Development in laboraty informatics

Clinical Microbiology Newsletter Vol. 18, No. 9

h4ay 1,1996

Development in Laboratory Informatics Pllen Jo Barat, Ph.D. Adjust Associate Pr+s.wr qf hfe&%e Department #Medicine University qfC.al~oti Los Angeles, CA 90095~1405

Today’s microbiology laboratory’s product is information, the results of the testing that we perform. To achieve the ultimate objective of better patient care, three goals related to &livery of theproductcanbeidentiliedzfastertumaroundtime,maeefficientdistribution, andmaereli&leandapph&le(higber quality) results. Auhas cantrib ute!dtosomesuccesses, such as dmeasingtwnarodtimetonz3ultsavaibbility. ButasDr.KenRyanprescientlywamedin 1984,autanauatmaysolveatecMcal problembutat?ateaninformation-hau dlins problem (1). ‘Ihebu%eoning scienceofli&oMay infamaticsishelpkrglaboratoryscientiststoachievetheirgoalsofinforma&ln gemXauonanddelivery.rnformaucsis thescienceamcemedwiththegathermg, . . ~.staage,and reu&alof’&owledge.“b&mma&s, pa&uliulyinotherdiscQ&s,isnotexelusively the realm of computtxs. Ebwever,theenmnousanormtsofdata hdVedandlbCspaedWith*data handlingrmrstoccllrin-IaLmrataysciencediccBtethatalm~systemsbepAIlMMtin~ydiSC~of

li&r&ayinf~(2).Int&aslrvey amduchdbytheCollegedAmes&tP tholog&in1990fotmdthat,of2370 labm%a&respondingbam ~ly45%hadalaba%ayinfonn&onsys&ninplace(3).‘Ihdnumberislikelytobemtihighertoday. CMNEEJ 18C9KX2S96

Informatics Systems for Simplifkation of Routine Tasks LaboratoryinfOmMtics systems shod be designed to satisfy four criteria. Piit, they must be easy to use. Most of the systems used in microbiology laboratories today fail that trite rion-they are text-based, keyboard mnemonic-driven, and require a steep learning curve. In contrast, most home and business computer programs are graphics-based and somewhat intuitive. The graphical user interface (GUI or “gooey”), using icons and easily recognixable symbols for operational activities such as removing a file by placing it into a trashcan or printing a document by choosing a printer icon, requires fewer operator functions and is easier to grasp. In the future, Windows-type programs and GUIs will free microbiologists to concentrate on benchwork rather than on negotiating a cumbersome series of keyboard commands. Easier-to-use interfaces will help with the second criterion: elimination of paperwork. Optical disk storage systems, already in use in many hospital settings, can help cut down on paper storage and facilitate information retrieval (4). other problems associated with paper records include transcription et~ofi, writing time, and interpreting handwritten information. Not creating thepapexreuxdinthefirstplaceisthf3 ultimate goal, however. Several micfobiology-friendly labora&y computer systems have incorporated a paperless workcard that allows capture and later recovery of all results generated during workup of a particular specimen, includE!sevier

ing individual organism identification test results and text-based descriptions and comments. Such systems will become universal in the near future. Barcoding, for inventory control as well as patient specimen and microbiology culture handling, has already been used to save laboraunians from needless labeling and time-consuming searching and veritlcation of individual subcultures and specimen containers (5). Automated blood culture instruments, for example, use bar codes to identify individual specimens and medium types. When entering or removing bottles from the instrument, the operator need only use a laser-light bar-code reader wand to record the action taken on the particular bottle. Bar codes are used for instrument quality control activities as well. Third, systems must be designed to

In This Issue Development in Laboratory Informatics . . . . . . . . . . . . . . . . . . . . . .65 In the not-sodistantfbturc.

the cmbi-

nationof sophisricated computers and intelligent smre will propel the

microbiokqy laboratory toward new achkvements in health care delivery

The Value of Human Papilloma Virus Typing. . . . . . . . . . . . . . . . . . .70 Although mo&cular techniques have allowed better epidaidogic studies on the aswciotion of certain HPV types and cetvical m@aGa. we may not be ready to implement these as stanabrd diagnostic tedts

