Cluster analysis of clinical data measured in the surgical intensive care unit.
A set of 13 extensively used hemodynamic, ventilatory and gas analysis variables are measured (on-line or off-line) on 200 patients in an intensive care unit (ICU) during the 6 h immediately following cardiac surgery. In order to identify both low- and high-risk patterns, a clustering method is applied to these data at three equidistant observation times. Application of the divergence criterion allows a quantitative evaluation of the diversity between the clusters identified, showing that the two patterns are really distinct in the 13-D space. The same criterion is then used to find possible subsets of variables capable of maintaining, in time, an effective separation power. The latter always include the cardiac index (CI), representative of cardiac performance, and two indices related to respiratory efficiency and metabolic rate, i.e., the carbon dioxide production index (VCO2I) and the arterio-venous oxygen difference (avO2D).
Dipartimento di Elettronica, Informatica e Sistemistica, Università di Bologna, Italy.
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Avanzolini
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Barbini
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Gnudi
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Grossi
D006348:Cardiac Surgical Procedures; D002315:Cardiopulmonary Bypass; D016000:Cluster Analysis; D003422:Critical Care / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D007558:Italy; D008432:Mathematical Computing; D008875:Middle Aged; D008991:Monitoring, Physiologic; D012720:Severity of Illness Index