Diagnostic probability matrix for identification of slowly growing mycobacteria in clinical laboratories

Abstract

A probability matrix is presented for identification of slowly growing mycobacteria that are likely to be encountered in clinical laboratories. The matrix includes 23 features that are useful for identifying members of 14 species or species complexes. The computer program identifies strains as a function of the ID (identification) score, which measures the discrimination among possible alternative identifications, and the R (ratio) score, which measures the degree of fit to the most likely taxa. It is not necessary to employ all 23 tests when initiating an identification; the program will suggest additional tests to perform when a partial data set fails to yield a definitive identification. Two independent sets of cultures comprising a total of 1,212 strains were used to test the matrix. Correct diagnoses were based on clustering behavior in numerical taxonomic analysis with larger numbers of features. The probable efficiencies with the two sets were 94.2 and 83.4%, respectively, and the accuracy of the definitive identifications for both sets exceeded 95%. A discussion is presented of situations when it may be appropriate to override an R score that has caused the rejection of an identification and to thereby enhance the efficiency.