Patterns in clinical chemistry requests.

For each patient sample that is presented to the clinical chemistry laboratory a combination of various tests can be requested. This combination or profile will depend on the condition of the patient, and hence also on the requesting hospital department. Several techniques were applied to detect and describe patterns in tests requested by the cardiology, hepatology and nephrology sections of the out-patient's Department for Internal Medicine. Comparison of the frequencies of ordering the tests showed significant differences between these sections. Cluster analysis and multidimensional scaling were used to show similarities and differences in the test profiles that were used by the sections. These techniques are useful for generating hypotheses, but the statistical significance of the clustering found is difficult to assess.

Exploring multivariate clinical chemical routine data concerning three major disease groups.

In preparation for multivariate analysis, an exploratory study has been undertaken to investigate the relative position, separability, homogeneity and shape of three major disease groups, using data from a clinical chemical routine package.The data set consists of 46 hepatology patients, 50 nephrology patients and 46 cardiology patients, and the measured blood levels include 20 common clinical chemical routine assays. Missing value problems were avoided by deleting some of the variables and objects.A univariate analysis was used as the basis ofa rescaling of the data.Bivariate (pairwise) plots of some major assays each show limited separation. The set of three such plots of the three major principal components reveals more distinction between the groups than was offered by univariate analysis. Three-dimensional extensions of these techniques allow better insight than any of the two-dimensional plots, but these three-dimensional versions require more plots for complete interpretation.Non-linear mapping of the data is the best way of retaining the distances and a fairly good separation is achieved in the plot. The plot is less informative about shape and relative position of the classes.Representation of the data as pictures of faces does not offer additional information and visual clustering is worse than in any of the techniques mentioned.During the analysis many assumed properties of the data are confirmed and a good starting pointfor multivariate classification is attained. Easy visual detection of outliers is offered by all techniques. Unfortunately, valuable information is lost in this data set by deleting some incomplete variables.