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avoid redundancy, which wastes hardware and software storage space and resources (6). The use of relational databases, such as Microsoft Access and Borland Paradox, allows overlap ping storage and retrieval of information from different archives into a single information repository (the query report or answer file). For example, a relationaldambasecanbeusedtocreatea stock organism library. Different files arecreatedtostorepatientdemographic data_ specimen information, organism morphological and biochemical data, and organism susceptibility patterns. If all files share one key field (often the specimen accession number), then a query report, such as a list of all ciprofloxaein-resistant Pseudomonas aeruginosu isolates from appendix tissues from female patients older than 65 yr, can be generated in seconds. Imagine the time and effort required to create such a file if organism-based records or patient result printouts (flatfile dambase systems) were the only resources available. Another route toward avoiding redundancy is the growing use of client-server systems. With these computing networks, one or more central processing units (CPUs) can manage program software and major file storage, sending the information out to any of a network of linked workstations or other computers, often personal computers (PCs), to perform specific functions. The server acts in a multitasking fashion, routing tasks to the most appropriate of the interlinked computing devices, printers, CPUs, and other

pexipherals,to makethe mostefficient use of all the compouents. ‘Ihefourthcriterionis thatof opensystemarchitecture (7). The immense arrayof data-generating instruments,patientrecordqxWories, datainput and output devices, and the globahxation of healthcare demand that information be transportable across numerous platforms (8). Insurance providers and

HCFA have already adopted HL7 (Health Level 7). a standardized format of electronic data interchange (EDI), for encoding basic patient information for billing purposes. With the growing trend toward regional&&ion of patient care, increasing use of computer-based patient records (the new meaning for CPR), and centralization of certain highresource technologies, the need for immediate exchange of electronic information among diverse end-users demands a standardized communication format among software vendors. The use of universal CPR was recommended as early as 1991 by the Institute of Medicine. Although laboratory instrument and information systems vendors are not yet cooperating seamlessly, future progress will probably require open-system architecture for the majority of applications.

Informatics Systems for Information Retrieval, Integration, and Analysis Althoughextremely commonplace, the use of computerized search programs of national reference databases should not be overlooked. They can be used to gather information useful for choosing the right product among those available for a given laboratory test. Medline searches simplified by user interfaces such as Grateful Med. available from the National Technical Information Service, allow useful and timely gathering of published information on diagnostic test utilization and reliibility. Even preparation of references for manuscripts and talks is assisted greatly by reference manager programsworking in concert with word-processing programs.Most microbiology labomtories now preparetheir protocols and procedure manuals using word pnxxssing software. This form of informatics allows ready access to individual protocols (using search parametersand file handling capabilities) and the ability to make changes and update procedures

relatively effortlessly. Laboratories m even using educational software for training and refreshing skills. Programs for Gram staining and identification of anaerobic bacteria are popular currently; many others are in development. As teaching resources become scarcer and trained microbiology specialists become mom rare, such computer-based learning programs will become more advantageous. Yale scientists have created NetMenu, an on-line information access system accessible from all areas within the medical center (9). Users can connect to numerous information sources, including medical libraries, a clinical advice service, a drug/toxicology database, and the clinical laboratory. Clinical laboratory test menus and patient results are available now and future expansion plans include adding surgical pathology and radiology to the network. Laboratory informatics systems are integral to providing laboratory information to the Yale community via NetMenu, which serves as a model for other institutions. Microbiologists are familiar with computer-based data analysis software. The API 2OEwas the most widely used multicomponent organism identification test product that employed computer-performedalgorithms to identify organisms based on the pattern of individual test results, entered as numeric codes. Recognition of several new biotypes and species of bacteria and detection of multistate outbreaks of disease caused by low level contamination of commercial injectables were made possible by the discriminatory power of the computer-assisted multicomponent system to recognize slight divergences from standardpatterns. More sophisticated systems that can juggle even larger numbers of variables, such as the MIDI software that analyzes gas-liquid chromatography patterns of the fattyacid methyl esters of bacterial cell walls

