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CLINICAL MICROBIOLOGY REVIEWS, July 1992, p. 302-327 Vol. 5, No. 30893-8512/92/030302-26$02.00/0Copyright X 1992, American Society for Microbiology

Automated Systems for Identification of MicroorganismsCHARLES E. STAGER' AND JAMES R. DAVIS2*

Department of Pathology, Ben Taub General Hospital, 1 and Department ofPathology,The Methodist Hospital,2 Baylor College ofMedicine, Houston, Texas 77030

INTRODUCTION ........................................................ 302VITEK ........................................................ 305SENSITITRE ........................................................ 308WALKAWAY-96, WALKAWAY-40, AND AUTOSCAN-4 ........................................................ 310ALADIN AND AUTOREADER ........................................................ 315BIOLOG ......................................................... 316MIDI MICROBLIL IDENTIFICATION SYSTEM......................................................... 318AUTOSCEPTOR ......................................................... 320STUDIES COMPARING AUTOMATED IDENTIFICATION SYSTEMS..........................................321DISCUSSION........................................................ 321ACKNOWLEDGMENTS ........................................................ 324REFERENCES.........................................................324

INTRODUCTION

Clinical microbiology has been an especially dynamicdiscipline during the past 10 to 15 years. The excitingdevelopments include the recognition of several new etio-logic agents, the reemergence of some classic pathogens,development of molecular diagnostic tools, and automationof antimicrobial susceptibility testing and microbial identifi-cation. This article explores the development of automatedidentification systems and reviews their performance.To limit confusion, we will avoid terms often used in the

literature such as semiautomated or partially automated.Webster's Dictionary defines automation as the "automati-cally controlled operation of an apparatus, process, orsystem by mechanical or electronic devices that take theplace of human organs of observation, effort, and decision"(77a). None of the systems described is totally automated.We use the term "automated" to describe the instrumentsdiscussed here and trust the reader to understand that someinstruments are more automated than others.The criteria used for inclusion of an instrument in this

review are as follows. (i) The minimum requirement isautomated result entry and identification of microorganisms.Systems requiring manual result entry are not discussed. (ii)The instrument must have a data base for the identificationof a large variety of different microorganisms. Instrumentssuch as automated enzyme immunoassay systems that iden-tify a relatively small number of microorganisms are notdescribed. (iii) The instrument must be available in theUnited States.For studies that have compared the identification accuracy

of two or more automated identification systems, the per-centile (P value) of the chi-square distribution as determinedby the chi-square test has been calculated.The development of the first generation of automated

equipment for clinical microbiology involved essentially twoapproaches. One can be described as the mechanization ofexisting techniques. The second combined mechanizationwith other changes, such as miniaturization and/or incorpo-ration of innovative substrates, inhibitors, or indicators. The

* Corresponding author.

primary goal was to enhance data acquisition and process-ing, particularly with regard to decreasing turnaround time.Although the instruments available today are improve-

ments over the original formulations, they still represent thefirst generation of instruments used to identify microorgan-isms. These instruments are widely accepted and veryhelpful; however, like the instruments used in clinical chem-istry and hematology laboratories, they will continue toevolve to better meet the needs of the clinical microbiologylaboratory. If we compare the modem clinical chemistryanalyzer, with its discrete multianalytes requiring no samplepreparation, with instruments available in clinical chemistryduring the 1960s, we believe we get a glimpse of what thefuture can be in microbiology. At the very least, we shouldtarget that level of automation for clinical microbiology andexpect future generations of equipment to be highly auto-mated, cost-effective, accurate, reliable, and flexible and toprovide rapid turnaround time.Among the first automated microbial identification sys-

tems were the Autobac Series II (formerly called the Auto-bac; Organon-Teknika, Durham, N.C.) and the AvantageMicrobiology Center (formerly called the Abbott MS-2) andQuantum II Microbiology System (Abbott Laboratories Di-agnostic Division, Irving, Tex.). These systems are nolonger manufactured but are still in service in some labora-tories. As an introduction to the systems used for automatedidentification, we believe that it is appropriate to provide abrief review of these systems.The Autobac Series II uses a 19-chambered plastic cuvette

and automatically interprets results of biochemical tests.One chamber is a growth control, and the other 18 containsubstrates composed of antibiotics, dyes, or other chemi-cals. Common members of the family Enterobacteriaceaeand six species or groups of nonfermentative and oxidase-positive gram-negative bacilli can be identified by differentialgrowth inhibition. After off-line incubation of the cuvette for3 to 6 h, a photometer automatically determines growthinhibition by analyzing the light-scattering index of eachchamber. A two-stage quadratic discriminant analysis pro-gram is then used to identify the isolate.

Early studies of the Autobac Series II with currently usedsubstrates demonstrated that 87.7 to 94.8% of the organisms

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AUTOMATED IDENTIFICATION SYSTEMS 303

tested were correctly identified (10, 11, 18, 27, 60, 106, 111).Commonly isolated organisms such as Escherichia coli,Kiebsiella pneumoniae, Proteus mirabilis, and Pseudomo-nas aeruginosa were correctly identified. However, rela-tively common organisms such as Enterobacter cloacae,Citrobacter freundii, Proteus vulgans, Providencia spp.,Salmonella spp., Shigella spp., and Pseudomonas cepacia,as well as uncommon organisms, were frequently incorrectlyidentified. In addition, when organisms not represented inthe data base were tested, they were frequently given anidentification. Only one of these early studies involved acomparison of the Autobac Series II with another automatedidentification system, the Vitek system (bioMerieux Vitek,Inc., Hazelwood, Mo.). The Vitek system will be describedlater in this paper. In this study, Barry et al. (10) tested 1,510members of the family Enterobacteriaceae and nonfermen-tative gram-negative bacilli. The Autobac Series II correctlyidentified 1,443 (95.6%) of the isolates tested, and 1,457(96.5%) were identified by the Vitek system (P > 0.10).Truant et al. (120) recently compared the Autobac Series IIwith the Vitek system and a visually read microtiter system.They tested 434 clinical isolates whose distribution wassimilar to that expected in the clinical setting. If the threesystems disagreed on identification, the API 20E (AnalytabProducts, Plainview, N.Y.) or conventional biochemicalswere used as the reference method. The Autobac Series IIcorrectly identified 357 (82.3%) of the isolates. Organismswith a low relative-probability identification value or uniden-tified organisms accounted for 32% of the errors. Organismsthat posed problems in this group included Citrobacterdiversus, Enterobacter cloacae, E. coli, Proteus mirabilis,P. aenrginosa, and Serratia marcescens. Misidentificationof the organism, particularly with Citrobacter spp., Entero-bacter spp., and E. coli, accounted for 68% of the errors.The Autobac Series II also misidentified organisms frommany different genera as either C. freundii or Enterobacteragglomerans. There was no discernible pattern to explainthese misidentifications. The earlier studies of the Autobacalso reported frequent problems in identification of Citrobac-ter and Enterobacter spp. but not E. coli, as reported byTruant et al. (120). In the study of Truant et al. (120), theAutobac Series II correctly identified 82.3% of the testedisolates, versus 95.6% for the Vitek system (P < 0.001).The Avantage Microbiology Center and Quantum II Mi-

crobiology System use a 20-chamber clear-plastic cartridgefor identification of aerobic gram-negative bacilli or yeasts.The Abbott Bacterial Identification Cartridge (BIC) andAbbott Yeast Identification Cartridge (YIC) each contain 20lyophilized substrates. The Avantage and Quantum II database includes information for identification of 29 genera orspecies of the Enterobacteriaceae, 2 Acinetobacter spp.,Xanthomonas maltophilia, 3 Pseudomonas species orgroups, 5 other oxidase-positive gram-negative bacilli, 15Candida spp., 8 Cryptococcus spp., S Rhodotorula spp., and4 additional yeast genera. After inoculation of the cartridges,baseline turbidimetric or colorimetric readings are obtainedfor the biochemical chambers. With the Avantage, theseoptical readings are simultaneously determined by light-emitting diodes and matched photodetectors, whereas theQuantum II uses a dual-wavelength spectrophotometer toread biochemical chambers in sequence. After off-line incu-bation at 35 to 37°C for 4 to 6 h (BIC) or at 30°C for 22 to 24h (YIC), readings are again obtained to determine turbidi-metric or colorimetric changes. Isolates are identified bycomparison of biochemical test results (oxidase and indole

test results for bacteria and the germ-tube test result foryeasts are manually entered) with a probability matrix.

There have been two reports of the Avantage BIC withcurrently used substrates. Jorgensen et al. (55) reported theresults of a collaborative evaluation of the Avantage BICused to identify commonly isolated nonfermentative or oxi-dase-positive gram-negative bacilli in 5 h. Conventionalbiochemicals were used as the reference system. The organ-isms included in the data base wereAcinetobacter anitratus,Acinetobacter iwoffi, Aeromonas hydrophila, Flavobacte-rium meningosepticum IIb group, P. aeruginosa, P. cepa-cia, Pseudomonas fluorescens-Pseudomonas putida group,X. maltophilia, and Plesiomonas shigelloides. In phase I ofthe study, 200 challenge strains were tested by each of threelaboratories. The overall accuracy was 96%, with 95%correct results for isolates in the data base and 98% forrecognition of biotypes not in the data base. Of 200 isolates,11 either produced a correct identification with low likeli-hood (probability, <80%) or required tests of oxidativefermentation of glucose or 10% lactose to separate A.anitratus from A. iwoffi. In phase II, 100 to 200 routineclinical isolates or selected stock strains were tested by eachlaboratory. In phase II, 95% of the isolates were correctlyidentified. Only 11 (2.5%) of 437 isolates yielded a lowlikelihood of identification, and only 4 (4%) of 103 isolatesnot in the Avantage data base produced misidentifications.P. fluorescens-P. putida presented a significant problem,with only 17 of 21 isolates correctly identified.The other report on the Avantage BIC was an abstract by

Snyder et al. (109). Of 342 members of the family Entero-bacteriaceae and 63 gram-negative other bacteria tested,94% were correctly identified by the BIC. The API 20E wasthe reference method. Serratia spp. and Enterobacter spp.were most frequently responsible for incorrect or inconclu-sive results with the BIC. The authors did not provide thedefinition of an inconclusive result.

Early reports on the Quantum II BIC (85, 93, 116) foundthat 90.1 to 98.2% of the members of the family Enterobac-teriaceae and 83.3 to 93.2% of the nonfermenters andoxidase-positive fermenters tested were correctly identified.Only small numbers of relatively uncommon clinical organ-isms were tested, and generally these organisms posed thegreatest challenge to the Quantum II BIC. The reactionsmost frequently responsible for incorrect identificationswere the lysine, ornithine, esculin, adonitol, inositol, andrhamnose reactions. Only one of these reports was a com-parative study involving another automated system, theVitek system (93). In this study, 382 members of the familyEnterobacteriaceae and 119 nonenteric gram-negative bacilliwere tested. The Quantum II BIC correctly identified 375(98.2%) of the organisms belonging to the family Enterobac-teriaceae and 374 (97.9%) were identified by the Viteksystem (P > 0.05). The Quantum II BIC correctly identified111 (93.2%) of the nonenteric organisms tested, and theVitek system identified 115 (96.6%) (P > 0.05). Rhoden andO'Hara (98) evaluated an updated Quantum II system, withchanges in the software, an expanded data base, and thechange of some biochemical formulations. The authorstested 335 isolates, which consisted of 258 members of thefamily Enterobacteriaceae, 55 nonfermenters, and 22 oxi-dase-positive fermenters. No more than 10 strains in each of65 species were tested. The isolates consisted of both typicaland atypical strains obtained from collections of the Centersfor Disease Control and were not representative of thosecommonly found in the clinical laboratory. The isolates wereidentified by conventional biochemical and serologic meth-

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304 STAGER AND DAVIS

ods. The Quantum II BIC correctly identified 92.6% ofmembers of the family Enterobacteniaceae, 92.7% of thenonfermenters, and 91% of the oxidase-positive fermenters.To obtain these levels of correct identification, additionalserologic (42 isolates), biochemical (34 isolates), or serologicand biochemical (17 isolates) tests were necessary as indi-cated by the Quantum II data base. The distribution ofresults was as follows: 184 (54.9%) were identified with aprobability of 95% or greater; 51 (15.2%) were identifiedwithin the range of 80 to 94.9%; and 57 (17%) were identifiedwith a probability of less than 80%. There were 25 misiden-tified organisms. Four nonfermenters (two P. fluorescensand two P. putida) and two oxidase-positive fermenters(Plesiomonas shigelloides) grew slowly, and this was be-lieved to contribute to their misidentification. Three strains(one indole-negative E. coli, one Kluyvera cryocrescens, andone Yersinia enterocolitica) had biochemical profiles notrecognized by the data base. The remaining 16 misidentifiedorganisms were generally newly recognized genera, and themisidentifications were due to false-positive or false-nega-tive test reactions.Cooper et al. (26) published a collaborative evaluation of

the MS-2 YIC. In phase I of the study, 91% of 179 stockcultures of yeasts were correctly identified. Conventionalmethods were used as the reference system. Isolates difficultto identify included Trichosporon beigelii, Geotrichum spp.,Candidafamata, and Candida humicola. In phase II, 96% of378 clinical isolates were correctly identified. When therewas a discrepancy between the routine laboratory test andthe YIC, conventional methods were used for identification.Only 2% of those correctly identified had a low likelihood ofcorrect identification (probability, <80%). Candida lusita-niae, Candida rugosa, and Cryptococcus laurentii were notproperly identified, although in each case the total number ofisolates tested was small.The only evaluation of the Avantage YIC was reported by

Connelly and Jerris (25). One hundred eighteen yeast iso-lates grown on Sabouraud dextrose agar with and withoutpenicillin (20 U/ml) and streptomycin (40 U/ml) were identi-fied by the YIC and conventional methods. The identity ofisolates grown on the nonselective and selective media andidentified by the YIC agreed with results obtained fromconventional methods 80 and 83% of the time, respectively.Of 18 isolates grown on Mycosel agar, 17 (94%) werecorrectly identified. All isolates of Candida albicans (N = 7),Candida tropicalis (N = 13), Candida glabrata (N = 7), andCryptococcus neoformans (N = 15) grown on selectivemedia were correctly identified by the YIC.Kiehn et al. (61) compared the Quantum II YIC with the

API 20C (Analytab Products) and the BBL Minitek (BectonDickinson, Towson, Md.) systems for the identification of245 yeasts. Discrepancies were resolved by conventionaltests and morphologic observations on commeal-Tween 80agar. The YIC correctly identified 80.4%, misidentified7.8%, and did not identify 11.8% of the isolates. Most of themisidentifications occurred because of false-positive assim-ilation reactions. Of the 29 yeasts not identified, 25 wereCandida spp., of which 50% were either Candida parapsi-losis or Candida pseudotropicalis.The Quantum II YIC was evaluated by Salkin et al. (102)

for identification of 239 yeasts. The API 20C was thereference system. The YIC correctly identified 86% of theisolates, but common yeasts (e.g., C. albicans and C.glabrata) were more readily identified (92% correct) thanwere less frequently encountered yeasts (73% correct).Sekhon et al. (104) evaluated the Quantum II YIC with 115

stocked clinical yeast isolates. The API 20C and conven-tional methods served as the reference. The YIC correctlyidentified 86% of the isolates. C. albicans, Candida guillier-mondii, Candida krusei, C. lusitaniae, C. pseudotropicalis,C. glabrata, Cryptococcus terreus, Cryptococcus laurentii,Hansenula anomala, Rhodotorula glutinis, Rhodotorula ru-bra, and Saccharomyces cerevisiae were identified with100% accuracy. Organisms identified with less accuracyincluded Cryptococcus neoformans (95%), C. parapsilosis(84%), C. tropicalis (80%), and T. beigelii (86%).

Pfaller et al. (94) compared the Quantum II YIC with theYeast Ident (Analytab Products) and the Vitek systems forthe identification of 120 common and 101 relatively uncom-mon clinical yeast isolates. The API 20C was the standardfor comparison. Discrepancies were arbitrated by conven-tional methods and morphologic testing. The YIC correctlyidentified 94% of the common isolates and 67% of theuncommon isolates. Overall, the YIC correctly identified82% of the isolates. Of the 40 misidentifications, 15 occurredat the genus level, 21 occurred at the species level, and 4isolates were unidentified even though they were in theQuantum II data base. C. parapsilosis, which was not in thedata base, was most frequently misidentified, followed by C.tropicalis and Geotrichum spp. Multiple false-positive andfalse-negative reactions were responsible for most of themisidentifications. The authors speculated that standardiza-tion of the inoculum with a spectrophotometer or hemacy-tometer might enhance the performance of the system.Compared with the Quantum II YIC, the Vitek correctlyidentified 83% of the isolates (P > 0.05).Recent abstracts regarding the Quantum II YIC have

reported that 91 and 99% of the tested yeasts were correctlyidentified (5, 48).

