[Evaluation of the new detectable method of the specimen mix-up in a microbiological examination].

To date, it has been difficult to detect a mix-up of specimens for microbiological examinations. We developed and evaluated a computer program that detects specimen mix-up during the examinations. Once some bacterial species such as Pseudomonas aeruginosa and MRSA are detected in an examination, it is not easy for these bacteria to disappear on subsequent examinations. It is possible to speculate that there has been a specimen mix-up when there are differences in the existence of bacterial species between the results of the past and current examinations. In this study, 1379 false mix-up specimens were created and evaluated for the detection of specimen mix-ups using this method and 417 of the 1379 specimens were detected as mix-ups (sensitivity; 30.2%). Furthermore, when 1486 real specimens were evaluated for the detection of mix-ups, 114 samples were judged to have been mixed-up. Three of these samples were mixed up during the examinations (specificity; 92.5%). These findings suggest that this method is useful for the detection of specimen mix-up during microbiological examinations.

[Quality control for antimicrobial susceptibility test using correlation between MIC results].

The method of National Committee for Clinical Laboratory Standards (NCCLS) is widely used for the daily quality control of the antimicrobial susceptibility test. This method, however, cannot detect the accidental error, although it is useful to detect the systematic error in the examination. We developed a computer program using the correlation between the various antimicrobial susceptibility test results to detect an accidental error. The combinations of the MIC results determined for two antimicrobial agents which showed a high correlation coefficient (> or = 0.7), were selected from 98 bacterial species (2122 strains) isolated from January 2000 to December 2000 at Oita Medical University Hospital. Subsequently, a total of 127 combinations of antimicrobial agents for 13 species were selected on the basis of acceptable correlation ranges. Then, the method were verified with 666 strains (5753 combinations) isolated during the period of January to June, 2001. Twenty-six strains (47 combinations) were identified as an unexpected result, and the occurrence of error were confirmed in 3 strains (12 combinations). These results suggest that this method which evaluated the correlation between MICs against different antimicrobial agents is applicable for the quality control of antimicrobial susceptibility testings.