NOTE: No nesponsibiity is -ed by the Fubliier for any hjuy and/or damage to persons or pmpelty as a matter of prod- liability, negli~mce or othenvke. or Fran any we or opcntiaaofmy~p~,~or~~inedintbomrlcr*l&rrin.Nocuslcr(cdta(orpmcsdumlhwldka~~oulunlar,inUKICldQI’Ijudlmmt,itr~L jwtitial. BecaIne of npid 8din mcdicd #cknce& WC manmend that the independentverifhtion of diagnaws and dmg doaagen thouId be mrdr Disauuioma. views. and recommaadrtim P to medical pd. chicc of dngs. md dny douga are the renpolribility of the uthon. Mkmbidogy Newdettrr @SN 01964399) k issued twice nwathly in aw Ldexed volume b year by Elsevia Science Inc.. 655 Avawe of the Amaicu, New Yodr, NY 10010. P&Xpa year: 315X00: for onh oahdc of the United Statea.Mexico and Can&: $270.00. Scum&CIMSpostage paid at New York, NY and at uiditimal oviliig office. pahinrtcr. !kxul Adzus dmngce to CIinicd Mkmbhbgy h’ewskttw,Ekevia Scii Inc.. 6SS Avmuc of tlx Americas. New Yak, NY 10010. Chicd

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Newsletter 18:9,1996

or the Biolog system that identifies or-

ganisms based on 95 differentmetabolii variables, are now in routine use. The Yellow IRE combines sophisticated pat&m critic programs with numeric variables to perform fully automated urinalyses. Based on images of bacteria white blood cells, yeasts, crystals, and other urine sediment objects previously p@d into the computer’s memory, the instnnnen~identities the objects plesent in the urine being [email protected]~i~~~~i~s~~ ~~~~~~f~v~~~ by~~.~~~~~s~ me& cannot identify with certainty are placed onto the screen far operatorintervention and identifkttion. One labaatoryhasusedthissystemasaurine screening device to accurately identify those urines that do not need culture (10). A similar pattern-recognition softW~~~W~~~~~~t&al bi~~rn~~ and s~~~~ ~~~~rn~~~~~~ Aladin instrument, previously pmduced by API (11). Quality assurance activities are readily adapted to infamatics systems. In addition to producing graphical depictions of data, such as monthly percent of “contaminated”blood cultures by patient care unit or monthly improvements in~~f~STAT~ stains, computer analysis can be used to s~~~or~tv~~~ vious pattems that would be impossible with manual data manipulation methods. One innovative laboratay tracks changes in the incidence of microbes isolated using time series analysis with moving weighti averages (12). Their system de&&d incorrect organism ids and even nosocomial outbreaks that would have gone use using manual oversight. Because microbiology has tmditionally been a subjective disciplii, relying OnVisuaIiIl&lXe&tionofresultsand demanding constant judgment &45sions by practitionezs,it has not heen automatedtothede~thatotherlaboratolyspe<&,suchaschemis&yand hematology, have been. Certain higherlevel funcknr, howeva, previously the ~~~~~~~~ Withbth@IWiIIloEC~putersystems.

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NSAIDS

Others

Figure 1. Hours between issue of critical value report and change in patient medication for control time period and computer-akrt interventionperiod.

Decision-support systems (DSS) to help in the production and verification of microbiology results are being developed and ~p~~~d rapidly. ‘Severallaboratoq inf~i~ systems now incorpurate an organism identifi~tion and susceptibility pattemmatching algorithm in their routine. Specific drug-bug combinations will genemte a “flag” that re4luiresoperator input for final results entry. For example, a member of the Enterobucteriuceueresistant to imipenem, an unlikely event, raises a flag prompting the ~hno~g~ to check the organism i~ti~~ti~ or to repeat the s~ti~dity test, ‘l&se simple decision support systems axecommonplace. Hierarchicalreporting of antibiotics based on resistance patterns is also aided by computer-generated formats already in routine use by several microbiology laboratory information systems. More advanced systems are bemg used for patient care decision support systems. A large Utah hospital developed a ~ornp~~ antibiotic consultant program to help physicians choose an empiric antibiotic therapy regimen (13). Data that were used to devebp the program, originally archived by an advanced laboratoryinformation system, included pathogens isolated from patients with similar characteristicsduring the previous 5-yr and &no time periods bssed on site of infection, as well as antibiotic ~~~bi~~ patterns far the organisms. Pharmacy input in&&d 0 19!XEbCViCrSCiQCChC.

