Statistical techniques for evaluating the diagnostic utility of laboratory tests.
Clinical laboratory data is used to help classify patients into diagnostic disease categories so that appropriate therapy may be implemented and prognosis estimated. Unfortunately, the process of correctly classifying patients with respect to disease status is often difficult. Patients may have several concurrent disease processes and the clinical signs and symptoms of many diseases lack specificity. In addition, results of laboratory tests and other diagnostic procedures from healthy and diseased individuals often overlap. Finally, advances in computer technology and laboratory automation have resulted in an extraordinary increase in the amount of information produced by the clinical laboratory; information which must be correctly evaluated and acted upon so that appropriate treatment and additional testing, if necessary, can be implemented. Clinical informatics refers to a broad array of statistical methods used for the evaluation and management of diagnostic information necessary for appropriate patient care. Within the realm of clinical chemistry, clinical informatics may be used to indicate the acquisition, evaluation, representation and interpretation of clinical chemistry data. This review discusses some of the techniques that should be used for the evaluation of the diagnostic utility of clinical laboratory data. The major topics to be covered include probabilistic approaches to data evaluation, and information theory. The latter topic will be discussed in some detail because it introduces important concepts useful in providing for cost-effective, quality patient care. In addition, an example illustrating how the informational value of diagnostic tests can be determined is shown.
Kazmierczak
S C
SC
East Carolina University School of Medicine, Department of Pathology and Laboratory Medicine, Greenville, NC 27858-4354, USA. kaz@brody.med.ecu.edu
D019963:Clinical Chemistry Tests / Q000191:economics / Q000592:standards*; D003362:Cost-Benefit Analysis; D005069:Evaluation Studies as Topic; D006801:Humans; D012372:ROC Curve; D012680:Sensitivity and Specificity