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J Clin Microbiol. 1990 July; 28(7): 1628-1634

Evaluation of autoSCAN-W/A automated microbiology system for the identification of non-glucose-fermenting gram-negative bacilli.

Veterans Affairs Medical Center, Seattle, Washington 98108.

ABSTRACT

We evaluated the ability of the autoSCAN-W/A (MicroScan Division, Baxter Healthcare Corporation, West Sacramento, Calif.), in conjunction with the dried colorimetric Neg ID type 2 panel (DCP) and new rapid fluorometric Neg ID panel (RFP), to identify non-glucose-fermenting gram-negative bacilli by challenging the system with 310 previously identified reference strains. Of these 310 isolates, 286 organisms were in the DCP data base and 269 were in the RFP data base. Use of the DCP panels resulted in 118 (41.3%) correct and 64 (22.4%) incorrect first choice identifications at greater than or equal to 85% probability, 61 (21.3%) low-probability identifications, and 43 (15.0%) reports of unidentified organisms. The RFP system reported 135 (50.1%) correct and 25 (9.3%) incorrect identifications at greater than or equal to 85% probability and 109 (40.5%) low-probability identifications. Unidentified isolates (DCP system only) and isolates producing low-probability first choice identifications (both systems) required supplementary biochemical testing. Over half (37 of 64 [57.8%]) of the DCP misidentifications were due to four commonly isolated, saccharolytic organisms (Alcaligenes xylosoxidans subsp. xylosoxidans, Pseudomonas putida, Pseudomonas fluorescens, and Xanthomonas maltophilia), while 7 of 25 (28%) of misidentifications in the RFP system were due to P. fluorescens. Of note, the RFP system identified non-glucose-fermenting gram-negative bacilli within 2 h of panel inoculation, allowing additional conventional biochemical tests to be set up the same day on low-probability isolates, whereas only 13.5% of the DCPs could be read at 18 h, with the remainder requiring 42 h of incubation before reading. When organism identifications were recalculated with the updated RFP data base and revised software, only 8.1% of all 310 isolates were misidentified at greater than or equal to 85% probability while 77.1% of the isolates were now correctly reported at this same high probability.

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