site-specific antibiotic penetration and dosages, allergies, and renal problem dosage alterations. The pathogen and ~tibi~ data are updated mon~ly by the system. During deacon phase, physicians ordezed appropriate antibiotics signifmtly more rapidly (within 12 h of culture collection) when they used the antibiotic consultant than when they did not have access to the program (a mean of 21 h after culture collection). In addition, the compukriced consultant’s choice of antibiotics was more often appropriate (94% of cases) than physicians’ choices were (77% of cases). Most physicians (81%) felt that use of the program improved patient care. However, not all computerassisted programs are so well received. Informatics-based Changes in Results Delivery Numerousinstitutions have looked into i~~-~ results delivery (14). ‘Ihe need is ~~n~bl~ one study evaluating the convenk3nal reporting system found that 15% of critical values reported by the laboratory were not documented at all on the patient’s chart ( 15). Workers at Harvard developed a

computer-assisted critical value reporting system for patients receiving nephrotoxic agents (16). If a patien~‘s czatinine rose abovea p~e~~~ pammekr,an al& was deliv& by emailto all physicianswho had re cently logged into the system to access 01964399#6/&0.00+ IS.00

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Figure 2. Phatmacy eqwnditures for the 3 yr duting which microbiology-pharmacy computerassisted intervention program was in @kct.

data on that particular patient. After im-

plementation, no differences were seen in time intervals to changes in the patient’s medication for three of the four categories of nephrotoxic drugs being monitored ACE inhibitors, aminoglycosides, and others (other antibiotics, analgesics,cancer drugs, etc.). The fourth category, nonsteaoidalauti-inflammatory agents (NSAIDs),exhibited significantly reduced time to change in therapy, accounting for a large part of the overall reduction of risk of serious renal impairment (55%) due to the alerts (Figure 1). Tire authors suggested that physicians normally Raid less attention to a mildly nephrotoxic agent, such astheNSAIDs,thantbeydidtothe well known agents such as aminoglyco&es. Thus, the alerts were only valuable and palatable to the physicians when they involved a parameter that might otherwise be overlooked. Twentyeight percent of physicians at this medical center found the alerts annoying (16). The message here is that computer-assistedresults reporting must be based on an appropiate intervention andbeseenasanassettohaveacompletely positive impact. Perhaps the most widely anticipated use of computer-assistedresults reporting among microbiologists is in the area of antibiotic susceptibilities.Microorganism identification and susceptibility testing instruments are developing softwaretolinkwithpharmzyprogramsto assist in this endeavor, although none hasachievedwidespmadacceptance yet. A multifaceted antibiotic utikation intervention program that made use of computer-genemted information was developed at the State University of New 68

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York at Buffalo (17). Organism isolation and susceptibility profiles were forwarded electronicaIlyfrom the laboratory to the pharmacy, where additional parameters were evaluated. Cases deemed appropriate for intervention were those in which the patient was improving on the initial therapy, and the pathogen isolated was susceptible to the original agent but also susceptible to a less expensive agent. A clinical pharmacist then approached the physician with a suggestion to change therapy. Over 9096of the recommended changes were made, resulting in a cost avoidance of morethan$8l,oooduringthefirst7mo period. Real pharmacy dollar costs for antimicrobial agents, however, declined by $~,OOO per year due to overall changes in physician ordering practices (Figure 2). The most common interventions were dosage adjustments @IO%), discontinuation of antibiotics (RF%),change to oral agent (17%) $116 D/C Rx

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Change to

Oral 11% Regimen Change 14%

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$134 $ = average per patient Figure 3. Dollars saved and percent of total savings by type of physician response due to microbiology-phawnacy computer-assisted interventiwsprogram. 8 1996 Eiscvier Scima Inc.