In summary, early studies with the Autobac Series IIdemonstrated that most of the gram-negative bacilli com-monly isolated in the clinical laboratory were correctlyidentified (10, 11, 18, 27, 60, 106, 111). However, problemswere reported in the identification of such organisms asEnterobacter cloacae, C. freundii, Proteus vulgaris, Provi-dencia spp., Salmonella spp., Shigella spp., P. cepacia, andmany uncommon organisms. Truant et al. (120) demon-strated additional unexplained problems in identification ofsome common organisms such as E. coli, Proteus mirabilis,and P. aeruginosa. The most recent versions of the Avan-tage BIC correctly identified approximately 95% of thegram-negative bacillus strains tested (55, 116), whereas themost recent version of the Quantum II BIC correctly iden-tified approximately 92% of typical and atypical gram-negative bacillus strains tested (98). Organisms that particu-larly posed identification problems for the Abbottidentification systems included P. fluorescens, P. putida,Serratia spp., and uncommon organisms. The MS-2 YIC(26), Avantage YIC (25), and Quantum II YIC (61, 94, 102,104) performed well in the identification of common yeastisolates, but less commonly encountered yeasts were fre-quently misidentified. The number of genera, species, orgroups of gram-negative bacilli or yeasts in the data base ofthe Autobac Series II or Abbott identification systems islimited. Consequently, these early identification systemshave had significant problems in identification of uncommonorganisms and newly recognized genera.These first automated identification systems are of histor-

ical importance and can serve as a reference of performancelevel for the systems to be described in this review.

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VITEK

The Vitek system had its origins in the 1960s, whenMcDonnell Douglas was contracted by the National Aero-nautics and Space Administration to develop an automatedsystem to detect and identify pathogens directly from urinespecimens of astronauts in space. This system was subse-quently modified and introduced to the clinical microbiologylaboratory as the AutoMicrobic System (AMS) in 1976. TheVitek system, based on bacterial growth in microwells ofthin plastic cards, could identify nine common urinary tractpathogens directly from urine specimens and used the most-probable-number concept to determine the presence of morethan 4.5 x 104 CFU per ml of microbes of urine. Vitek laterdeveloped additional cards that required pure cultures forinocula. These 30 microwell cards contained antibiotics orbiochemical substrates. Susceptibility test cards are avail-able for both gram-negative bacilli and gram-positive bacte-ria with 11 antimicrobial agents per card. Results are avail-able in 4 to 8 h. Vitek has a variety of standard test kits, andcustom-defined test kits can be purchased. Over 40 antimi-crobial agents are currently available on the cards, andresults from each test include an interpolated MIC, as well asthe National Committee for Clinical Laboratory Standardscategories of susceptible, moderately susceptible, interme-diate, and resistant. Identification cards automatically inter-preted by the Vitek system are the Gram-Negative Identifi-cation Test Kit (GNI), the Gram-Positive Identification TestKit (GPI), and the Yeast Biochemical Test Kit (YBC).Identification cards that require off-line incubation and man-ual entry of the results into the Vitek computer are theAnaerobe Identification Test Kit, Neisseria/HaemophilusIdentification Test Kit, and Enteric Pathogen Screen TestKit.The GNI and GPI each contain 29 substrates and a growth

control medium. The GNI substrates include 25 conven-tional biochemical substrates, 3 proprietary substrates, and 1antibiotic. The GNI must be marked if the organism isoxidase positive. The GNI data base includes informationfor identification of 46 species of members of the familyEnterobactenaceae and 39 species of other gram-negativeorganisms. The GPI substrates include 26 based on conven-tional biochemical tests, two antibiotics, and one dye. TheGPI must be marked for catalase-negative, beta-hemolyticorganisms or for coagulase-positive organisms that are cat-alase positive. The GPI data base includes information foridentification of 23 Streptococcus species, 4 Enterococcusspecies, 16 Staphylococcus species, and 4 Corynebacterium,Aerococcus, Listenia monocytogenes, and Erysipelothrixrhusiopathiae species or groups. The YBC contains 26substrates which are based on conventional methods. TheYBC data base includes information for identification of 16Candida species, 6 Cryptococcus species, 3 Rhodotorulaspecies, 2 Tnichosporon species, 3 Geotnichum species, 2Prototheca species, and single species of four additionalgenera.The Vitek system is an integrated modular system consist-

ing of a filling-sealer unit, reader-incubator, computer con-trol module, data terminal, and multicopy printer. The Viteksystem can be purchased with a capacity of 30, 60, 120, or240 cards and can be interfaced with other computers. Adata management center can be added.

Inocula for the identification cards are prepared fromselective (GNI or GPI) or nonselective (GNI, GPI, andYBC) agar media. Inocula for the GNI, GPI, and YBC areprepared by suspending several colonies in 1.8 ml of 0.45 to

0.5% saline and adjusting the suspension to the equivalent ofa no. 1 (GNI and GPI) or a no. 2 (YBC) McFarland standard.The inoculum is automatically transferred to the card via atransfer tube during the vacuum cycle of the filling module.The GNI and GPI are placed in plastic trays, each trayholding up to 30 cards. The tray is placed in the reader-incubator at 35°C, and at hourly intervals a digitized analogoptical reading, proportional to the light attenuation for eachtest well, is obtained for each card. The first reading usuallyestablishes a baseline value, and the amount of light reduc-tion caused by growth or a biochemical reaction in themicrowell is determined on subsequent readings. A prede-termined minimum change is required to differentiate be-tween positive and negative reactions. Final identification bythe GNI is reported between 4 and 18 h. Most of thenon-glucose-fermenting gram-negative bacilli are reported at18 h. Organisms are identified by the GPI between 4 and 15h. The YBC is incubated off-line at 30°C for 24 h and thenplaced in the reader-incubator for a single reading. A mes-sage "reincubate for 24 h at 30°C" indicates that a definitiveidentification requires more incubation time. At 48 h, onemust fill in the 48H mark on the card and obtain a secondreading. The biochemical test results for all cards are com-pared with the data base, and the first and second choices, aswell as their absolute likelihoods and normalized percentprobabilities, are reported. The biochemical test results, aswell as supplemental tests if required, are printed.The Vitek system original gram-negative identification

card was called the Enterobacteriaceae Biochemical Card(EBC) and was designed for the identification of members ofthe family Enterobacteriaceae within 8 h. The EBC con-tained 23 conventional biochemical substrates, 2 inhibitors,and 1 nonconventional substrate. Even though the originalVitek system had a limited data base, numerous studiesfound the EBC to correctly identify 92 to 99% of the testedorganisms (7, 12, 17, 29, 39, 41, 50, 53, 58). Organisms notrepresented in the data base, uncommon organisms, andincorrect reactions for certain biochemicals such as adonitol,arginine, citrate, H2S, and malonate were responsible formost misidentifications. The lack of an indole reaction wasalso considered a weakness of the system. Ferraro et al. (39)found that commonly isolated members of the family Entero-bacteriaceae could be presumptively identified by the EBCin 4 h. They found that 97% of the members of the Entero-bacteriaceae isolated in their laboratory belonged to 11species of six genera. When the EBC was limited to those 11species, 83% of the isolates were correctly identified togenus or species at 4 h. Two percent of the isolates weremisidentified, and 15% were not reported until 8 h.

Vitek then introduced the EBC+, which, with the additionof acetamide, cetrimide, glucose oxidation, and a supple-mental oxidase test, was capable of identifying members ofthe family Enterobacteriaceae within 8 h and nonfermenta-tive and oxidase-positive gram-negative bacilli within 18 h.Studies of the EBC+ demonstrated that 90 to 96% of themembers of the family Enterobacteriaceae and 86 to 97% ofthe gram-negative organisms that were not members of theEnterobacteriaceae tested were correctly identified (7-10,44, 56, 125). However, uncommon organisms were fre-quently misidentified or had a low percent likelihood ofidentification. The new substrates added to the EBC+ wereof limited value in the identification of gram-negative organ-isms that were not members of the Enterobacteriaceae, andonly Aeromonas iwoffi, Aeromonas hydrophila, and P.aeruginosa were consistently correctly identified (7, 8, 54,108, 125). Barry and Badal (8) determined the reliability of

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the EBC+ for early preliminary identification of isolates.They reported that Vitek system identifications were 96%accurate at 4 h if all results with a probability of <80% wereexcluded and if Morganella morganii, Acinetobacter spp.,

Yersinia spp., Salmonella spp. (other than Salmonellatyphi), Shigella spp. (other than Shigella sonnei), Enterobac-ter agglomerans, P. cepacia, the P. putida-P. fluorescensgroup, or other uncommon genera such as Klebsiella spp.(other thanK pneumoniae or Klebsiella oxytoca), Citrobac-ter amalonaticus, Serratia liquefaciens, or Vibno spp. were

not reported. If these criteria were adhered to, about half ofthe isolates in their series could be identified accurately at 4h.The GNI has now replaced the EBC+, and there have

been changes and modifications in the substrates and up-

dates in the data base. Pfaller et al. (93) reported on theaccuracy of the GNI for the identification of 382 members ofthe family Enterobacteriaceae and 119 nonenteric gram-

negative bacilli (representing five genera and nine species).The API 20E served as the reference method. The GNIcorrectly identified 97.9% of the members of the Enterobac-teniaceae and 96.6% of the nonenteric organisms. Only twomembers of the Enterobacteriaceae and one of the nonen-

teric bacteria required additional testing. Of the 12 organ-

isms misidentified, 2 were misidentified at the genus leveland 2 were misidentified at the species level. The remainingeight isolates were not identified even though they were inthe data base. Several aberrant biochemical reactions were

responsible for most misidentifications. Enterobacter spp.

were most frequently misidentified. The GNI identified 35%of the organisms within S h (44% of the members of theEnterobacteriaceae and 7% of the nonenteric organisms)and 63% within 6 h (75% of the members of the Enterobac-tenaceae and 25% of the nonenteric organisms).

Truant et al. (120) reported on the performance of theGNI, testing 434 clinical isolates whose distribution was

similar to that expected in the clinical setting. The GNIcorrectly identified 95.6% of the isolates with a likelihood of.90%. However, many uncommon members of the familyEnterobacteriaceae were not evaluated, and only threegenera and seven species of nonenteric bacilli were in theseries. Enterobacter cloacae was the only problem organ-

ism, with 8 of 41 isolates yielding either a low probability or

being unidentified. This was thought to be due to thepremature reading of the arginine, lysine, and omithinereactions.

Plorde et al. (96) evaluated the GNI for identification of419 non-glucose-fermenting gram-negative bacilli represent-ing 14 genera and 35 species. The isolates were identified byconventional biochemicals. Of the 419 tested organisms, 63belonged to species not included in the data base. Fifty-eight(92%) of these isolates were appropriately reported as uni-dentified. Of the 356 test organisms included in the database, 307 (86.2%) were correctly identified, 36 (10.1%) were

not identified, and 13 (3.7%) were misidentified. If thefirst-choice identification had been accepted as correct, 18(4.3%) isolates would have been erroneously reported.When first-choice identifications were rejected if the report

indicated "questionable biopattern" and when all isolateswere screened for characteristic odor and an appropriateantimicrobial profile before acceptance of the report, misi-dentifications were reduced to 1.2%. The average time to

identification was 15 h.The most recent study of the GNI was reported by Pfaller

et al. (95). They tested 292 members of the family Entero-bactenaceae and 91 nonenteric bacilli (including 8 genera

and 11 species). The API 20E and conventional biochemicaltests were used as the reference system. The GNI correctlyidentified 96.2% of the members of the Enterobactenaceaeand 90.1% of the nonenteric bacilli. Of the 20 misidentifica-tions, 4 were at the genus level, 4 were at the species level,and 12 organisms were not identified even though they wereincluded in the data base. Acinetobacter spp., P. aerugi-nosa, and Enterobacter spp. were most frequently misiden-tified. The GNI identified 58% of the members of theEnterobacteriaceae at 4 h, 31% at 5 to 8 h, and 11% at 9 to13 h. Of the nonenteric isolates, 15% were identified at 4 h,an additional 45% were identified at 5 to 8 h, and anadditional 40% were identified at 9 to 18 h. To determineinterlaboratory agreement, a common set of 134 isolates wastested at the two participating laboratories. The overallagreement between the two laboratories was 92%.Recent abstracts involving the GNI have been published.

Colonna et al. (24) tested 358 members of the family Entero-bacteriaceae and 142 nonfermenters with the GNI. The API20E and API NFT (Analytab Products) were used as thereference systems. They found that 94.7% of the members ofthe Enterobacteriaceae were correctly identified, while 3.6%were inconclusive, 1.1% were incorrectly identified, and0.6% yielded no identification. However, only 79.6% of thenonfermenters were properly identified, whereas 13.4%were inconclusive, 2.8% were incorrectly identified, and4.2% yielded no identification.

In 1989, the GNI data base was expanded to include Vibrioalginolyticus, Vibrio damsela, Vbrio fluvialis, and Vibriovulnificus. Farnham et al. (38) evaluated the GNI and thisexpanded data base with 212 members of the family iri-onaceae, including 24 A. hydrophila, 27 Plesiomonasshigelloides, 35 V. alginolyticus, 37 Vibrio cholerae, 12 V.damsela, 18 V. fluvialis, and 24 Vibio parahaemolyticusisolates and 35 Vibrio spp. Reference systems were the API20E and conventional biochemicals. The overall correlationof the GNI with the reference identification was 95%. Of thenew species in the data base, 90% of V. alginolyticus, 97% ofV. vulnificus, 100% of V. damsela, and 100% of V. fluvialisisolates were correctly identified.There have been limited studies of the GPI for identifica-

tion of Staphylococcus spp. Three studies (2, 45, 101) wereconducted soon after the GPI was introduced, at which timethere were 27 substrates and only seven Staphylococcusspecies in the data base. Staphylococcus aureus, Staphylo-coccus epidermidis, and Staphylococcus saprophyticus werecorrectly identified in the range of 88 to 100%. However, theaccuracy for the remaining four species in the data baseranged from 0 to 71%. In addition, 47 to 65% of themisidentified species were incorrectly identified as otherStaphylococcus species. A recent study of the GPI involved130 strains representing 14 species of staphylococci of bo-vine origin (72). The isolates were identified by conventionalmethods. Eighty-five of the strains (representing 12 species)were in the Vitek system data base, and 58 (68%) werecorrectly identified. Forty-seven of these were identified atthe .90% level of confidence. The GPI correctly identified100% of the S. aureus and S. epidermidis, 91% of theStaphylococcus xylosus, and 80% of the Staphylococcussimulans isolates. Staphylococcus chromogenes (N = 43)was not in the data base; 39 strains were misidentified (27 asS. simulans), and 4 strains were unidentified. A recentabstract (46) compared species identification of the coagu-lase-negative staphylococci by the GPI with species identi-fication by the methods of Kloos and Schleifer (64). The GPIcorrectly identified 148 of 149 S. epidennidis, 37 of 39



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Staphylococcus haemolyticus, and 20 of 27 other coagulase-negative staphylococcal strains.

Streptococcus pyogenes and species of group D strepto-cocci and enterococci were accurately identified with a highlevel of confidence (3, 36, 101) by the GPI. The accuracy ofidentification of Streptococcus agalactiae varied from 85.7to 100%. However, only 61.9 to 85.7% of these strains wereidentified with a high level of confidence. Ruoff et al. (101)and Appelbaum et al. (3) found that the GPI correctlyidentified 36.3 to 76.2% of the beta-hemolytic group C, F,and G streptococci. Ruoff et al. (101) reported excellentresults for Streptococcus pneumoniae with the GPI. How-ever, two subsequent studies identified 80.9 to 84.4% ofStreptococcus pneumoniae isolates, and only 76.6 to 76.9%of the results were at a high level of confidence (3, 36). Fourstudies of the GPI for identification of the viridans groupshowed an accuracy of 57.2 to 79%, and many of the strainswere identified at a low level of confidence (3, 36, 92, 101).There have been single studies of the GPI for identificationof Listeria and Aerococcus spp. Ruoff et al. (101) reportedthat 30 of 30 L. monocytogenes isolates were correctlyidentified, all with a likelihood of between 90 and 100%.Facklam et al. (36) found that 24 (92.3%) of 26 Aerococcusspp. were correctly identified, 84.6% with a high level ofconfidence. There are no reported studies of the GPI foridentification of Corynebacterium spp. or Erysipelothrixrhusiopathiae.

