Development and evaluation of a urine protein expert system

Abstract

Based on the quantitative determination of creatinine, total protein, albumin, α1-microglobulin, IgG, α(Z)-macroglobulin, and N-acetyl-β,D- glucosaminidase in urine in combination with a test strip screening, the findings of hematuria, leukocyturia, and proteinuria can be assigned to prerenal, renal, or postrenal causes. Using this graded diagnostic strategy as a knowledge base, we developed a computer-based expert system for urine protein differentiation ('UPES') as a decision-supporting tool. The knowledge base was implemented as a combination of 'if/then' rules and two-step bivariate distance classification of marker proteins. The knowledge for this form of pattern recognition was derived from the results for a set of 267 patients with clinically and histologically documented nephropathies. To determine the diagnostic value of UPES, we tested another set of data: results for 129 urine analyses from 94 patients. Using these data, the system reached 98% concordance with the clinical diagnoses for the patients and was superior to the diagnostic interpretations of four human experts. UPES has been successfully integrated into the laboratory routine process, including automated data import.