Departments of Medicine and Pediatrics (Medical Informatics), Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
Critical Care 2010, 14:R10 doi:10.1186/cc8864
See related commentary by Buchman, http://ccforum.com/content/14/2/217
Published: 2 February 2010Abstract
Introduction
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.
Methods
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.
Results
We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters.
Conclusions
Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.
