Location of Repository

Multi-Laboratory Evaluation of an Automated Microbial Detection/Identification System

By P. B. Smith, T. L. Gavan, H. D. Isenberg, A. Sonnenwirth, W. I. Taylor, J. A. Washington and A. Balows

Abstract

An automated and computerized system (Automicrobic System [AMS]) for the detection of frequently encountered bacteria in clinical urine specimens was tested in a collaborative study among six laboratories. The sensitivity, specificity, reliability, and reproducibility of the AMS were determined, and the system was compared with conventional detection and identification systems. In this study, pure cultures and mixtures of pure cultures were used to simulate clinical urine specimens. With pure cultures, the sensitivity of the AMS in identifying the nine groups of organisms most commonly found in urine averaged 92.8%. The specificity averaged 99.4%, and the reliability of a positive result averaged 92.1%. The latter value was strongly influenced by a relatively high occurrence of false positive Escherichia coli results. The AMS was capable of detecting growth of most organisms, including those which it was not designed to identify. However, it identified some of these incorrectly as common urinary tract flora. Reproducibility of results, both within laboratories and among different laboratories, was high. Fast-growing organisms, such as E. coli and Klebsiella/Enterobacter species, were detected often at cell populations well below the AMS enumeration threshold of 70,000/ml. In mixed culture studies, high levels of sensitivity and specificity were maintained but when Serratia species were present in mixtures with other organisms, there was often a false positive report of E. coli. The overall performance of the AMS was considered satisfactory under the test conditions used

Topics: Bacteriology
Year: 1978
OAI identifier: oai:pubmedcentral.nih.gov:275320
Provided by: PubMed Central
Sorry, our data provider has not provided any external links therefore we are unable to provide a link to the full text.

Suggested articles


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.