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Chapter 48 : WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents

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Abstract:

WHONET is a free informatics tool developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance in Boston for surveillance of bacterial infections and their resistance to antimicrobial agents. Since its inception in the 1960s as a set of mainframe-based routines, through the latest Web incarnation, WHONET has had two major objectives. The first is to analyze the reports of microbiology laboratories to delineate the specific problems with infections seen in health centers and the communities they serve. The aim is to provide infection control, antimicrobial stewardship committees, and public health professionals with the data they need to develop appropriate interventions to stem the spread of antimicrobial resistance.

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 1
FIGURE 1

Frequency distributions of inhibition zones of spp., spp., and spp. isolates from 1966 through 1969 around disks of penicillin, tetracycline, and chloramphenicol ( ). Isolates toward the right of each graph are susceptible, isolates to the far left possess high-level resistance, and isolates in the middle exhibit moderate or intermediate levels of resistance. Circled numbers represent the percent susceptible to the indicated antimicrobial. A comparison of the four species indicates that isolate subpopulations with similar levels of resistance exist across all four species but in very different proportions. (Reprinted from reference with permission.)

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 2
FIGURE 2

Frequency distributions of inhibition zones of from 1973 to 1974 around disks of penicillin, erythromycin, tetracycline, chloramphenicol, and streptomycin ( ). Boxed numbers represent the percent resistance (in contrast to Fig. 1 ) to the indicated antimicrobial. Histograms in the first row are from a health care facility in the United States and with the exception of penicillin exhibit very low rates of resistance. Histograms in the second row are from a facility in a European country with remarkably high rates of resistance, in approximately half or more of the tested isolates for four of the five antimicrobials shown. (Reprinted from reference with permission.)

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 3
FIGURE 3

The number of individuals by country registered on the WHONET website as of July 2013. The total number of registrations worldwide at that time was approximately 5,800. Not all registered individuals are WHONET users, and not all WHONET users are registered on the WHONET website, but the map reflects well the relative use of WHONET by country worldwide.

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 4
FIGURE 4

The WHONET data analysis control screen in which the user selects the type of analysis, organisms, and data files to include and isolate filters. In this example, the user has selected the %RIS and test measurements analysis for and from blood isolates collected in the intensive care unit for a January 1995 data file. Results are to be exported to an Excel file with the name provided.

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 5
FIGURE 5

WHONET scatterplot of MICs of ceftriaxone (CRO) and of ceftazidime (CAZ) for all isolates of at one hospital during 1 year. The circle in the lower left corner encloses the 3,489 isolates that had MICs of 0.5 µg/ml (susceptible) for both agents. The circle in the upper left corner encloses the five isolates that had an MIC of 0.5 µg/ml for ceftazidime (susceptible) and an MIC of 64 µg/ml for ceftriaxone (resistant). All five isolates were from patient A, who, as shown in the inserted table, had initially had a single urine and a month later urine and blood isolates with that unique-for-the-year combination of MICs. This patient had received a kidney transplant 3 months earlier on another continent, which illustrates how the systematic screening of phenotypes may detect the incursion of a foreign strain. (Reprinted from reference with permission.)

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 6
FIGURE 6

Antimicrobial resistance phenotype subtyping and tracking in space and time. Disk diffusion zone diameters (mm) around susceptibility test disks are listed in columns for each of 10 antibiotics tested at 76 medical centers using WHONET in Argentina. Dates have been modified from the original data submitted. Test results represent all 40 isolates of from 2008 to 2011 with a particular antibiotype. These isolates are highly resistant, susceptible to only IPM, CTX, and CAZ. Of the 76 medical centers, this phenotype was seen in only 19 centers from 12 provinces. Until November 2011, most facilities had only a single patient with this phenotype, and at most three. Then from November 2010 through June 2011, this phenotype was only seen in a single facility: hospital S, with 11 patients in an 8-month period, primarily in ICU patients. This variation in time and geographic distribution is striking and highlights the heterogeneity and poorly appreciated epidemiology of strain subpopulations, as well as opportunities for prevention of spread with prompt notifications in real time to health care providers, infection control staff, and public health authorities.

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 7
FIGURE 7

Collaborators in WHONET-Argentina utilized the SaTScan features in WHONET for real-time (weekly) detection of possible outbreaks of shigellosis ( ). (A) One such confirmed outbreak of nonsusceptible to trimethoprim/sulfamethoxazole, detected using antimicrobial resistance phenotypes as a strain marker, is presented, highlighting detection and investigation of the event associated with hospital HGC. (B) Strains were collected for PFGE typing, verifying the clonality of the strains associated with the cluster period. (Reprinted from reference with permission.)

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 8
FIGURE 8

The most recent version of WHONET takes advantage of the new feature in SaTScan to generate Google Earth-compatible KML files. The map indicates the location of two statistical clusters identified by SaTScan, each of which involves two health care facilities.

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 9
FIGURE 9

From a series of patients with multiple isolates of per patient tested against a standard set of 47 Vitek biochemicals, the left-hand graph compares the observed weighted average within-patient variance (lower points with black regression line) against the theoretical weighted average within-patient variance (upper red line) if there were no correlation between results of individual patient isolates. The right-hand graph plots the covariance parameter estimate obtained by generalized linear mixed-model variance component analysis using the weighted average within-patient variance ( ). In both analyses, GlyA, TyrA, and SUCT stand out as “nuisance” variables, exhibiting relatively higher levels of variability (less reproducibility) than other biochemicals. By excluding such variables from WHONET analyses, we have demonstrated improved ability to recognize phenotypic clonal populations and improved detection of possible outbreaks. (Reprinted from reference with permission.)

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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Image of FIGURE 10
FIGURE 10

The first two graphs display unprocessed MALDI-TOF signal spectra (horizontal axis is molecular mass, vertical axis is number of ions) obtained from the Bruker Biotyper from two isolates of from a WHONET-Argentina laboratory, highlighting significant differences between the two strains with regard to both peak numbers and peak heights. The third graph indicates a set of major “consensus” peaks common to both signal profiles, suggesting a set of signal peaks that may be uniquely indicative or probabilistically suggestive of . If one were to focus on the unique peaks of distinct strains, it may be possible to ascertain the clonality and phylogenetic relatedness of strains in real time.

Citation: Stelling J, O'brien T. 2016. WHONET: Software for Surveillance of Infecting Microbes and Their Resistance to Antimicrobial Agents, p 692-706. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch48
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References

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