Recent abstracts describing the GPI used for viridansstreptococci include one by Schiminsky and Ferrieri (103),who tested 84 viridans streptococci, 56 enterococci, and 3group D streptococci. Isolates were identified by conven-tional biochemical tests. Only one enterococcus was improp-erly identified to species level. Streptococcus sanguis II,Streptococcus sanguis I, Streptococcus mitis, and Strepto-coccus salivarius were most frequently isolated, and 79% ofthese isolates were correctly identified. Overall, 63 (75%) ofthe 84 viridans streptococci were properly identified.Adridge et al. (1) identified 222 clinical isolates of viridansstreptococci from blood and odontogenic infections by usingthe GPI without supplemental biochemical tests. Conven-tional methods served as the reference system. The GPIcorrectly identified 46.1% of the isolates, with 13% yieldingno identification. Hinnebusch et al. (51) tested 186 viridansstreptococcal isolates (150 blood culture isolates and 36stock strains) by using the GPI and conventional biochemi-cals. The GPI properly identified 60.2% of the isolateswithout supplemental tests. With supplemental tests, 80.6%were correctly identified. If three rarely encountered species(Streptococcus morbillorum, Streptococcus uberis, andStreptococcus acidominimus) were excluded, the GPI cor-rectly identified 79.7% of the remaining isolates withoutsupplemental tests.

Early studies of the YBC demonstrated 84.9 to 96%accuracy of identification of yeasts (49, 66, 88). Generally,common clinical isolates such as C. albicans, C. tropicalis,C. pseudotropicalis, C. parapsilosis, C. glabrata, Crypto-coccus neoformans, Cryptococcus albidus, R. rubra, and S.cerevisiae were readily identified with a high level of confi-dence. However, uncommon clinical isolates posed more ofa problem for the system. Hasyn and Buckley (49) identified84.9% of 352 freshly isolated, germ tube-negative yeasts withthe YBC, but 10.7% of these required microscopic examina-tion of inoculated cornmeal-agar medium for morphologicalcharacteristics. Oblack et al. (88) reported an accuracy of84% for 253 clinical yeast isolates when YBC biochemicaldata were used, but this improved to 96% when morpholog-

ical characteristics were used. The manufacturer recom-mended incubation of the YBC for 24 h. Oblack et al. (88)recorded reactions at 24, 48, and 72 h for 236 isolates. Elevenisolates were misidentified at 24 h, but 7 of these werecorrectly identified at 48 h. The other four isolates were notcorrectly identified even at 72 h. However, seven isolatescorrectly identified at 24 h were misidentified at 48 or 72 h.Pfaller et al. (94) reported on the YBC for the identificationof 120 common clinical yeast isolates and 101 less commonclinical yeast isolates, with the API 20C as the reference.The YBC correctly identified 103 (86%) of the commonisolates and 81 (80%) of the uncommon isolates, for an 83%overall accuracy. The YBC misidentified 37 isolates (13 atthe genus level and 18 at the species level), and 6 isolateswere not identified even though they were in the Vitek database. Several isolates, including Candida humicola, Can-dida lambica, C. lusitaniae, Candida paratropicalis, andPrototheca wickerhamii, were not in the Vitek data base andwere incorrectly identified. Five yeasts (C. tropicalis, C.parapsilosis, C. lambica, C. paratropicalis, and C. glabrata)accounted for 62% of the YBC misidentifications. Multiplefalse-positive and false-negative biochemical tests were re-sponsible for the misidentifications.

Vitek expanded the YBC data base in 1989 and allowed fora 48-h incubation when necessary. El-Zaatari et al. (34)evaluated this data base with 398 clinical isolates, including9 genera and 26 species of yeasts and yeastlike fungi, thehyphomycete Geotrichum candidum, and the achlorophyl-lous alga P. wickerhamii. The API 20C was the referencemethod. The YBC identified 243 (99.2%) of 245 of thecommon isolates (C. albicans, C. parapsilosis, C. tropicalis,C. glabrata, and Cryptococcus neoformans) and 144 (94.1%)of 153 of the uncommon isolates. The overall accuracy was97.2%. Of the 387 correctly identified isolates, 282 (70.8%)were identified at 24 h, 105 (26.4%) were identified at 48 h, 67(17.8%) required additional morphological evaluation, and15 (3.8%) required both additional biochemical tests andmorphological studies. They concluded that the YBC withan updated and expanded data base was markedly improvedand provided a rapid and reliable identification of yeasts.Recent abstracts confirm that the new YBC data base

provides accurate identification of clinical yeast isolates.Forsythe et al. (40) evaluated the YBC with 34 stock strainsand 144 clinical yeast isolates. The standard for comparisonwas the API 20C. The overall agreement between systemswas 97%. The agreement was 99.5% when only clinicalisolates were examined. The YBC was evaluated by Yamaneet al. (127), who used 292 strains (26 species) of yeasts. TheAPI 20C, and morphological examination when necessary,was the standard for comparison. Excellent agreement wasobtained for C. albicans, C. tropicalis, C. parapsilosis, C.glabrata, S. cerevisiae, and Cryptococcus neoformans.Overall, 95.2% of the isolates, including those that requiredsupplemental tests, were correctly identified. Of the lesscommon species, 91.8% were correctly identified. Quinn andHorstmeier (97) tested the YBC with 368 commonly encoun-tered clinical yeast isolates. The standard method of identi-fication was not mentioned. The YBC correctly identified93.5% of the isolates. Supplemental testing or morphologicalexamination was required for 13.8% of the isolates.McWhirter et al. (77) tested the YBC with 105 germ tube-negative clinical yeast isolates. Conventional methods wereused as the standard. The YBC correctly identified 86% ofthe isolates in 24 h. After supplemental tests were per-formed, 98% of the isolates were correctly identified. Sim-monds et al. (107) reported on the YBC for identification of

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TABLE 1. Performance of automated identification systems for identification of members of the family Enterobactenaceae

No. of % of identifications that were:System organisms Reference

tested Correct Inconclusive Not made Incorrect

Vitek GNI 382 97.4 0.5a 1.6 0.5 93Vitek GNI 339 96.2 _b _C -C 120Vitek GNI 292 94.5 1.7d 1.7 2.1 95Vitek GNI 358 94.7 3.6e 0.6 1.1 24Sensititre AP80 358 93.3 0.3e 0 6.4 24Sensititre AP80 180 78.9 f _c _c 70autoSCAN-4 GNB 307 84.0 11.49 2.0 2.6 99autoSCAN-4 GNB 372 95.4 2.79 0 1.9 42WalkAway-96 R-GNB 292 86.3 8.99 0.3 4.5 95WalkAway-96 R-GNB 358 83.0 7.8e 0 9.2 24WalkAway-96 R-GNB 180 86.1 f _c _- 70Biolog GN MicroPlate 212 52.0" 2.0 38.0 8.0 79

60.0' 10.2 21.3 8.5

Percent correct identification with first-choice likelihood of <80%. Supplemental tests were required to confirm correct identification.b Identifications with first-choice likelihood of <90% were considered inconclusive; the percent inconclusive identifications not stated.c Performance could not be determined from data presented.d Percent correct identification with first-choice likelihood of <90%.e Identification likelihood not stated; percent inconclusive identifications that were correct identifications not stated.f Identification likelihood not stated; percent inconclusive identifications not stated.g Percent correct identification with first-choice likelihood of <85%.h Correct identification to species level at 4 h.Correct identification to species level at 24 h.

232 clinical yeast isolates from 10 genera and 29 species. TheAPI 20C was used as the standard method, and discrepan-cies were resolved with conventional assimilation and fer-mentation tests. The overall accuracy of the YBC was85.3%. A 48-h reading of the YBC was required for 47.8% ofthe isolates. When biotypes not found in the data base wereexcluded, the accuracy was 94%. Morphological observa-tions were needed to identify >50% of the isolates, andconfirmatory biochemical tests were required for identifica-tion of 8% of the isolates by the YBC.As will be true for all of the automated identification

systems considered, Vitek Systems has continued to im-prove the capability of its systems to accurately identifymicroorganisms. Consequently, when attempting to deter-mine the performance of the Vitek system, one shouldconsider the publications that have evaluated or comparedthe latest software, data base, biochemical configuration, orother performance characteristics of their system. In consid-ering recent studies of the GNI, members of the familyEnterobactenaceae were correctly identified in the range of94.7 to 96.2%, with Enterobacter spp. the most frequentlymisidentified (24, 95, 120). Gram-negative organisms thatwere not members of the Enterobactenaceae were correctlyidentified in the range of 79.6 to 95%, with P. aeruginosa andAcinetobacter spp. the most commonly misidentified (24, 38,95, 120). S. aureus, S. epidermidis, S. saprophyticus, S.xylosus, and S. haemolyticus have been correctly identifiedby the GPI in the range of 88 to 100%, but less commonlyencountered species have frequently been misidentified oridentified with a low likelihood (2, 45, 46, 72, 101). Strepto-coccus pyogenes and species of group D streptococci andenterococci have been accurately identified with a high levelof confidence by the GPI, but other Streptococcus specieshave posed more of a problem for the system (1, 3, 36, 51,92, 101, 103). In recent publications, the YBC correctlyidentified 93.5 to 98% of tested yeasts and yeastlike isolates,when organisms not represented in the data base wereexcluded and when required supplemental tests or morpho-logical examinations were performed (34, 40, 77, 97, 107,127).

The Vitek system is a comprehensive microbial identifi-cation system that needs further improvement in its identi-fication accuracy for some Staphylococcus and Streptococ-cus species, as well as for unusual and uncommon bacteriaand yeasts. In addition, a shortened time for identification ofnon-glucose-fermenting gram-negative bacilli, gram-positivebacteria, and yeasts would be desirable. On-line incubationof the YBC, Anaerobe Identification Test Kit, Neisseria/Haemophilus Identification Test Kit, and Enteric PathogenScreen Test Kit cards would further enhance the utility ofthe Vitek system.

Tables 1, 2, 3, and 4 demonstrate the identification accu-racy for members of the family Enterobacteriaceae, forstrains of other gram-negative organisms, for Staphylococ-cus strains, and for strains of yeasts and yeastlike organisms,respectively, for each of the automated identification sys-tems that can currently be purchased.

SENSMTTRE

The Sensititre fluorogenic system (Radiometer America,Inc., Westlake, Ohio) is a modular system composed of acomputer and an automated reader. This system identifiesgram-negative bacilli and performs susceptibility tests onboth gram-positive and gram-negative bacteria in either 5 or18 h. Breakpoint or MIC capability for gram-negative andgram-positive bacteria is available for 54 antimicrobialagents. The Sensititre AP80 panel data base includes infor-mation for the identification of 84 members of the familyEnterobacteriaceae, 24 oxidase-positive fermenters, 16pseudomonads, and 16 other nonfermenters.The automated Sensititre system is composed of a Sensi-

titre Fluorescence AutoReader, a Digital PRO 380 computerwith 768K of main memory, 20 or 30 MB hard-disk and dualfloppy-disk drives, data terminal, and multicopy printer. ASensititre Automatic Inoculator is also available. The Sen-sititre software has menus for demographics, interpretationof culture results, data analysis, report generation, andepidemiological evaluations.The Sensititre AP80 gram-negative identification test plate

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TABLE 2. Performance of automated identification systems for identification of strains of gram-negative organisms that were notmembers of the Enterobacteriaceae



Vitek GNI 119 95.8 0.8a 1.7 1.7 93Vitek GNI 95 93.7 _b -C -C 120Vitek GNI 356 74.7 11.5d 10.1 3.7 96Vitek GNI 91 86.8 3.3d 7.7 2.2 95Vitek GNI 142 79.6 13.4e 4.2 2.8 24Vitek GNI 212 95.0 -fc _c 38Sensititre AP80 142 71.1 5.6e 7.0 16.2 24Sensititre AP80 60 35.0 f -c -c 70autoSCAN-4 GNB 98 58.2 36.79 4.1 1.0 99WalkAway-96 R-GNB 91 92.3 3.39 0 4.4 95WalkAway-96 GNB 310 41.3 21.39 15.0 22.4 117WalkAway-96 R-GNB 310 50.2 40.5 0 9.3 117WalkAway-96 R-GNB 142 74.6 14.1w 0 11.3 24WalkAway-96 R-GNB 60 81.7 -fc -c 70Biolog GN MicroPlate 140 38.6h 12.8 33.6 15.0 79

55.8' 12.6 20.8 10.8a Percent correct identification with first-choice likelihood of <80%. Supplemental tests were required to confirm the correct identification.b Identifications with first-choice likelihood of <90% were considered inconclusive; percent inconclusive identifications not stated.c Performance could not be determined from data presented.dPercent correct identification with first-choice likelihood of <90%.eIdentification likelihood not stated; percent inconclusive identifications that were correct identifications not stated.f Identification likelihood not stated; percent inconclusive identifications not stated.g Percent correct identification with first-choice likelihood of <85%.h Correct identification to species level at 4 h.Correct identification to species level at 24 h.

will test three separate organisms, with each section con- cence. Therefore, absence of fluorescence indicates a posi-taining 32 dried, fluorescently labeled substrates. Substrates tive test. In other cases (carbon assimilation reactions,are linked to a fluorophore (4-methylumbelliferone or 7-ami- decarboxylase reactions, and urea hydrolysis), the pH isno-4-methylcoumarin). Some of the substrates (peptidase, increased, thus producing fluorescence.pyranosidase, phosphatase, and glucuronidase) do not di- Inocula are prepared, using a built-in nephelometer in therectly fluoresce. However, when enzymes release the fluo- Automatic Inoculator, by transferring one or more coloniesrophore, it will fluoresce. The fluorogenic reaction occurs from either nonselective or selective agar media to sterilewhen UV light is absorbed and electrons are raised to higher distilled water to equal a no. 0.5 McFarland standard. Theenergy levels. When these electrons return to their original nephelometer is calibrated to be linear over the range of no.states, photons with lower energies and hence longer wave- 0.1 to 0.9 McFarland standard. The Automatic Inoculatorlengths are emitted. Fluorometric reactions, which detect dispenses 50 ,ul of inoculum into each test well and thenpH changes, are also used. In one case, the pH of the dispenses 150 ,ul of oil into the urea and a proprietarysubstrate is originally alkaline and the fluorophore is fluo- substrate microwell. A transparent adhesive seal with per-rescent. With oxidation or fermentation of carbohydrates, forations over the wells containing malonate, pyruvate,acid production lowers the pH and quenches the fluores- citrate, and agmatine is applied to the plate. The test plate is

TABLE 3. Performance of automated identification systems for identification of Staphylococcus species



Vitek GPI 150 67.3a 0 0 32.7 2Vitek GPI 190 83.2b 6.8c 3.7 6.3 45Vitek GPI 148 89.9 4.1c 0.7 5.3 101Vitek GPI 85d 68.2e 0 11.8 20.0 72

130d"f 44.6 0 11.6 43.8Vitek GPI 215 95.3 0 1.9 2.8 46autoSCAN-4 GPB 175 83.4 -9 -g 52WalkAway-96 R-GPB 239 91.6 3.8 2.1 2.5 114

a Percent correct identification regardless of first-choice likelihood percentage.b Percent correct identification with required supplemental physiologic tests.c Identified as Staphylococcus spp.d Staphylococcal strains of bovine origin.e Percent correct identification for staphylococcal species present in Vitek data base.f Forty-five staphylococcal strains were from species not contained in the Vitek data base.g Performance could not be determined from available data.