change to less expensive agent (14%), and diversion of patient to a research protocol (11%) (Figure 3). A simpler program was initiated at the LDS Hospital in Salt Lake City (18). patients whose antibiotics were inconsistent with the microbiology culture and susceptibility results were identified by a computer algorithm. A clinical pharmacist would then notify the attending physician of the discrepancy. Over a l-yr period, 32% of 2,157 susceptibility results genetated an alert. Of 420 interventions, physicians changed therapy 30% of the time. One reason for the surprisingly low rate of change was that the microbiology laboratory susceptibility report did not include a number of agents listed on the formulary and being used in individual patients. This was because the laboratory used commercially produced susceptibility testing panels with predeterminedantibiotics. Without specific in vitro data and with the patient showing improvement, many physicians chose not to change regimens. The results of this study should encourage labomtories and pharmacies to work together to merge their antibiotic choices.

The Future of Laboratory Informatics in Microbiology Voice recognition systems, already in place for anatomic pathology, will come of age in microbiology laborato ries. Gpemtors will record Gram stain results, rapid test results, manual susceptibility results, and colony characteristics by voice. The computer system itself will use vocal cues to remind microbiologists to perform certain functions or to sound warnings. One can imagine the computer saying “‘Areyou sure that organism is a Stenotrophomonas maltophilia?It was susceptible

to ampicillin.” Of course, the screen will display a colorful, intuitive graphical interface, with all relevant information, whether on-line or archived, immediately accessible with simple commands. Earliermifxobhbgy results on the same patient even from previous admissions, will automatically be displayedasawindowonthescreen.Tbe labo*itgrmanualandOllreruSefUlin-

formation, including reference mateaiC!linicd Micmbiology Newsletter 189,19%

pknmana

BaronCommunity Hospital lest

!mport result

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ratory infarmatios sphn8 survey: a chalkngcfor a decade. Arch. Pathol.

Name: Charles Droot Primary Diagnosis:

Urosepsis

Primary physician:

J. Pate1

Location:

and reporting. Ann. N.Y.

Acad.!ki. 428%3-250. 3. &vii, F., et al, 1993. A clinical lsbo-

Lab.Md. 117:12-21.

4. Jamot, JJ. 1994. Laboratorydata storage using the optical disk. Clin. Lab. !Zci.7:342-347.

3 north

5. Learn,,H.M. 1995. Bar codiig in the micmbidogy lab. Med. Lab. obt?mff

27~52-53. 6. Buck,GE 1987. The role of microcomputers for data analysis and storage. p. 177-187. In JR. Jorge. (ed.), Automation in clinical microbiilogy. Boca Raton, n, CRC Press, Inc.

7. La~way, T. 1994. L&ado&s begin to Previous day results

Main men1

Figure 4. Author’s depiction @the microbiology cmputer mcmitor work-card of the ftiure.

als, will be only a keystroke or a com-

mand away. Image capt4neand manipulation will be an extremely useful and used tool. Mimpes will have video cameras installe4iand all importantGram stain images will be captured, stored, and recalled as needed (Figure 4). ‘Ihey may even be appended to computerized reports sent to physiciansat theirpatient

unitwo&Mions or in theiroffices. Robotkswillhaveitsplaceinmicrobiology. Alreadyavailable,improved automatedsystemswill handletrauspoltof !qe&ensfromthepatient,routing of specimenswithinthe &xatory, inoculationofmedkpreparationand stainingofsmeals,andsub&Uingof brothcultures.RepMion of serumfor serologicaltests,pqaring oqqukms for storage,and~trieval of storedisolateswillalsobethereqonsibilityof autan&dsysteansIvsanytestsc~y ptXkmedmamlallyarwithsemi-& majedsy!&3lUwillbedoneCStiRlybyintdrume&intheneati. Testselectionandresultsinteqretationwillbeaidedbyneuralnetworks. Tksecompute~datahandlingand inteqfetationsystems,able to improve theirresultswith xepetition(training), iuesome&estoutedastheekctronic brain.using parauelalTaysof inputand outputdata,mXRalnetwaks arecurrentlybeing z%sSSedfar makingdiagClink! Mcmbii~

