tailieunhanh - Báo cáo y học: "Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis. | Cohen et al. Critical Care 2010 14 R10 http content 14 1 R10 c CRITICAL CARE RESEARCH Open Access Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis Mitchell J Cohen 41 Adam D Grossman42 Diane Morabito3 M Margaret Knudson 1 Atul J Butte4 and Geoffrey T Manley3 Abstract 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 .