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TABLE 4. Performance of automated identification systems foridentification of yeasts and yeastlike organisms

~No. of % Correctly

System organisms identified Referencetested

Vitek YBC 352 84.9a 49Vitek YBC 1,106 93.4a 66Vitek YBC 253 95.7a 88Vitek YBC 120b 85.8c,d 94Vitek YBC iole 80 2C,d 94Vitek YBC 245b 99.2a 34Vitek YBC 153e 94.la 34Vitek YBC 178 97.0f 40Vitek YBC 292 95.2a 127Vitek YBC 368 93.5a 97Vitek YBC 105 98.la 77Vitek YBC 232 94.0a 107autoSCAN-4 YIP 232 96.6a 107autoSCAN-4 YIP 357 62e 112autoSCAN-4 YIP 189 79d 14

a Correct identification with required microscopic morphological examina-tions or supplemental physiologic tests.

b Common yeast isolates.c Percent correct identification with first-choice likelihood of 280%.d No supplemental microscopic morphological examinations or physiologic

tests performed.e Uncommon yeast isolates.f Identification likelihood not stated; requirements for microscopic morpho-

logical examinations or supplemental physiologic tests not stated.

incubated off-line at 35 to 37°C and may be read on theSensititre Fluorescence AutoReader after 5 or 18 h ofincubation.Reagent addition is not required with the AP80 identifica-

tion panel. The test plate is placed into the AutoReader,where it is automatically transported to the reader station.The light source is a broad-band xenon flash lamp (360 nm)that generates microsecond pulses of high-peak-power light.The light goes through interference filters and a beam-splitting cube with wavelength-selective coatings to lensesthat focus the light onto the test well and the detector. Thedetector is a photomultiplier tube that transmits the rawfluorescence data to the DEC PRO 380 computer. It requiresapproximately 30 s to read a plate. The biocode generated ismatched to the Sensititre data base. There may be additionaltest prompts for indole, oxidase, motility, or pigment pro-duction. A reincubate prompt also occurs at 5 h if there is noidentification or a low probability of identification. The resultof each test is printed, and for each organism listed, thequality of the identification (excellent, good, group identifi-cation, etc.) is determined on the basis of calculated proba-bility values. Test results against any identified organism(s)are also listed.There have been three recent studies of the Sensititre

AP80 identification panel. Colonna et al. (24) tested 358members of the family Enterobacteriaceae and 142 nonfer-menters isolated from clinical specimens. The API 20E andAPI NFT were used as the reference systems. Results forthe members of the Enterobacteriaceae were 93.3% correct,6.4% incorrect, and 0.3% inconclusive, and results for thenonfermenters were 71.1% correct, 16.2% incorrect, 5.6%inconclusive, and 7.0% unidentified. Because of the limita-tion inherent in an abstract, it was not stated whetherisolates were correctly identified to genus or to species.Also, it was not stated whether these data applied to the 5- or18-h identification with the AP80. Lyznicki et al. (70) re-

ported on Sensititre AP80, using the MicroScan NegativeCombo type 7 panel for identification of gram-negative rodsas the reference system. Discrepant results were resolvedwith either the API 20E or the API NFT. For entericgram-negative rods, 79% (142 of 180 strains) were correctlyidentified to species, whereas for nonfermentative gram-negative rods, only 35% (21 of 60 strains) were correctlyidentified to species. Incorrect identifications occurred with7.9% (19 of 240) of the strains tested. Weckbach et al. (122)reported a clinical trial of the Sensititre AP80 identificationsystem at three study centers. Additional data were given atthe time of presentation of the paper. The expanded data(110) included 879 members of the family Enterobac-teriaceae, 100 Pseudomonas spp., 33 Acinetobacter spp.,and 11 other gram-negative bacilli (including Aeromonas,Plesiomonas, and Bordetella spp.). Results for the AP80were compared with results for the API 20E at S and 18 hindependently. Discrepant results were arbitrated by theCenters for Disease Control with conventional biochemicals.At 5 h, 95.9% of the isolates were identified to genus, while92.4% had an acceptable species match. A total genus matchfor isolates tested at 18 h was 98.4%, and an acceptablespecies match was 96.3%. For the AP80, the percentage ofisolates that required additional rapid tests at 5 h was 3.5versus 2.0% at 18 h. The incidence of additional testsrequired by the API 20E was 5.6%. Even though the glucosenonfermenters represented only 13% of the isolates tested,33% required reincubation after the 5-h reading.

Limitations of the Sensititre system include the require-ment for off-line incubation of AP80 panels, the necessity ofperforming additional rapid tests for some isolates, and thenecessity for reincubating some AP80 panels. In addition,the development of systems for the rapid identification ofgram-positive bacteria, anaerobes, and yeasts would expandthe utility of the Sensititre fluorogenic system. RadiometerAmerica will introduce the ARIS (Automated Reading andIncubation System) to the United States in 1992. ARIS willprovide "walk-away" automation and plate reading technol-ogy based on the Sensititre AutoReader.The limited results available with the Sensititre AP80

identification system are very encouraging. However, fur-ther evaluation of this identification system, particularly indefining its accuracy for both common and uncommonorganisms at both 5 and 18 h, is needed.

WALKAWAY-96, WALKAWAY-40, AND AUTOSCAN-4

The WalkAway-96 (formerly called the autoSCAN-W/A)and WalkAway-40 (Baxter Diagnostics, Inc., MicroScanDivision, West Sacramento, Calif.) are computer-controlledsystems that will incubate microtiter panels and automati-cally interpret biochemical or susceptibility results witheither a photometric or a fluorogenic reader. MicroScan alsomanufactures the autoSCAN-4, which requires off-line incu-bation and, with the exception of fluorogenic panels, will testthe same panels as the other two automated systems. Allthree systems perform susceptibility tests on aerobic gram-negative bacilli, gram-positive bacteria, and anaerobes. Flu-orogenic panels provides a 3.5- to 7-h susceptibility result foraerobic gram-negative bacilli and a 3.5- to 15-h susceptibilityresult for aerobic gram-positive bacteria. Conventional pan-els provides a 15- to 24-h susceptibility result for aerobicbacteria and a 24- to 48-h susceptibility result for anaerobes.MicroScan has numerous panel types, including both MICand breakpoint susceptibility panels. Custom panels are alsoavailable. The three systems automatically identify gram-



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negative bacilli, gram-positive bacteria, fastidious aerobicbacteria, anaerobes, and yeasts.The rapid fluorogenic panels for identification of gram-

negative bacilli (R-GNB) and gram-positive bacteria (R-GPB) use fluorogenic substrates (4-methylumbelliferone or7-amino-4-methylcoumarin attached to a phosphate, sugarmoiety, or amino acid) or fluorometric indicators. Identifi-cation is based on hydrolysis of fluorogenic substrates, pHchanges following substrate utilization, production of spe-cific metabolic by-products, or the rate of production ofspecific metabolic by-products after 2 h of incubation. Themodified conventional panel for identification of gram-nega-tive bacilli (GNB) has 29 modified conventional or chro-mogenic tests and six antibiotics, and results are available in15 to 42 h. Both the R-GNB and GNB data bases includeinformation for identification of 59 groups, genera, or speciesof members of the Enterobacteriaceae and 57 groups, gen-era, or species of nonfermentative and oxidase-positivegram-negative bacilli. The modified conventional panel foridentification of gram-positive bacteria (GPB) has 25 modi-fied conventional or chromogenic substrates, one dye, andthree antibiotics and yields an identification in 15 to 42 h.Both the R-GPB and GPB data bases include information foridentification of 24 genera or species of the family Micrococ-caceae, 18 members of the family Streptococcaceae, 4Enterococcus spp., Aerococcus viridans, and L. monocyto-genes. The MicroScan Rapid Haemophilus and NeisseriaIdentification Panel (HNID) has 17 modified conventional orchromogenic tests and one antibiotic, and the data baseincludes information for identification of Haemophilus influ-enzae (seven biotypes), Haemophilus parainfluenzae (fourbiotypes), Haemophilus aphrophilus-Haemophilus paraph-rophilus, Haemophilus haemolyticus, four Neisseria spe-cies, Moraxella catarrhalis, and Gardnerella vaginalis in 4h. The MicroScan Rapid Anaerobe Identification Panel(AIP) has 24 modified conventional or chromogenic sub-strates, and the data base includes information for identifi-cation of 21 anaerobic gram-negative bacilli, 13 anaerobicnon-spore-forming gram-positive bacilli, 8 anaerobic gram-positive cocci, and 16 clostridia in 4 h without anaerobicincubation. The MicroScan Rapid Yeast Identification Panel(YIP) has 27 modified conventional or chromogenic sub-strates, and the data base includes information for identifi-cation of 16 Candida spp. or biotypes, 8 Cryptococcus spp.,and 11 genera (representing 16 species) of yeasts or yeastlikeorganisms in 4 h.The WalkAway-96 and WalkAway-40 are composed of an

incubator, ultrasonic humidifier, carousel holding towers,bar code reader, spectrophotometer, fluorometric reader,reagent-dispensing subsystem, panel-accessing mechanism,and computer for panel scheduling and tracking. The auto-SCAN-4 is a colorimetric optical system that performssimilarly to the spectrophotometer in the WalkAway-96 andWalkAway-40. Panels are processed one at a time afteroff-line incubation. The technologist communicates withthese systems from the keyboard and monitor of an IBMPersonal Computer AT or IBM Personal System/2 com-puter, model 60 or 80, via MicroScan Data ManagementSystem (DMS) software. The DMS produces chartable re-ports, epidemiologic reports, work logs, and antibiograms.The results can be displayed on a monitor or printed. APharm-Link program, which allows the pharmacy to interactwith the MicroScan system, can be added. The DMS willsupport two MicroScan instruments and multiple worksta-tions. One- or two-way interfaces with other computersystems are available.

The inoculum for the AIP must be prepared from nonse-lective media, whereas the inoculum for all other MicroScanidentification panels can be prepared from selective media.Media containing blood, yeast extract-malt extract agar, andinhibitory mold agar should not be used for yeasts. Severalmorphologically similar colonies are picked and suspendedin either sterile distilled water (GNB, GPB, AIP, and YIP),sterile 0.4% saline (R-GNB and R-GPB), or MicroScanHNID Inoculum Broth (HNID). The suspension is adjustedto the equivalent of a no. 0.5 McFarland turbidity standard(GNB, R-GNB, GPB, and R-GPB), a no. 3 McFarlandstandard (HNID and AIP), or a no. 5 McFarland standard(YIP). The inoculum is further diluted in sterile water for theGNB and GPB and in MicroScan Inoculum Broth for theR-GNB and R-GPB. The inoculum is poured into a seedtrough and covered with a transfer lid. The RENOK rehy-drator/inoculator (Baxter Diagnostics, Inc., MicroScan Di-vision), a manual pipettor that simultaneously rehydratesand inoculates MicroScan conventional panels or designatedrapid panels, is attached to the transfer lid. A lever is liftedon the RENOK, creating a vacuum; as a result, inoculum isdrawn into the inoculator transfer lid. The inoculum-filledtransfer lid is then positioned on the GNB, R-GNB, GPR, orR-GPR, and with the depression of a release button on theRENOK, 115 + 10 ,ul of inoculum is dispensed into eachmicrowell. For the HNID, AIP, and YIP, each microwell ismanually filled with 50 ,ul of inoculum with a manualpipetting device.

Before incubation of the panels, a mineral oil overlay mustbe added to the lysine, omithine, and decarboxylase basewells of the R-GNB; to the glucose, urea, H2S, lysine,arginine, ornithine, and decarboxylase base wells of theGNB; to the arginine and urea wells of the GPB; to the ureaand indole wells of the AIP; and to the urea well of theHNID. A sealing strip is placed over the citrate, malonate,o-nitrophenyl-3-D-galactopyranoside, tartrate, acetamide,cetrimide, oxidation-fermentation glucose, oxidation-fer-mentation base, and decarboxylase base wells when theGNB is used with oxidase-positive organisms.A bar code label for patient identification is printed by the

DMS and attached to the side of the panel by the operator.The WalkAway-96 will process up to 96 panels, and theWalkAway-40 will process up to 40 panels. Any combinationof fluorogenic or conventional panels can be randomlyloaded into the tower shelves of the carousel. After theincubator door is closed, the bar code reader scans eachpanel in the tower and the computer then schedules baselinereadings, incubation, reagent addition when required, andreadings for each panel. Currently, up to 12 reagents can bedispensed. For the fluorogenic reader, the light source is aquartz-halogen incandescent bulb filtered so that UV light of370 nm is focused into each of the 96 wells. Any freefluorophore present in the wells will then emit light at 450nm. Nonfluorophore wavelengths are filtered from this light,and the light is focused onto a detector. Normal instrumentvariations in panel readings are compensated for by afluorescing reference disk. When conventional panels areread on the WalkAway systems, a tungsten-halogen lightsource passes through interference filters on a color wheeland is focused on 97 optical fibers, 96 of which will indepen-dently illuminate a separate well in the 96-well panel. The97th photometer yields a baseline reading with which allother photometer readings are compared. A color wheelrotates during each read cycle and, depending on the optimallight source for each substrate, provides a reading at one ofsix different wavelengths. The data are transferred to the

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IBM computer, where the results are converted to a biotypenumber and compared with probabilities in the DMS database. All possible identifications are printed in descendingorder from the highest probability to a cumulative total of99.9%.The original automated reader for MicroScan, the auto

SCAN-3, was studied by Woolfrey et al. (126), who used 678clinical isolates of members of the family Enterobac-teriaceae that had been identified by the API 20E andconventional biochemical tests. The autoSCAN-3 correctlyidentified 95.1% at the genus level and 94.9% at the specieslevel versus 97.9% at the genus level by visual interpretation(P < 0.01). Organisms most frequently misidentified at thegenus level were E. coli (5 of 141 isolates), Enterobactercloacae (6 of 53 isolates), and Shigella spp. (15 of 42isolates). Of 42 Shigella strains, 97.9% were correctly iden-tified by visual interpretation of the panels compared with64% by the autoSCAN-3. Overall, approximately one-thirdof the panels had to be visually inspected for at least one

reaction as a result of equivocation by the autoSCAN-3reader. The biochemicals that required visual interpretationwere randomly distributed, thus indicating the need forimproved discrimination by the autoSCAN-3 reader. Inaddition, 37 isolates were designated as very rare biotypesby the autoSCAN-3 and required visual interpretation of theplate. This resulted in correct identification of 27 of theisolates as Serratia marcescens and 1 as Proteus mirabilis.Of the remaining nine isolates, four were correctly identified(three Enterobacter cloacae and one Serratia marcescens)when the telephone computer service was contacted.Rhoden et al. (99) evaluated a production model of the

autoSCAN-4 with typical and atypical strains of members ofthe family Enterobacteriaceae, nonfermenters, and nonen-

teric fermenters, that had been identified at the Centers forDisease Control by conventional methods. These MicroScantest trays required freezer storage. The autoSCAN-4 identi-fied 95.4% of the members of the Enterobacteriaceae (N =

307), 96.6% of the nonenteric fermenters (N = 29), and94.2% of the nonfermenters (N = 69). Visual interpretationof the trays yielded essentially the same results. Additionaltests were required for as few as 5.2% of the members of theEnterobacteriaceae and as many as 51.8% of the nonentericfermenters. "Very rare biotype" was printed by the auto

SCAN-4 for only four strains of the Enterobacteriaceae andthree nonfermenters. Three of six Edwardsiella tarda iso-lates were misidentified because weakly positive H2S tests

were read as negative. In several instances, the autoSCAN-4reader did not detect a weakly positive arginine test. Theauthors concluded that the autoSCAN-4 was a highly effi-cient, accurate, and reliable system for identification ofgram-negative bacilli.

Gavini et al. (42) evaluated the autoSCAN-4 for identifi-cation of 372 clinical isolates of members of the familyEnterobacteriaceae with a dry panel and a new formulation.Conventional tests were used for reference identification.The autoSCAN-4 correctly identified 95.4% of the isolates at

the species level and 98.4% at the genus level. Of the 17

isolates not identified at the species level, 16 required

additional tests. A correct identification at the genus level

was obtained in 13 of these cases. Only one isolate was

misidentified, an Enterobacter agglomerans isolate that was

identified as enteric group 41.Pfaller et al. (95) reported on the WalkAway-96 R-GNB

for identification of 292 members of the family Enterobac-

teriaceae and 91 nonenteric bacilli. The API 20E and con-

ventional biochemical tests were used for reference identifi-

cation. The R-GNB identified 86.3% of the members of theEnterobacteriaceae and 92.3% of the nonenteric bacilli.Overall, 87.7% of all isolates were correctly identified, witha likelihood of 285%. An additional 29 isolates (7.6%)yielded a correct identification but with a likelihood of <85%(overall identification, 95.3%). They found that 8.9% of themembers of the Enterobacteriaceae and 7.6% of all isolateswould require supplemental tests for correct identification.Of 17 misidentifications, 12 were at the genus level and 5were at the species level. In one instance no identificationwas given even though that organism was in the data base.Enterobacter spp. and members of the tribe Proteeae re-quired supplemental testing most frequently. A common setof 134 isolates was tested in the two participating laborato-ries to determine interlaboratory agreement. The overallagreement was 86%.Tenover et al. (117) tested 310 well-characterized non-

glucose-fermenting gram-negative bacilli on the WalkAway-96, using both the GNB and R-GNB. The GNB reported41.3% correct at285% probability, 21.3% correct with lowprobability, 22.4% incorrect, and 15.0% unidentified. TheR-GNB reported 50.1% correct at .85% probability, 40.5%correct with low probability, and 9.3% incorrect. Alcali-genes xylosoxidans subsp. xylosoxidans, P. putida, P. fluo-rescens, and X. maltophilia accounted for 57.8% of themisidentifications with the colorimetric panel, and P. fluo-rescens accounted for 28% of the misidentifications with therapid fluorometric panel. Supplemental tests were requiredfor 36.3% of the isolates tested with the GNB and 40.5%tested with the R-GNB. With the R-GNB, supplementaltests could be set up after the interpretation at 2 h. Only13.5% of the GNB tests could be interpreted at 18 h, with theremainder requiring 42 h of incubation. When the identifica-tions were recalculated with an updated R-GNB data baseand revised software, 77.1% were correctly identified at>85% probability and 8.1% were misidentified at this sameprobability.