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nosticdecisionsin areassuch as prostatecancerand interpretation of Pap smears(19). Microbiologyuses include interpreting complexpatterns,such as thosegeneratedby a Westernblot, and readingprimaryplateculturesor initial specimengram-stainedsmears. Despitethesepredictionsof incn2sing use of infamatics in the laboratory, microbiologistsshouldnot fearfor their jobs. New parameters,inherentwith the continuousocclptenceofnew and emerginginfectiousdiseases,require humaninterventionand interpretation. Microbes are biological entities, subject totheforcesofnatunz.notallofwhich can be predicted or controlled for assessment by an automated system, even if the system is driven by the most sophisticated computer. Knowledgeable professionals will be required to program and validate the new instruments and create, modify, and update the computer-driven algorithms. The technology changes outlined and suggested here offer opportunities for microbiologists to grow and kam, moving into the

~opcn-rystanrapprDlchH~ Managemad Technology Augustz37-58.

8. Friedman, B.A., and W. Mitchell. 1993. InQrating infoxmatial from decentdized laboratcry testing sites: the creation of a valuc+addal network. Am. J. Clin. Pathol. 99~637-642.

9. Shifman, MA., et al. 1994. NetMenu: expaiuuzeintheimplementationofsn institutionsl menu of information sources. Proceedingsdhe annual symposium on computet applications in medical care, p. 554-558.In C. S&an

(ed.), PalifT?aIt-ccntered canputing: 17th WNl8lsynrpDeirmrOnCUtIpU~applicatiurriumodicalcarc(Aconfcnnetofthe ~~InfmnaticsA&qo&_ tiaI).McGlaw-Hi&Inc,NewYalL 10. Bartlett, R.C., et al. 1992. &reeniug for urinary tract infection with the Yellow IRIS. Lab. Med. 23:59%602. 11. D’ Amato, R.F.. ct al. 1988. Novel sdicaticnofvideoimageprocessingtobib: cllcdalltnd~~ily m. J. Qin. Micrdd. 26: 1492-1495. 12. Dcssau, R.B., and P. Steenberg. 1993. complterid surveillance in clinical microbiology with time saies analysis. J. Clin. Microbil. 31:857-860. 13. Evans, RS., et al. 1994. Improving empiric antibbtic selectii using cornputcr decisian support. Arch. Intun. Med. X%878884.

futureof laboratoryinformaticsand diagnosticswith confidence.

14. Ryan, K.J. 1983. Computer systems in clinical microbiibgy. C&L Lab. Med. 3:101-l 10.

R+nmces

15. Tate, K.E, adR.M. Gardna. 1994. Computers, quality, and the clinical laboratory: a look at critical value reporting, p. 193-197. In C. S&an (cd.), op. cit.

1.

Ryan,K.J.1985.Mcthndsfor+dhnnsmissiiofmicrobiologyrcpolts.Diagn Mkmbid.InfectDis. 3:33S-385.

2. Rysn, K.J. 1984. ‘he coqutcr

in miCTobiology:future applications in test 0 19%EkviirSdaurJ.oc.

16. Rind, D&l.. et al. 1994. Effect of com01%-43p91pblso.00 + lS.00

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puter-based alerts on the treatment and outamIes of hosp&alked patients.

macy. the infectious disease division, and the microbiology laboratory. Di-

Arch. Intun. Med. 154:1511-1517.

agn. Microbial. Infect. Dis. 16255-264.

17. -tag, JJ., et al. 1993. Changes in antimicrobial agent usage resulting from interacticms among clinical phar-

18. Pestotnik, S.L., et al. 1990. Therapeutic antibiotic monitoring: surveillance using a computerized expert system. Am.

J. Med. 88:4w8. 19. Snow, P.B.,D.S. Smith, and WJ.Catalona 1994. Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study. J. Ural. 152:1923-1926.