Published abstracts regarding the WalkAway-96 R-GNBhave reported an identification accuracy that has varied from66.4 to 97.8% (24, 30, 32, 47, 57, 59, 70, 75, 76, 87, 118).Organisms reported in some studies to be frequently misi-dentified or to yield inconclusive results included K pneu-moniae, K oxytoca, Enterobacter cloacae, C. freundii,Serratia marcescens, Proteus mirabilis, M. morganii, Shi-gella spp., and P. fluorescens (30, 32, 59, 75, 118). One studyreported that 21% of the P. aeruginosa isolates tested in theWalkAway-96 failed to grow and could not be identified (59).The original MicroScan GPB panel for identification of

gram-positive bacteria was supplied and stored frozen andcontained 27 tests. Tritz et al. (119) evaluated the GPBfrozen panel for identification of 100 Enterococcus strains.Conventional methods were used for identification of entero-cocci and were based on reactions listed by Facklam andCollins (37). Sixty isolates of streptococci, identified byconventional methods, were also evaluated. The GPB testswere read on the autoSCAN-4, and 94 enterococci (77Enterococcus faecalis, 14 Enterococcus faecium, and 3Enterococcus durans strains) were correctly identified. Ofthe remaining six strains, four were identified as Enterococ-cus faecalis by GPB and as Enterococcus solitarius by theconventional method and two were identified as Enterococ-cus durans by GPB and as Enterococcus hirae by theconventional method. The GPB did not identify any of the 60Streptococcus spp. as Enterococcus spp. Hussain et al. (52)determined the accuracy of the GPB for identification of 175clinical isolates of coagulase-negative staphylococci. The



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GPB results were compared with those of the API Staph-Ident system, and discrepancies were resolved by the con-ventional method of Kloos and Schleifer (64). The GPB uses18 of the 27 substrates for identification of Staphylococcusspp. The GPB was interpreted both visually and by theautoSCAN-4. After overnight incubation, only 36% of theidentifications were complete; the remaining isolates re-quired an additional 24 h of incubation. The GPB correctlyidentified 83.4% of the coagulase-negative staphylococci,with 97% of S. epidermidis and S. saprophyticus isolatesyielding an excellent identification. An acceptable identifi-cation (>80%) was obtained for most S. haemolyticus, S.simulans, Staphylococcus capitis, Staphylococcus cohnii,and S. xylosus isolates, but only 35.2% of Staphylococcushominis, 69.5% of Staphylococcus warnei, and 60% ofStaphylococcus sciun isolates were properly identified. Sup-plemental tests were not required with the GPB. The onlyreading errors by the autoSCAN-4, compared with visualreadings, were observed with bacitracin, crystal violet, ornovobiocin susceptibility and were due to air bubbles orscratches on the plastic well. The GPB biochemical tests thatdiffered from conventional tests included the pyrrolidonyl-3-naphthylamide, ,-D-galactopyranosidase, lactose, and

urease tests (false-negative reactions) and the arginine, man-nose, novobiocin, and lactose tests (false-positive reac-tions).Kloos and George (62) compared the GPB and R-GPB

tests for the identification of 25 different Staphylococcusspp. The 920 strains tested were obtained from reference andtype collections, clinical specimens, and the skin of humansand animals. The nonreference strains were identified by thecharacteristics and methods described by Kloos and Lambe(63), and selected strains from each species were verified viaDNA-DNA hybridization with reference or type strains. TheGPB was read visually at 15 to 48 h, and the R-GPB was readby the WalkAway-96 at 2 h. Both systems correctly identi-fied 290% of strains of Staphylococcus arlettae, S. aureus,Staphylococcus auricularis, S. capitis subsp. capitis, Staph-ylococcus camosus, S. cohnii subsp. cohnii, Staphylococcuslentus, S. saprophyticus, and S. sciuri. In addition, theR-GPB correctly identified >90% of strains of Staphylococ-cus caprae, Staphylococcus caseolyticus, S. epidermidis,Staphylococcus kloosii, S. warneri subsp. 2, and S. xylosus.Both systems correctly identified between 80 and 90% ofstrains of Staphylococcus equorum, S. haemolyticus subsp.1, Staphylococcus hyicus, and Staphylococcus intermedius.The R-GPB also correctly identified between 80 and 89% ofstrains of S. capitis subsp. ureolyticus, S. cohnii subsp.urealyticum, S. hominis, S. simulans, and S. wameri subsp.1. Both systems identified 50 to 75% of strains of S.chromogenes, Staphylococcus gallinarum, Staphylococcuslugdunensis, and Staphylococcus schleiferi.

Stoakes et al. (114) evaluated the WalkAway-96 R-GPBwith 239 strains of staphylococci belonging to 17 species.Ten or more strains of commonly occurring clinical strainswere tested. Conventional methods were used to establishthe true identity of the strains. Of the tested strains, 219(91.6%) were correctly identified without additional tests, 9(3.8%) were correctly identified after additional tests, 6(2.5%) were incorrectly identified, and 5 (2.1%) were classi-fied as rare biotypes and not identified. The misidentifiedstains (all considered common isolates), were S. capitis (2 of21 strains), S. hominis (2 of 20 strains), S. wameri (1 of 25strains), and S. xylosus (1 of 12 strains). One discrepantreaction (positive in each instance) was responsible for eachmisidentification. Of the 37 (representing 7 species) less

commonly isolated Staphylococcus strains, none were incor-rectly identified and only 1 (Staphylococcus lugdunensis)required additional tests for correct identification.Godsey et al. (43) evaluated the WalkAway-96 R-GPS for

identification of 607 staphylococci, 704 streptococci, 42Micrococcus isolates, 28 Aerococcus isolates, and 30 L.monocytogenes isolates. The accuracy of identificationranged from 79% (S. warnen) to 100% (multiple species).The overall identification accuracy was 95.7%.

Stoakes et al. (113) evaluated the AIP both by visualinterpretation and with the autoSCAN-4. The results werecompared with those obtained by the Virginia PolytechnicInstitute conventional method. A total of 237 anaerobeswere tested, of which 166 (70%) were correctly identified tospecies level (probability .85%) by visual readings versus157 (66.2%) correctly identified by the autoSCAN-4 (P >0.30). When supplemental tests were performed, 80.1 and76.7%, respectively, were correctly identified (P > 0.30).The visual and automated interpretations produced 94 dis-crepant reactions, including 37 with gram-negative bacilli, 48with clostridia, and 9 with other organisms. The autoSCAN-4 could not interpret a reaction in 46 instances,whereas 93 reactions were difficult to interpret visually.Difficulties in interpretation of reactions were randomlydistributed with the autoSCAN-4 but were frequent forL-lysine-,3-naphthylamide reactions with Bacteroidesfragilisand for bis-p-nitrophenyl phosphate reactions with gram-positive organisms when read visually. To determine thereproducibility of the system, 50 strains (30 gram-negativebacilli and 20 clostridia) representing 1,200 reactions weretested in duplicate. Discrepant results were 69 (5.8%) withthe autoSCAN-4 and 57 (4.8%) with visual readings (P >0.20). As a result, 8% of the strains were changed from acorrect to an incorrect identification with the autoSCAN-4and 6% with visual readings.Land et al. (67) evaluated the YIP with 437 recent clinical

yeast isolates, using the API 20C as the reference method.However, the MicroScan automated instruments were notused and only visual readings were obtained. The YIPidentified 85% of all isolates and 92% of the taxa included inthe data base. The YIP identified 94% of rapidly growingyeasts in the genera Candida, Hansenula, Pichia, Rhodo-torula, Saccharomyces, and Torulopsis (98% when organ-isms and biocodes not in the data base were excluded).However, only 65% of identifications of slower-growingyeasts (Blastoschizomyces, Cryptococcus, Geotnichum, Hy-phopichia, Phaeococcomyces, Prototheca, and Trichos-poron species) correlated with results of the API 20C. Whenunrecognized organisms and biocodes were excluded, thecorrelation improved only to 68%. The yeasts that posed thegreatest problem for the YIP were Torulopsis beigelii, Blas-toschizomyces capitatus, Geotrichum spp., and Cryptococ-cus neoformans. The YIP correctly identified 40 (67%) of 60known serotypes of Cryptococcus neofornans. One strain ofserotype A was incorrectly identified, and 19 isolates (3 of 14serotype A, 3 of 15 serotype B, 2 of 15 serotype C, and 11 of15 serotype D isolates) yielded biocodes not found in thedata base. The authors concluded that the expansion of theYIP data base and adjustment of the indoxyl phosphatasesand pH indicators for sucroses 1 and 2 and trehalose shouldmake the YIP an excellent system for identification ofmedically important yeasts.

St.-Germain and Beauchesne (112) reported on a modifiedversion of the MicroScan yeast identification system. Foursubstrates (isoleucine, urea, N-acetylgalactosamine, andtrehalose) had been reformulated and the data base regener-

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ated for Version 17 software. The authors evaluated the YIPfor identification of 357 yeastlike clinical isolates of 11genera and 30 species. Of these, 217 were common isolatesand 140 were relatively uncommon. The YIP results wereread both visually, by two observers, and with the autoSCAN-4. The API 20C and morphological characterizationon cornmeal-Tween 80 agar were the reference systems.Conventional tests were used to resolve any discrepanciesbetween the API 20C and the YIP. Both the YIP, asinterpreted by the autoSCAN-4, and the API 20C identified278 (78%) of 357 strains without supplemental tests. Withsupplemental tests, the YIP correctly identified 345 strains,incorrectly identified 10 strains, and did not identify 2strains, for an overall accuracy of 96.6%. It accuratelyidentified 99.5% of the common strains and 92.1% of the lesscommon strains. Of the common strains, the autoSCAN-4incorrectly identified only one strain of C. glabrata (99.5%accuracy), whereas none were incorrectly identified whenthe panels were visually interpreted. Of the less commonstrains, the autoSCAN-4 incorrectly identified nine strainsand failed to identify two strains (92.1% accuracy). On visualinterpretation 12 strains were incorrectly identified and 2 or3 strains were not identified (89.3% accuracy). There was anoverall accuracy of 96.6% for the autoSCAN-4 versus 95.8%for visually interpreted panels (P > 0.50). Identical profilenumbers were obtained by both observers and the autoSCAN-4 for 44% of the 357 strains. Difficulties with theinterpretation of chromogenic substrates, particularly,-naphthylamide substrates and nitrophenyl-linked sub-strates, resulted in discrepancies in profile numbers betweenthe two observers and the autoSCAN-4.

Abstracts regarding the YIP have recently been published.Simmonds et al. (107) reported on the YIP for identificationof 232 clinical yeast isolates from 10 genera and 29 species.The API 20C was used as the standard method, and discrep-ancies were resolved with conventional assimilation andfermentation tests. The overall accuracy of the YIP was 59%when interpreted by the autoSCAN-4 and 72% when inter-preted visually (P < 0.01). When biotypes not found in thedata base were excluded, the accuracy was 62% by autoSCAN-4 and 76% by visual interpretation (P < 0.01). Mor-phological observations were needed to identify more than50% of the isolates, and confirmatory biochemical tests wererequired for identification of 29% of the isolates by theautoSCAN-4 versus 16% by visual interpretation. Belcher etal. (14) tested the YIP with 189 clinical yeast isolates from 7genera and 20 species. The Minitek Yeast Carbon Assimila-tion procedure (Becton-Dickinson) was the standard forcomparison. The autoSCAN-4 correctly identified 79% ofthe isolates, whereas visual interpretation correctly identi-fied 86% (P > 0.05). No supplemental morphological orphysiologic tests were performed. Stockman and Roberts(115) tested the YIP with 260 yeast isolates from 24 species.The API 20C was the reference method. It was not statedwhether readings were obtained by an automated system orby visual inspection. The YIP correctly identified 75.3% ofthe isolates with a probability of >85%. Supplemental testswere required for 21% of the isolates. Two C. famata, twoCandida zeylanoides, and one each of C. glabrata, C.lipolytica, C. parapsilosis, C. tropicalis, Kluveromyces lac-tis, S. cerevisiae, and Sporobolomyces isolates were misi-dentified by the YIP.

In summary, the most recent study of the autoSCAN-4GNB correctly identified 95.4% of tested members of thefamily Enterobacteriaceae to the species level (42). Pfaller etal. (95) reported that the WalkAway-96 R-GNB correctly

identified 86.3% of members of the family Enterobac-teriaceae and 92.3% of gram-negative nonenteric bacillitested. Supplemental tests were required for 7.6% of theisolates for correct identification. Tenover et al. (117) foundthat the WalkAway-96 GNB and R-GNB correctly identified62.6 and 90.6%, respectively, of the gram-negative nonen-teric bacilli tested. However, approximately 35 and 45%,respectively, of the correct identifications with the GNB andR-GNB had a probability of less than 85%. In addition,approximately 40% of the isolates required supplementaltests for correct identification by both the GNB and R-GNB.Only 13.5% of the GNB results could be interpreted at 18 h,with the remainder requiring 42 h of incubation. A. xylosox-idans subsp. xylosoxidans, P. putida, P. fluorescens, and X.maltophilia were most frequently misidentified by the GNB,whereas P. fluorescens accounted for 28% of the misidenti-fications by the R-GNB. Published abstracts concerning theWalkAway-96 R-GNB have reported an identification accu-racy that has ranged from 66.4 to 97.8% (24, 30, 32, 47, 57,59, 70, 75, 76, 87, 118). There have been no studies of theautoSCAN-4 involving the GPB panel with dried substrates.Kloos and George (62) found that the WalkAway-96 R-GPBcorrectly identified common Staphylococcus spp. and manyuncommon Staphylococcus spp. at a probability of >85%.Stoakes et al. (114) found that the WalkAway-96 R-GPBcorrectly identified 91.6% of Staphylococcus spp. testedwithout supplemental tests and 95.4% with required supple-mental tests. None of the less commonly isolated Staphylo-coccus spp. (37 of 239 strains) were incorrectly identified.Godsey et al. (43) tested 1,411 gram-positive bacteria, in-cluding staphylococci, streptococci, Micrococcus isolates.Aerococcus isolates, and L. monocytogenes, with the Walk-Away-96 R-GPB and found an overall identification accu-racy of 95.7%. The only evaluation of the AIP was with theautoSCAN-4 (113). When required supplemental tests wereperformed, the autoSCAN-4 AIP correctly identified 76.7%of the anaerobes tested (N = 237). Results for correctidentification with the autoSCAN-4 compared favorablywith visual interpretation of the same panels (P > 0.30).St.-Germain and Beauchesne (112) reported on a recentmodified version of the autoSCAN-4 YIP and found that99.5% of the common yeast isolates and 92.1% of the lesscommon yeast isolates tested were correctly identified whenthe required supplemental tests were performed. There wasan overall accuracy of 96.6% for the autoSCAN-4 versus95.8% when the same panels were visually interpreted (P >0.50). Recent abstracts on the autoSCAN-4 YIP have re-ported that 62 and 79% of the yeasts tested were correctlyidentified (14, 107). There have been no published studies ofthe WalkAway-96 YIP.The MicroScan automated systems will identify a wide

variety of organisms. However, further improvements in thedata base and software for all panel types are needed. Anautomated method for the addition of mineral oil to thevarious panels and for inoculation of the HNID, AIP, andYIP would enhance work flow.There have been no evaluations of the HNID panel and

GPB dried-substrate panels by using MicroScans automatedsystems. Evaluation of the WalkAway-40 and further eval-uation of the WalkAway-96 and autoSCAN-4 with appro-priate MicroScan panel types will be required as MicroScan continues to update the data bases and softwareprograms.



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ALADIN AND AUTOREADER

The Automated Laboratory Diagnostic Instrument (ALA-DIN; Analytab Products) is a computer-assisted system withon-line incubation that interprets biochemical and suscepti-bility tests by a video image process. Analytab Products alsomanufactures the UniScept system AutoReader, which teststhe same panels as the ALADIN but requires off-line incu-bation and which performs automatic interpretation of re-sults through photometric evaluation of test wells at multiplewavelengths. The ALADIN and UniScept systems performboth MIC and qualitative susceptibility tests on aerobicgram-negative bacilli and gram-positive bacteria. Both sys-tems identify gram-negative bacteria (UniScept 20E), gram-positive bacteria (UniScept 20GP), and anaerobes (AN-Ident). Other Analytab Products identification panels can beused with either system, but manual entry of the reactions isrequired. These panels identify gram-negative bacteria (Rap-id-E and Rapid NFT), Staphylococcus spp. (Staph-Ident),and yeasts (Yeast-Ident).The UniScept 20E has 20 modified conventional sub-

strates, and the data base includes information for identifi-cation of 55 species of the family Enterobacteriaceae and 50groups, genera, or species of nonfermentative, oxidase-positive, gram-negative bacilli in 18 to 24 h. The UniScept20GP has four chromogenic, one ,B-naphthol-labeled, one0-naphthylamide-labeled, and 14 modified conventional sub-strates, and the data base includes information for identifi-cation of 13 Staphylococcus spp., 3 Enterococcus spp., and2 Streptococcus spp. (group D, nonenterococcus) in 18 to 24h. The AN-Ident has nine chromogenic substrates whosereactions are detected by the liberation of indoxyl, o-nitro-phenol, or p-nitrophenol from the substrates, nine 3-naph-thylamide-labeled substrates, one modified conventionalsubstrate, and a test for catalase. The AN-Ident data baseincludes information for identification of 83 groups, genera,or species of anaerobic bacteria in 4 h.The ALADIN system is composed of an incubator, eleva-

tor, reagent-dispensing station, grabber arms, image proces-sor, and disposal station. A separate workstation interactswith the ALADIN and is composed of a keyboard, computer(Compaq 386 Sx model 40), UniScept deziner-er Software,and Okidata 320 printer. The software contains the data basefor all UniScept products, susceptibility programs, patientdemographics, storage of data, and antibiograms. A bidirec-tional mainframe computer interface that allows up-load ofdaily test results and down-load of demographics may bepurchased.

Inocula for the UniScept 20E and UniScept 20GP can begrown on either selective or nonselective agar media,whereas inocula for the AN-Ident must be grown on nonse-lective agar media (excluding 5% sheep blood Trypticase soyagar [TSA] or Schaedler's blood agar). To prepare inocula,several morphologically similar colonies are selected andsuspended in sterile 0.85% saline (UniScept 20E and UniScept 20GP) or sterile distilled water (UniScept 20E, UniScept 20GP, and AN-Ident). The suspension is adjusted tothe equivalent of a no. 0.5 McFarland turbidity standard(UniScept 20E and UniScept 20GP) or a no. 5 McFarlandstandard (AN-Ident). The inocula for the UniScept 20E andUniScept 20GP are diluted 1:100 in sterile 0.85% saline. Witheither the UniScept Autoinoculator or the manual pipettingdevice, 100 ,ul of inoculum is dispensed into each micro-tube of the UniScept 20E (300 RI into the citrate, Voges-Proskauer, and gelatin microtubes) and UniScept 20GP. Forthe AN-Ident, each microtube is manually filled with 2 drops

(approximately 85 i±l) of the suspension. When the UniScept20E is to be incubated off-line and then read on the UniSceptAutoReader, the cupule section of the arginine, lysine,ornithine, urea, and H2S tubes must be manually overlaidwith mineral oil.The ALADIN has a 60-specimen-capacity incubator, with

up to two UniScept panels (identification and/or susceptibil-ity) being processed per specimen. This allows the process-ing of up to 120 individual panels. A virtually unlimitednumber of panels may be incubated off-line for reading onthe ALADIN. One or two UniScept panels per specimen areplaced in a universal carrier, which is a molded plastic frameapproximately 5 by 85/8 in. (ca. 13 by 22 cm) in size. Thepanels are inoculated, and the universal carrier is placed into1 of 60 test slots (two rows of 30) in the incubator. Theelevator and grabber arms automatically transfer the panelsto the read station. The specimen number and panel type areinterpreted by video image processing. This activates theappropriate incubation cycle and reagent addition for eachpanel. After appropriate incubation, panels are transferredto the reagent-dispensing station for addition of reagents;after further appropriate incubation, they are returned to thereader for a final examination. Currently, up to nine reagentscan be dispensed. Each microtube is examined by a black-and-white video image processor through one to four coloredfilters selected by the computer, depending on which reac-tion is being analyzed. The video imaging camera isolates anarea of interest for each microtube and uses 200 to 300picture elements, or pixels, to view this area. For example,fermentation reactions are read at the middle or bottom ofthe microtube. The density of each pixel perceived by thecamera is a specific voltage that is represented digitally forcomputer processing. These specific results are convertedinto plus or minus reactions, and the computer generates aseven-digit profile number. This profile number, or "bio-type," identifies the most probable organisms of each bio-type. The panels are then transferred to the disposal station,where they are discarded into a waste bag by miniatureforklifts.

Panels incubated off-line are placed in a tray and read onthe UniScept AutoReader or the ALADIN. For the UniScept AutoReader, a photometer examines each microwellat multiple positions and wavelengths. A photodiode detectsthe transmitted light, and the resulting voltage is convertedto an optical density value, which is equated to a predeter-mined positive or negative reaction. The resulting biochem-ical profile is compared with the data base for identification.Shulman et al. (105) reported on the ability of the ALA-

DIN to read UniScept 20E panels. A total of 300 isolates (144E. coli, 45 Proteus/Providencia spp., 27 KiebsiellalEntero-bacter spp., 18 Serratia spp., 12 Pseudomonas spp., and 54other organisms) were tested. On a test-for-test basis, theALADIN and visual readings of 300 organisms showed a99.0% level of correlation. These differences in readings didnot affect the final identification of any of the tested organ-isms. The reference system to establish the true identity ofthe organisms and the percentage of organisms correctlyidentified was not mentioned.Navarro et al. (86) reported on the ALADIN for its ability

to read AN-Ident panels. A total of 125 anaerobes (48Clostridium spp., 37 Bacteroides spp., 15 Fusobacteriumspp., 9Actinomyces spp., 5 Propionibacterium spp., and 11other organisms) were tested. With respect to visual read-ings of the 125 organisms, the ALADIN showed a 96.1%level of correlation on a test-for-test basis. The abstract didnot mention whether these differences in readings affected

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the final identification of any of the tested organisms by theALADIN. Also, the reference system to establish the trueidentity of the organisms and the percentage of organismscorrectly identified were not mentioned.The only extensive report on the ALADIN identification

system was a collaborative study at three medical centers(28). In this study, 318 aerobic and facultatively anaerobicgram-negative bacteria and 148 obligately anaerobic isolateswere tested. All bacteria were identified by establishedLonventional methods. The UniScept 20E and AN-Identbiochemical reactions were interpreted on the ALADIN byvideo imaging. Then the panels were visually interpreted.The results were compared on a test-for-test basis, with thevisual interpretation serving as the reference. When two ormore discrepant biochemical test results per test systemwere obtained, the isolate was retested. The results of theretest were considered final. Altogether, 6,360 individualtests were performed on the 318 gram-negative bacteria,with an overall agreement of 96.5%. False-negative readingsfor indole production, mannitol fermentation, and tryp-tophan deaminase produced =90% agreement. The authorssuggested that the computer algorithms for these substrates,all carbohydrates, and the o-nitrophenyl-3-D-galactoside re-action be adjusted.With anaerobic test results, the overall agreement be-

tween the ALADIN and visual interpretation was 95.2%.False-positive results with indoxylacetate yielded an 81.1%agreement for this substrate. False-negative results, partic-ularly with carbohydrate and some ,B-naphthylamide deriv-ative tests, were noted. It was suggested that the indole testmight be improved by the substitution of dimethylaminocin-namaldehyde for p-dimethylaminobenzaldehyde. This hasnow been incorporated into the ALADIN.

Intralaboratory and interlaboratory reproducibility withthe ALADIN biochemical profiles averaged 96.0 and 91.5%,respectively, in the three laboratories. Similar values wereobtained for visually determined results.The report of D'Amato et al. (28) verified that there was

good agreement between video image and visual interpreta-tion of biochemical reactions for the UniScept 20E andAN-Ident, but it did not report the effect of false-positive orfalse-negative results by video image on identification of theorganisms tested.There are some limitations to the use of the UniScept 20E

and the ALADIN. With the UniScept 20E, some slow-growing members of the family Enterobacteriaceae andgram-negative organisms that are not members of the En-terobacteriaceae require supplemental tests (oxidase, oxida-tion-fermentation glucose medium, or motility medium) andincubation for 36 to 48 h. The ALADIN will not identifythese organisms, since reagents are added and panels areread at 24 h. These panels are automatically returned to theincubator for removal and off-line incubation.The only study of the UniScept system AutoReader was

reported by O'Hara et al. (90). They compared the agree-ment of automated and visual readings of biochemical testswith the results of the UniScept 20E. They tested 291oxidase-negative and 49 oxidase-positive glucose-fermentingisolates. Since the AutoReader is not designed to read aUniScept 20E at 48 h, nonfermentative bacteria were notevaluated. Of the 6,800 tests compared, only 45 readings(0.7%) did not match. Discrepant results involved 16 bio-chemicals, with indole and citrate disagreeing most often (13of 45).The ALADIN and UniScept system AutoReader identify

a limited number of gram-positive bacteria. Although the

organisms that can be identified by the UniScept 20GPrepresent most of the gram-positive bacteria routinely en-countered in the clinical laboratory, it is desirable to have anautomated system that could identify a broader range ofsignificant gram-positive bacteria. The only rapid (4-h) panelthat can be automatically read by the ALADIN and UniScept system AutoReader is the AN-Ident. Automated read-ing of other rapid Analytab Products identification panelswould enhance the usefulness of these systems.

It is commonly accepted that the UniScept 20E, UniScept20GP, and AN-Ident have excellent data bases for identifi-cation of bacteria. However, there are no reported studieswith either the ALADIN or UniScept system AutoReaderand the UniScept 20GP. The studies by D'Amato et al. (28)and O'Hara et al. (90) indirectly suggest that the ALADINand UniScept system AutoReader, respectively, will reliablyidentify isolates with either the UniScept 20E or the AN-Ident. However, studies to prove the accuracy of identifica-tion of the UniScept 20E, UniScept 20GP, and AN-Identwhen tested on these systems have not been reported.

BIOLOG

The Biolog system (Biolog, Inc., Hayward, Calif.) wasintroduced in 1989 for identification of aerobic gram-negativebacteria (enteric bacilli, nonfermenters, and fastidious spe-cies) by determination of carbon source utilization profiles.Recently, Biolog has added the capability to identify a broadrange (cocci, bacilli, and spore-forming bacilli) of aerobicgram-positive bacteria. The Biolog GN MicroPlate (for iden-tification of gram-negative bacteria) and the Biolog GPMicroPlate (for identification of gram-positive bacteria) are96-well dehydrated panels containing tetrazolium violet, abuffered nutrient medium, and a different carbon source foreach well except the control, which does not contain acarbon source. The microwells are rehydrated with a cellsuspension and read at either 4 h or overnight (16 to 24 h) forthe ability of the bacteria to utilize the carbon source.Tetrazolium violet is a redox dye used to detect electronsdonated by NADH to the electron transport system. Re-duced tetrazolium violet is a purple formazan. When acarbon source is not used, the microwell remains colorless,as does the control well. The resulting pattern of purple wellsyields a "metabolic fingerprint" of the bacterium tested (15,16).For metabolic capability studies, the MT MicroPlate con-

tains only tetrazolium and a buffered nutrient medium with-out a carbon source in any of the 96 wells. The user can addvarious carbon sources to the microplate. The ES Mi-croPlate contains 95 different carbon sources and is designedfor characterizing and/or identifying different E. coli andSalmonella strains, for mutant strain characterization or forquality control testing of E. coli K-12 or Salmonella typhi-murium LT2 strains carrying recombinant plasmids, and forepidemiological studies. Although there is no Biolog database for the MT MicroPlate and ES MicroPlate, a customdata base can be created by using Biolog software. Biolog isdeveloping the capacity for identification of yeasts, anaer-obes, and additional environmental and oligotrophic bacte-ria.The 95 substrates contained in the Biolog GN MicroPlate

and the Biolog GP MicroPlate include carbohydrates, car-boxylic acids, amides, esters, amino acids, peptides, amines,alcohols, aromatic chemicals, halogenated chemicals, phos-phorus- and sulfur-containing chemicals, and polymericchemicals. The Biolog system data base includes informa-



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tion for identification of 569 species or groups of aerobicgram-negative bacteria and 225 species or groups of gram-positive bacteria and encompasses almost all known humanpathogens and most important environmental species.The Biolog automated system consists of a manual eight-

channel repeating pipettor, a turbidimeter, a MicroPlateReader, a MicroLog Program Disk, and any DOS-basedIBM-compatible PC, including XT and AT 286, 386, and 486models. A manual system without the MicroPlate Reader isalso available. The computer must have a hard drive of atleast 20 MB, at least one floppy drive (3.5 or 5.25 in. [ca. 8.9or 13.3 cm]), and, when the automated reader is used, anenhanced 101 keyboard. The software runs on systems withVGA or EGA color graphics or with monochrome displays.Different software versions can be purchased, either formanual entry or for automated reading of the test results.The latter software version allows data to be printed andsaved in computer files, which can be utilized by user-provided software routines. Alternatively, the data can befiled in a "user data base" in which the reaction patterns arepermanently saved. An unknown biochemical profile can becompared with the Biolog GN or Biolog GP data base, theuser data base, or a combination of the two. Other featuresof the software include on-line information about any speciesin the library, cluster analysis programs in the form ofdendrograms and two-dimensional and three-dimensionalplots to demonstrate the relatedness of strains or species,and the separation of the GN data base into clinical andenvironmental versions.The inoculum for gram-negative organisms is prepared

from TSA or TSA-blood agar medium. The inoculum forgram-positive clinical and food isolates, with few excep-tions, must be grown on Biolog Universal Growth Mediumwith 5% sheep's blood (Biolog, Inc.). Environmental isolatestypically do not require the addition of blood. Generally,bacteria are grown for 4 to 18 h. A swab is gently rolled overthe colonies to prevent carryover of nutrients from the agarmedium into the saline (0.85%) suspension of bacteria. Thecolorimeter or spectrophotometer (optical density at 590 nmof 0) is blanked with a tube containing uninoculated saline.The bacterial suspension is adjusted to within a low-stan-dard-to-high-standard range. The inoculum should be usedwithin 10 min. The plates are inoculated with 150 ,ul per welland incubated at 30 to 35°C either with or without CO2. Theplates are read at 4 h either manually or on a computer-controlled microplate reader. The MicroLog software sub-tracts the background cell density from the negative controlwell and interprets all tests above a threshold as positive.The metabolic profile of the organism is matched to a database of patterns by using the MicroLog Software Program.Data from the MicroPlates can be permanently saved incomputer files to facilitate subsequent analyses. The auto-mated reader processes a plate in 5 s.

There have been four published studies on the use of theBiolog GN, one of which used the MicroPlate Reader.Carnahan et al. (19) tested 20 clinical strains each of Aero-monas hydrophila, Aeromonas caviae, and Aeromonas so-bria with the Biolog GN. The reference methods for identi-fication of the isolates were not stated. Isolates were grownovernight on TSA, and the Biolog GN was inoculated as

specified by the manufacturer. Inoculated plates were incu-bated at 35°C for 18 to 20 h and then read manually. For thethree species tested, nine substrates were found to yieldgood, discriminatory values. Seven of these substrates hadnot been previously identified as being useful for identifyingAeromonas isolates to the species level. All 60 Aeromonas

strains were correctly classified to species. However, Aero-monas species were not included in the Biolog data base atthat time, and this study demonstrated only thatAeromonasisolates could be identified to species with substrates presentin the Biolog GN panel.Armon et al. (4) tested the Biolog GN panel with Legion-

ella spp., also before this organism was included in theBiolog data base. They tested one strain each of Legionellapneumophila (Philadelphia 1), Legionella pneumophila (en-vironmental isolate), Legionella micdadei, Legionellaoakridgensis, Legionella longbeachae, and Legionella gor-manii. The strains were identified by reference biochemicalsbut were not serogrouped. The strains were grown for 3 daysat 35.5°C on buffered charcoal-yeast extract agar supple-mented with L-cysteine. The organisms were harvested, andthe inoculum for each strain was prepared in two differentbuffers [0.1 mol/liter of phosphate buffer (pH 7.0) and 0.05mol/liter of N-(2-acetoamido)-2-aminoethane sulfonic acid(ACES) buffer supplemented with 4 ml of 10% L-cysteine(pH 7.0) per liter]. The inoculum prepared in each buffer wasinoculated into a separate Biolog GN panel and, afterincubation, was read manually. The Legionella strains testedyielded biochemical profile variations that allowed distinc-tion between the tested isolates. With the existing data base,the authors found some overlap of biochemical profiles withMoraxella bovis. The biochemical reactions showed anenhanced color reaction when ACES buffer with L-cysteinewas used as diluent for the inoculum.