Editorial

The Value of Human Papilloma Virus Typing Cynthia E. Flynn, M.D. Debra A. Bell, M.D. Department qf Pathology

hhwachu.w?~ General Hospital Boston, MA 02114

This editorial will review the currently available typing methods for human pap&ma virus (IlPV) and evaluate their clinical utility. HPV has been linked to anogenital (especially cervical) neoplasia in three ways. First, histologic and cytological correlations were observed between HPV infection (condyloma or koikzytosis) and cervical intraepithelial neoplasia (GIN) and invasive neoplasia. Second, specific I-IPV types were linked to neoplasia, leading to the definition of high-risk (I-WV16,18), low-risk (HPV 6,11,4244),andintermediate-lisk(HPv31,33, and35)types.Thethirdpieceofevidence istheinvitlodatathatdemonstratedthat HI’Vtypes 16,18,31,and33(butnot6 orll)GWinduceimmcnt&G&mand aneupbidy in cultnmd keratinocytes. The strong association between the presence of I-WV DNA and cervical neoplasia, coupled with the rapid development of molecular diagnostic technology, natumlly leads one to speculate about the possible clinical role of detection of HPV nucleic acids. HPV structure R@lomavirusesarespecies-specific, site-sPXilic,doublestranded,supercoiled DNA viruses of ap~xoximately 8 k&biWXett&kWdwithinaIticosahedral capsidprotein.Thevirusesinfecttheepithelialcellsand@icateintheirnt&i. SeventytypesofHWhavebeende8ned basedonDNAsequenceheterology(ap pmrimatelyonethkdof~havebeen described in the female genital tract). HPvviralgenesaredividedinto 70

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twofunctional groups, the early (E) and late Q genes. There are seven E genes, each with a different function. The oncogenic HPV subtypes are believed to work via the expression of E6 and E7 oncoproteins (combined with possible interaction with cellular oncogenes and tumor suppressor genes): viral DNA integration is also likely to play a role in this oncogenic mechanism. The two L genes encode structural viral proteins and are therefore required for productive viral infection. Epidemiology Each year more than one million women are diagnosed with clinically overt I-IW infection (genital warts); 2 to 3% of young women develop abnormal Papanicolaou (Pap) smear results and form HPV-related condylomata or cervical precancers. And it is estimated that 10% of women harbor HPV infection in their genital tracts with or without the presence of clinical disease. The evidence for type-specific HPVrelated disease demonstrates that HPV types 6 and 11 are almost exclusively associated with external or exophytic genital condylomata (Table 1). However, only 15 to 20% of flat condylomata of the cervix or mild dysplasias are associated with I-IW types 6 and ll,whereasrarevariantsoftypes6and 11 have been associated with squamous cancers. HW 16 is the viral type detected most frequently in high-grade CIN (50 to 70%) and invasive cancers (60%). However, HPV 16 is also found in 15 to 40% of low-grade cervical lesions, and HPV 16 is the most common I-WVtype found in women with normal cervical cytology. I-IPV 18 is the second most prevalent I-WV type found in high-grade CIN or invasive cancers. It is associated with 10 to 20% of cervical 8 1996 Elscvicr Science Inc.

cancers, including up to 60% of adenocarcinomas and 88% of small-cell carcinomas. HPV la-positive carcinomas have been associated with more aggressive clinical courses. I-IW types 16 and 18 have been called *‘high-risk viral types.“HPV types 31,33,35,45,51, 55,56, and 58 appear to have intermediate oncogenic potential and are found in approximately 30% of high-grade pmcursor lesions and 10% of invasive carcinomas. Low-grade squamous lesions appear more heterogenous in respect to their HPV associations. HPV infection (especially with highrisk types, i.e., 16 and 18) is the primary factor in the development of cervical neoplasia. However, in order for I-IW infection to progress to cervical cancer, it appears that the presence of other cofactors (such as smoking or hormonal agents) and/or relative immune-deficiency may be necessary. HPV Detection Techniques The standard microbiological methods of culture and serology are not available for papillomaviruses. Nondiagnostic clinical features, insensitive and non-specific cytopathic effects, and non-constitutive vital protein pioduction have combined to necessitate reliance on nucleic acid detection techniques in I-IPV-related investigations. The more commonly used methods, Southern blot, dot (slot) blot, and in situ hybridization, have been based on detection of DNA sequences homologous to HPV. More recently nucleic acid amplification techniques, such as the polymerase chain reaction (PCR), havebecome important tools in the evaluation of HPV-related disease. Southern blotting, one of the most common methods for the study of DNA, is performed using purified highCliid

Micmbiobgy Newsletter 18:9.1996