Mauchline and Keevil (73) used the Biolog system toestablish a new data base and identify asaccharolyticLegionella spp. They tested single type strains of Legionellapneumophila serogroups 1 through 14 (excluding serogroups4 and 9) and a single type strain of Legionella bozemanii,Legionella dumoffii, Legionella feeleii, Legionella hacke-liae, Legionella israelensis, Legionella rubrilucens, Legion-ella longbeachae, and Legionella micdadei. The isolateswere grown on buffered charcoal-yeast extract agar at 37°Cfor 72 h. Inocula were prepared in Page's amoebal saline,and Biolog plates were inoculated and then incubated at 37°Cin either air or a low-oxygen (=4%) atmosphere. The plateswere read at 24-h intervals up to 72 h, both visually and witha Merertech Microplate Reader (Atlas Bioscan, BognorRegis, United Kingdom). When the tested legionellae wereincubated in air, some of the reactions were not visible at 24h and the substrates required longer incubations to turnpositive. However, when the same strains were incubated ina reduced-oxygen atmosphere, definite reactions were ap-parent in 24 h. When tested under these conditions, none ofthe legionellae had a metabolic profile that closely matchedany other bacteria in the Biolog data base, indicating that theprofiles obtained were specific for Legionella species. Inaddition, the authors tested Biolog plates with environmen-tal isolates that had been provisionally identified by serologictesting as various Legionella species. They compared theresults with a combination of the Biolog data base and theirown data base and found that the results agreed with thoseobtained by serologic testing. In further experiments, it wasdetermined by using distilled water as the diluent and a

normal aerobic atmosphere during incubation that adequatenumbers of positive reactions occurred at 24 h to allowaccurate identification of the tested strains. The authorsconcluded that the Biolog system had the ability to identifythe tested Legionella strains at least to the species level butthat multiple strains from all known species would have to becharacterized to establish a comprehensive and stable database.

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Miller and Rhoden (79) evaluated an early version of theBiolog GN data base with a diverse group of clinicallyrelevant members of the family Enterobactenaceae (212strains) and other gram-negative organisms (105 nonferment-ers and 35 oxidase-positive fermenters) consisting of 91species. The isolates consisted of usual and unusual organ-isms in proportions likely never to be found in the clinicallaboratory and were identified by conventional biochemicaland serologic techniques. The Biolog GN was read on theMicroPlate Reader, and only 266 (75.6%) of 352 organismstested were identified with an acceptable similarity index(SI). Of the 266 strains identified, 87.3% were correct at thegenus level and 75.6% were correct at the species level after24 h. The error rate was 12.8%. In this study of 352 strains,46.6% of the species were correctly identified at 4 h and57.1% were correct at 24 h. The error rate was 10.4% at 4 hand 9.6% at 24 h. When the isolates used in this study werereevaluated with an upgrade of the data base (Release 2.00),86.4% of the members of the family Enterobacteniaceae and80.4% of the other gram-negative organisms were correctlyidentified. The authors pointed out that the SIs of thecommon clinical isolates may be weakened by slow-growingenvironmental strains. In late 1991, an expanded and revisedversion of the data base (Release 3.00) was introduced. Thisversion has not yet been evaluated.

Recent abstracts have reported on the Biolog GN, butonly one of these studies used the MicroPlate Reader.McLaughlin et al. (76) reported on the Biolog GN andMicroPlate Reader for identification of infrequently isolatedgram-negative human pathogens. All strains tested wereobtained from the American Type Culture Collection. BiologGN correctly identified 68.5% (89 of 130 strains) to specieslevel and 79.2% (103 of 130 strains) to genus level. Tenpercent (13 of 130 strains) were incorrectly identified. Barthet al. (12) evaluated the Biolog GN but did not use theMicroPlate Reader. They tested the Biolog GN with 46miscellaneous gram-negative bacteria recently isolated fromhumans. The isolates had been identified by conventionalbiochemical tests. The Biolog GN results were read at 4 hand at 16 to 24 h. In most cases, the 4-h reading was the sameas the later reading. Fifty-nine percent of the organisms werecorrectly identified to species level, and 83% were identifiedto genus level. Four of the organisms were listed as thesecond choice for the Biolog GN, and there was no correla-tion between conventional and Biolog GN identifications forfour other organisms. The colorimetric reactions were diffi-cult to interpret. Roman et al. (100) reported on the BiologGN but did not use the MicroPlate Reader. They tested 75strains of P. cepacia isolated from cystic fibrosis patients inOhio, in Utah, and in Toronto, Canada. The strains had beenidentified by using conventional biochemicals. At 24 h, 53(86%) of 62 strains were correctly identified by the BiologGN. To determine genus similarities and species differences,Wong (124) tested 12 strains of Brucella melitensis, 9 strainsof Brucella abortus, and 6 strains of Brucella suis withBiolog plates. Strains that were epidemiologically related(strains isolated from common-source infections and labora-tory accidents) were also tested. After incubation, the plateswere read visually. Methylpyruvate, monomethyl succinate,and DL-lactic acid were utilized by all Brucella spp. tested.All B. suis isolates tested utilized L-arabinose, a-ketobutyricacid, uronic acid, ,B-hydroxybutyric acid, and uridine. Thelast two substrates were positive only with B. suis. D-Fruc-tose, alaninamide, L-alanine, L-asparagine, and L-glutamicacid were utilized by all B. abortus isolates tested. It wasconcluded that each Brucella strain had some distinguishable

features in its metabolic profile and that strains that wereepidemiologically related had similar metabolic profiles.However, the results suggested that the Biolog systemwould not replace standard methods for species identifica-tion of Brucella isolates. Ewalt et al. (35) evaluated themanual Biolog system for the ability to differentiate Brucellabiovars. The reference system for identification was notmentioned. They tested isolates of B. abortus bv. 19; B.abortus bv. 1, 2, and 4; B. suis bv. 1, 2, and 3; Brucella ovis;Brucella canis; B. melitensis bv. 1 and 2; and Brucellaneotomae. There were no visible reactions at 4 h, but therewere definite reaction patterns for each Brucella sp. and forthe majority of biovars at 24 h. Isolates of the same speciesand biovar demonstrated some variation in their metabolicprofile. Methylpyruvate and DL-lactic acid were utilized bymost of the isolates.There is an obvious need for further evaluation of the

Biolog GN and MicroPlate Reader for identification ofgram-negative bacilli. There have been no published studiesof the Biolog GP. With the large Biolog data base forgram-negative and gram-positive bacteria, the Biolog systemcould enhance the ability of laboratories to identify unusualbacteria. As the capabilities of the Biolog system continue toexpand and the accuracies of the new products are verified,the value of the Biolog system in the clinical laboratoryshould increase.

MIDI MICROBIAL IDENTIFICATION SYSTEM

Gas chromatography of cellular fatty acids is a rapid andreliable means of identifying organisms encountered in theclinical laboratory (80, 81). Because of the large number offatty acids found in the cell wall and cell membranes ofbacteria and because the composition of cellular fatty acidsis a very stable genetic trait that is highly conserved within ataxonomic group, fatty acid composition can be successfullyused for identification of bacteria. The MIDI MicrobialIdentification System (MIS; Microbial ID Inc., Newark,Del.) is a fully automated, computerized, high-resolution gaschromatography system that can analyze more than 300 fattyacid methyl esters ranging in length from 9 to 20 carbons.The MIS computer then searches data bases of knowncompositions to automatically identify yeasts, anaerobicbacteria, and aerobic bacteria, including mycobacteria.Welch (123) has recently reviewed the applications of cellu-lar fatty acid analysis in clinical microbiology and hasdescribed the fatty acid profiles found in various microor-ganisms.The MIS data base includes information for identification

of 15 genera and 65 species or subspecies of the familyEnterobacteriaceae; 45 Pseudomonas species, subspecies,or biovars; 18 Staphylococcus species or subspecies; 19Bacillus species; and 53 additional genera of aerobic bacteriacontaining 197 species, subspecies, or biovars. In addition,the MIS data base includes information for identification of32 species, subgroups, or complexes of mycobacteria; 31genera of anaerobes containing 254 species, types, orgroups; and 23 genera of yeasts including 195 species orsubspecies. Periodically, an expanded and updated data baseis provided at no cost to users of the system.The MIS is composed of a gas chromatograph (model

5890A; Hewlett-Packard Co., Avondale, Pa.) equipped witha fused-silica capillary column (25 m by 0.2 mm [innerdiameter]) containing cross-linked methyl-phenyl silicone asthe stationary phase, an automatic injector, a sample con-troller, a sample tray, a flame ionization detector, an elec-



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tronic integrator controlled by a DOS-based 386 computersuch as the Hewlett-Packard Vectra, and a ThinkJet Printer/Plotter. The MIS software includes programs for operationof the gas chromatograph, automatic peak naming, datastorage, and comparison of the unknown profile with one ormore data bases by using pattern recognition algorithms.The data base contains more than 100,000 profiles of strainscollected worldwide and grown under standardized condi-tions. Generally, 20 or more strains of a species or subspe-cies are included in the data base.

Environmental aerobic bacteria are cultured on Trypticasesoy broth agar (TSBA) at 28°C. Bacteria that grow poorly onthis medium are grown on a more enriched medium (e.g.,Neissenia spp. are grown on chocolate agar). Clinical aerobicbacteria are incubated at 35°C on TSBA. Mycobacteria aregrown at 35°C on Middlebrook 7H10 or 7H11 agar witholeate-albumin-dextrose-catalase enrichment (Mycobacteri-um marinum is grown at 30°C). Yeasts are cultured onSabouraud dextrose agar at 28°C. Anaerobic bacteria aregrown overnight in peptone-yeast extract-glucose broth at35°C and harvested by centrifugation. Approximately 40 mgof cells is saponified with 1.0 ml of 1.2 M NaOH in 50%aqueous methanol by heating the cells in a boiling-water bathfor 30 min. The saponified cellular lipids are methylated with2 ml of methylation reagent (325 ml of 6 N HCI and 275 ml ofmethanol) for 10 min at 80°C, and the fatty acids areextracted with 1.25 ml of extraction reagent (200 ml ofhexane and 200 ml of methyl tert-butyl ether) by rotating themixture for 10 min at room temperature. The lower, aqueousphase is removed; 3 ml of sample cleanup reagent (10.8 g ofNaOH dissolved in 900 ml of distilled water) is added; andthe tube is rotated for 5 min. About two-thirds of the organicphase is then transferred to a septum-capped sample vial andplaced in the sample tray. Samples are then logged into thecomputer. The automatic injector injects 2,ul of the extractthrough a heated, self-sealing rubber septum in the heatedinjection port (250°C). The autosampler allows the system tobe operated unattended for up to 2 days at a time. Theinjected sample is volatilized and swept through the columnby a stream of carrier gas (hydrogen). The column is encasedin a thermoregulated oven, and the MIS computer raises thetemperature from 170 to 270°C at5°C per min. At the end ofthe analysis, the column is cleaned by heating (310°C for 2min). The flame ionization detector (300°C) sends the elec-tronic signals produced by the analytes to integrators thatamplify and process the signals. These data are passed to thecomputer, where they are stored and can be compared withthe data base. MIS identifications are listed with a confi-dence measurement (SI) on a scale of 0 to 1.0. The total testtime for each specimen is 30 min.The calibration standard used with the MIS is a mixture of

straight-chain saturated fatty acids from 9 to 20 carbons inlength and five hydroxyl acids. With the calibration mixture,the retention time of the various peaks can be converted toequivalent chain length data for fatty acid identification. Theequivalent-chain-length value for each unknown compoundis compared with the external standard for peak naming.Changes in sample injection volume and variables such ascarrier gas flow rates and column and detector temperatureswill affect the sample retention time. Therefore, the calibra-tion mixture is analyzed after each 10 sample analyses tocorrect for any possible drift of retention time.The Library Generation Software allows the user to gen-

erate data bases. The software contains cluster analysisprograms that will generate dendrograms or two-dimensional

plots of principal-component analyses. The cluster analysisprograms are valuable for tracking nosocomial infections.The MIS has proven valuable in some cases for the

differentiation of phenotypically similar organisms and forsubgrouping or subspecies characterization of organisms.Wallace et al. (121) used the MIS to compare the fatty acidprofiles of Kingella denitrificans, Kingella kingae, and Kin-gella indologenes with the phenotypically similar Cardio-bacterium hominis and Eikenella corrodens. Kingella in-dologenes, Cardiobacterium hominis, and Eikenellacorrodens demonstrated large amounts of cis-vaccenic andpalmitic acids, whereas myristic and palmitic acids were themajor acids in Kingella denitnificans and Kingella kingae.Only Cardiobacterium hominis lacked 3-hydroxypalmiticacid and 3-hydroxymyristic acid and could be differentiatedfrom Kingella indologenes and Eikenella corrodens by thepresence of 3-hydroxypalmitic acid and 3-hydroxymyristicacid, respectively, in these bacteria.

Christenson et al. (22) isolated Pseudomonas gladioli from11 patients with cystic fibrosis and found that it was notassociated with any infectious complications. P. gladioli isprimarily a plant pathogen and is rarely isolated from hu-mans. Since it resembles P. cepacia, a known infectiousagent in cystic fibrosis patients, the authors confirmed theidentity of the isolates by using conventional biochemicals,DNA hybridization studies, and analysis of cellular fattyacid profiles with the MIS. Most of the P. gladioli isolatescontained 3-OH C10:0 fatty acids, whereas all 58 strains of P.cepacia lacked this fatty acid.Mukwaya and Welch (84) determined the cellular fatty

acid profiles for 42 strains of P. cepacia isolated frompatients at five cystic fibrosis centers. Hexadecanoic (C16:0)acid, cis-9 hexadecenoic (C16:1 ci,9) acid, and an isomer ofoctadecenoic (C18:1) acid were present in significant amountsin all strains, and none of the fatty acids had fewer than 14carbon atoms. Through numerical analysis of the fatty aciddata, the authors identified a different subgroup present ateach of the cystic fibrosis centers.Lambert and Moss (65) used the MIS to determine the

cellular fatty acid profiles of 182 Legionella strains repre-senting 23 species. The 23 species differed in the relativeamounts of 14-methylpentadecanoic (i-C16:0), hexadecanoic(C16:1), and 12-methyltetradecanoic (a-C15:0) acids and couldbe placed into three major fatty acid groups. When the fattyacid profiles of these Legionella strains were linked withtheir ubiquinone contents, the strains could be distinguishedfrom other gram-negative bacteria.deBoer and Sasser (31) examined the cellular fatty acid

compositions of Erwinia carotovora strains and found thatErwinia carotovora subsp. carotovora and Erwinia caroto-vora subsp. atroseptica had six common fatty acids but thatthe subspecies could be differentiated because three of thesefatty acids had different ratios.Moss et al. (83) found that Moraxella spp. could be

differentiated from Acinetobacter spp., Psychrobacter im-mobilis, Oligella urethralis, and CDC groups EO-2, EO-3,M-5, and M-6 on the basis of differences in cellular fattyacids. The MIS also determined that Moraxella bovis, Mor-axella nonliquefaciens, and some strains of Moraxella lacu-nata have a common fatty acid group, while Moraxellaosloensis, Moraxella phenylpyruvica, Moraxella atlantae,and strains of Moraxella lacunata have species-specific fattyacid profiles. They also used pigment production, cellularmorphology, and cellular fatty acid profiles of strains orig-inally classified as CDC group EO-2 to identify them asPsychrobacter immobilis, EO-3, or EO-2.

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Osterhout et al. (91) evaluated the MIS with 573 isolates ofgram-negative nonfermentative bacteria including Pseudo-monas, Shewanella, Comamonas, Flavimonas, Xanthomo-nas, Acinetobacter, Agrobacterium, Alcaligenes, Borde-tella, Flavobacterium, Methylobacterium, Moraxella,Ochrobactrum, CDC group EO-2, CDC group VB-3, CDCgroup M-5, CDC group IVC-2, Chryseomonas, Sphingobac-tenum, Oligella, and Weeksella species. Of these, 536 werefresh clinical isolates and 37 were reference strains. Allisolates were identified by conventional tests. Isolates werecultured at 28°C for 22 to 26 h on TSBA with 5% sheepblood. MIS identifications with an SI of 20.5 were consid-ered a good match. The MIS correctly identified 478(90%) of532 strains contained in the data base. However, only 314(59%) had an SI of .0.5. Of the 54 strains incorrectlyidentified, 26 were Acinetobacter, Moraxella, orAlcaligenesstrains and 12 were Pseudomonas pickettii strains. Of 41isolates (representing 12 species) not in the data base, 33were not identified or were named with very low SIs. Theauthors attributed discrepancies to incorrect or poorly de-fined data base profiles or an inability to differentiate speciesthat are genetically and chemically closely related. Refer-ence strains of P. aeruginosa and X. maltophilia demon-strated a significant variation in SIs when they were testedon 100 different occasions. There was significant improve-ment in SIs when organisms were incubated at 35°C andanalyzed by a data base generated at this temperature withthe Library Generation Software. It was concluded that thedevelopment of a data base for the culture conditions rou-tinely used in clinical microbiology laboratories might im-prove the accuracy of the MIS.

Abstracts concerning the identification of common anduncommon clinical isolates by the MIS have recently beenpublished. deTurck et al. (33) evaluated the MIS withwell-characterized bacterial strains. The MIS correctly iden-tified 102 (93.4%) of 109 strains of Pseudomonas spp.(including 13 species) and 58 (81.2%) of 71 strains of othergram-negative nonfermenters (including 12 species). None ofthe strains were incorrectly named; rather, they produced noidentification. Of 30 strains of Bacteroidesffragilis tested, 29(97%) were correctly identified. The authors reported that118 additional anaerobic bacteria of various genera were allcorrectly identified to the genus level. The MIS, using theVirginia Polytechnic Institute anaerobe library, was com-pared with the RapID ANA II System (Analytab Products)for identification of anaerobic clinical isolates (74). Discrep-ancies between the two systems were resolved at VirginiaPolytechnic Institute by conventional testing. The isolatestested were 25 Bacteroides spp., 8 other anaerobic gram-negative bacilli, 20 Clostndium spp., 10 nonsporeforminggram-positive bacilli, and 14 anaerobic gram-positive cocci.The MIS and the RapID ANA System identified 97 and 94%,respectively, of the anaerobic isolates tested (P > 0.20).Ayers and Solomon (6) cultured Staphylococcus strains onTSBA at 28°C (595 cultures) or 5% sheep blood agar at 350C(1,318 culture). The standard method for identification ofisolates was not mentioned. The strains were chromato-graphed for cell wall fatty acids in the MIS, and the datawere stored by Library Generation Software. Cluster anal-ysis with two-dimensional plots and dendrograms were usedto demonstrate isolate relationships. Factors that adverselyaffected relatedness included growth on TSBA, small-colonyvariants, the initial culture from frozen or lyophilized refer-ence strains, cell wall fatty acid heterogeneity with somespecies, and the similar taxonomic position of some species.The MIS software recognized tight clusters with high SIs

when full-bodied colonies were obtained from 5% sheepblood agar that had been incubated at 35°C for 24 h.Euclidian distance cuts were used to define groups, andreference strains or phenotypes were used to name thedefined groups. The authors found that all 29 named specieswere identified by the generated library and 20 unnamedstaphylococcal strains (clusters) were recognized. Moss etal. (82) used the MIS to create cellular fatty acid libraryentries for 358 bacterial species or unnamed groups com-monly recovered from clinical specimens. There were 91cellular fatty acid groups, of which some were specific at thegenus or species level while others contained species fromdifferent genera. Conventional culture and biochemicalmethods served as the reference identification for the iso-lates. Of 2,018 isolates, the MIS correctly identified 78% tothe proper cellular fatty acid group, 13% were equivocalbecause of one or more unusual biochemical reactions, 6%were unidentified by both cellular fatty acid and conven-tional methods, and 3% were misidentified by cellular fattyacids. Master et al. (71) identified 13 isolates of pink-pigmented, oxidase-positive bacteria with the MIS, API20E, Rapid NFT, BBL Sceptor (Becton Dickinson), CorningUni-NF Tek (Flow Laboratories, Inc., Rosalyn, N.Y.),MicroScan, Pasco (Difco, Inc., Wheat Ridge, Co.), andVitek GNI. Identifications of the same isolates by conven-tional biochemical methods served as reference identifica-tions. The unknown fatty acid profiles were analyzed byboth the routine and an experimental MIS data base. TenMethylobactenum spp. and one Pseudomonas vesicularisisolate were correctly identified, and one isolate was cor-rectly classified as a gram-positive bacillus by the MIS. Theremaining isolate was not identified. The commercial bio-chemical systems were unable to identify any of the 13isolates.

Extensive evaluations of the MIS for the accurate identi-fication of common and uncommon clinical isolates (aer-obes, mycobacteria, yeasts, and anaerobes) and for isolateswhich are misidentified, unidentified, or identified with a lowlikelihood by commonly used commercial systems will berequired to determine the utility of the MIS in the routineclinical setting.

AUTOSCEPTOR

The autoSceptor (Becton Dickinson) will automaticallyread and report up to 15 Sceptor panels that have beenincubated off-line. The autoSceptor is capable of identifyinggram-negative bacilli and interpreting breakpoint or MICtests on gram-positive and gram-negative bacteria after 18 to24 h of incubation. Various panel formats are available forsusceptibility testing.The identification panel contains 24 dried, modified, con-

ventional substrates. The data base includes information foridentification of 42 species of the family Enterobactenaceaeand 36 groups, genera, or species of nonfermentative andoxidase-positive gram-negative bacilli in 18 to 24 h.The autoSceptor is composed of a modified InterMed

ImmunoReader NJ-2000 microELISA reader, Digital Profes-sional 350/380 computer, and a Data Management Center(DMC). The DMC has menus for demographic entry, dataanalysis, editing, report generation, and epidemiologicalevaluations. A bidirectional mainframe interface is available.

Inocula in 0.85% saline are prepared by suspending colo-nies grown overnight on selective or nonselective media tothe equivalent of a 0.5 McFarland turbidity standard. Panelsare inoculated (100 ,ul per well) with a computer-controlled,



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TABLE 5. Studies comparing automated identification systems

No. of System (% identification accuracy)organisms P value Reference

tested Vitek autoSCAN-4 WalkAway-96 Sensititre

246 GNI (99.2) R-GNB (94.3) <0.01 30292a GNI (96.2) R-GNB (95.2) >0.50 9591b GNI (90.1) R-GNB (95.6) >0.10 95

180a R-GNB (86.0) AP80 (79.0) >0.05 7060b R-GNB (82.0) AP80 (35.0) <0.001 70

358a GNI (94.7) R-GNB (83.0) AP80 (93.3) <0.001 (GNI Vs R-GNB) 24>0.20 (GNI vs AP80)<0.001 (AP80 vs R-GNB)

142b GNI (79.6) R-GNB (74.6) AP80 (71.1) >0.30 (GNI vs R-GNB) 24>0.05 (GNI vs AP80)>0.50 (R-GNB vs AP80)

232 YBC (85.0) YIP (59.0) <0.001 107

a Members of the family Enterobacteriaceae.b Gram-negative organisms that were not members of the Enterobacteriaceae.

seven-tip automatic inoculator or the totally automatedSceptor preparation station. Panels are incubated off-lineaerobically at 35°C for 18 to 24 h. Before panels are loadedinto the autoSceptor, specimens are logged into the DMC,bar code labels are attached to the panels, and the bar codesare scanned with a bar code wand. This allows linking of theunique sequence number on the labeled panel to the patientspecimen information. Kovac's reagent is added to theindole well, and ferric chloride is added to the tryptophandeaminase well. Immediately before the autoSceptor readsthe panel, an instrument-mounted scanner reads the barcode and links that panel with the patient specimen informa-tion in the DMC. To obtain test results, light absorbances ofthe substrates in Sceptor panel wells are measured. Lightfrom a halogen-tungsten lamp is projected onto a row ofseven wells. The light from each well is reflected, con-densed, filtered, and directed to a multibranch glass fiber,which branches the light from each well to a separatephotodiode detector. Absorbance values from selected fil-ters are used to determine color changes in the colorimetrictests. After all biochemical reactions are read, the DMCdetermines the identification of the isolate and links thatresult to any other patient data.The only report in the literature on the autoSceptor is an

abstract (21). The authors evaluated the accuracy of theautoSceptor with 570 gram-negative bacilli. Panels read onthe autoSceptor were also examined visually, and the bio-chemical test results were compared. There was more than97% agreement for biochemical tests, and identification tospecies on the autoSceptor was 95%. The reference systemused to establish the true identity of the isolates was notmentioned.The disadvantages for the autoSceptor are off-line incuba-

tion of panels, manual addition of reagents, and identifica-tion of only gram-negative bacilli. The initial report on theautoSceptor is favorable, but this system must be furtherevaluated to determine its utility in the clinical laboratory.

STUDIES COMPARING AUTOMATEDIDENTIFICATION SYSTEMS

Five studies have compared the identification accuracy ofeither two or three different automated identification sys-tems. Table 5 shows the identification accuracy for each ofthe automated systems, the percentile (P value) of thechi-square distribution as determined by the chi-square test

for each study, and the cited references. The details of howeach automated system performed versus the referencesystem are found earlier in this paper where that particularautomated system is reviewed.Table 6 compares various features of the automated iden-

tification systems reviewed in this paper.

DISCUSSION

The systems reviewed here vary considerably in theirapproach to the identification of microorganisms. The MIS,for example, analyzes cellular material, whereas others usemore conventional end points such as increase in cell densityor color changes due to shifts in pH. There is also somevariability in the degree of automation, spectrum of organ-isms identified, and turnaround time. The constant is thatmost of these instruments have proven their applicability inclinical microbiology laboratories throughout the country.Specific capabilities, as well as a review of their operationand the details regarding accuracy of identification, aredescribed in the individual sections of this review and willnot be summarized in the discussion. Although the systemsavailable today have proven capabilities, there is ampleroom for improvement and we can expect an expanded rangeof identification capabilities, shorter turnaround time, andgreater automation, particularly in the area of data manage-ment, to be available in the future. The devices describedhere are, with the exception of the Vitek urine card, basedon pure culture techniques. In other words, the organismmust be isolated before the identification process is per-formed. There are, however, procedures, both manual andautomated, that can identify organisms directly in specimensby using antibodies or nucleic acid probes. We believe thatthese direct approaches have a very definite but limitedutility in the immediate future. For conditions such asmeningitis, the consequences of the infection and the ex-pected range of pathogens are limited enough to make suchan approach desirable and feasible, but in other situations,such as diarrhea, it would not be economically or oftenmedically justified. A variety of other approaches for iden-tification of microorganisms have been described, such asflow cytometry, image analysis, and mass spectrometry (13,20, 69). These all have the potential to provide rapid identi-fication of microorganisms but at present are beyond thescope of the routine clinical microbiology laboratory.As discussed by Miller (78), because there is no conven-

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tion for what constitutes an accurate or inaccurate test resultfor automated identification systems and under what circum-stances they should be tested, the overall accuracy of thesystems is difficult to determine. Miller (78) presents a verygood description of steps which could lead to a consensusapproach to resolve many of the issues that have plagued usduring our review of the literature on automated identifica-tion systems. We are unable to objectively answer thequestion of which instrument is best for which application.Among the many reasons is that the accuracy of a system ishighly dependent on the organisms tested and that thesepopulations are never the same in the various studies. Also,by the time a study is reported, the manufacturer has oftenchanged the data base, altered thresholds on the reader, ormodified, added, or replaced some of the substrates. Inaddition, some studies have used photometrically standard-ized inocula, whereas others have used generally less reli-able visually prepared inocula. Also, the reference system insome studies may not always yield the correct identification,and hence the system under study can be unfairly penalized.In a recent reevaluation of the API 20E, O'Hara et al'. (89)showed that a system did not even necessarily provide thesame level of accuracy over time. They were not able toexplain why this was so, but an obvious difference was anexpanded data base. The list of variables goes on! However,it is obvious from the literature that automated systemsaccurately identify common clinical isolates. Rare biotypesof common organisms and unusual organisms are oftenmisidentified, identified at low likelihood, or not identified.The accuracy of systems has been observed to vary evenwith the same kind of organism when it is evaluated indifferent laboratories. Kelly et al. (60), Stevens et al. (111),and Truant et al. (120) suggested that variation in thebiotypes of individual species encountered in different areasof the country may contribute to this performance variation.In support of this hypothesis, Kelly et al. (60) reported acollaborative evaluation of the Autobac by four laboratoriesin various parts of the country. Each laboratory testedorganisms collected in its geographic area. Correct identifi-cation of Citrobacter spp. ranged from 69 to 100% among thelaboratories, and that of Enterobacter spp. ranged from 62 to100%. The reason for this variation was not evident. Theauthors also noted a variation in accuracy with Acinetobac-ter spp. at two of the laboratories. One of these laboratoriescorrectly identified 65% of their strains, whereas the othercorrectly identified 100% of their strains. When the strainsfrom the first laboratory were retested at the second labora-tory, the accuracy forAcinetobacter spp. was similar to thatof the first laboratory. Land et al. (67) reported that only 27%of serogroup D isolates of Cryptococcus neoformans, whichare common to Europe and other temperate regions, werecorrectly identified by the MicroScan YIP. On the otherhand, 83% of serogroups B and C, serogroups geographicallyfound nearest to the manufacturer, were correctly identified.Serogroup A isolates, which are commonly found in theremainder of the United States, were identified 73% of thetime. The results of Kelly et al. (60) and Land et al. (67)suggest that the data bases of these automated identificationsystems include limited biotypes from certain geographicareas. That geographic or regional variation exists is notsurprising, but apparently some manufacturers have notadequately regionalized the strains in their data base, andthis has caused problems. We know that several manufac-turers have programs for acquisition of biotypes of commonand unusual organisms from various geographic areas of the

country. As the data bases are expanded to include theseorganisms, the accuracy of the systems should improve.Would more substrates in a panel improve the accuracy of

identification? Lapage et al. (68) designed a computer pro-gram to address this question. The results indicated thataberrant strains of members of the family Enterobac-teriaceae and nonfermentative gram-negative bacilli re-quired an average of 29 to 32 tests for reliable identificationand that additional tests did not improve the accuracy. Mostof the systems reviewed in this paper contain 29 or moresubstrates in their identification system. Certainly, there arecircumstances under which the availability of fewer than 29substrates is limiting. For example, the Avantage BIC, with20 substrates, will identify only 10 members of the gram-negative organisms that are not members of the Enterobac-teriaceae.How effective are automated identification systems in

determining the relatedness of isolates for epidemiologicpurposes? The Biolog system and the MIS have clusteranalysis programs in the form of dendrograms and two-dimensional or three-dimensional plots to demonstrate therelatedness of strains or species. There are, however, onlylimited reports on the effectiveness of these systems forepidemiologic purposes. Wong (124) used the Biolog systemto test Brucella strains which were isolated from common-source infections or laboratory accidents and found thatstrains that were epidemiologically related did have similarmetabolic profiles. However, it was not indicated whethercluster analysis or dendrograms were used in that study. Thestudy suggested that the Biolog system should prove usefulin epidemiologic studies. Mukwaya and Welch (84) used theMIS to determine the cellular fatty acid profiles for 42 strainsof P. cepacia isolated from patients at five cystic fibrosiscenters and found, through numerical analysis of the data,that a different subgroup was present at each of the centers.Clarridge and Harrison (23) evaluated the MIS for straindifferentiation of X. maltophilia from the surgical intensivecare unit (7 strains) and medical intensive care unit (9strains) in a hospital. When it was assumed that a group ofstrains from different patients were clustered with the samedegree of relatedness as defined by all strains from onepatient during different extractions and runs on the MIS, themedical intensive care strains made up a single groupwhereas the surgical intensive care strains were grouped intothree clusters. Only two studies have evaluated the repro-ducibility of biochemical tests with automated instruments.Stoakes et al. (113) retested 50 strains of anaerobes (30gram-negative bacilli and 20 clostridia) with the autoSCAN-4AIP. Of the 1,200 reactions, 69 (5.6%) were recorded differ-ently between the two trials. As a result, 8% of the strainschanged classification from correct identification to incorrectidentification. Murray et al. (85) evaluated the reproducibil-ity of the biochemical reactions obtained with the QuantumII BIC by testing, on three consecutive days, 40 gram-negative organisms belonging to members of the familyEnterobacteriaceae. Identical biocodes for all three testswere obtained for only 10 (25%) of the 40 organisms. Aftermodification of the photometer, an additional 25 isolateswere evaluated. For the triplicate tests, identical biocodeswere obtained for 13 organisms (52%). The results of theselast two studies suggest that the automated systems evalu-ated are not highly reproducible in generating identicalbiocodes and that the error rate would be too great for thesystems to be of value in epidemiological studies. Howreliable other automated systems would be in generatingidentical biocodes has not been reported in the literature.

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We believe that more standardized evaluations of auto-mated identification systems should be performed but thatanyone contemplating such studies should examine verycarefully the observations of Miller (78). When trying tocompare or determine the accuracy of the available instru-ments, we were surprised to find that comprehensive evalu-ations have not been reported, particularly since many of thechanges in data bases and other improvements have beenmade. Some automated systems or parts of systems have notbeen independently evaluated.

Manufacturers should consider providing test cards orpanels that contain only the supplemental substrates re-quired for their identification systems. When supplementaltests are necessary, they are frequently not readily available,and significant delay and expense are required for identifi-cation of what should not be a difficult isolate. There is aneed to develop techniques to identify and determine sus-ceptibility patterns for life-threatening infections morequickly. We need methods for direct testing of positive bloodcultures, for example.We should make better use of the computer capabilities of

the systems. Epidemiology, determination of the signifi-cance of organisms identified, and continuing educationcould easily be enhanced. For example, when a relativelyrare isolate is identified, the operator should be able to querythe computer data base for information regarding its likeli-hood of causing infection, its probable susceptibility pattern,and other relevant data.Although it is difficult to imagine a more exciting and

stimulating period for clinical microbiologists than the recentpast, the immediate future appears to be at least as stimu-lating.

ACKNOWLEDGMENTSWe thank Janice Edwards-Bryant and Susan Fogg for typing the

manuscript.REFERENCES

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