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Clostridium difficile surveillance: harnessing new technologies to control transmission Expert Rev. Anti Infect. Ther. 11(11), 1193–1205 (2013)

David W Eyre and A Sarah Walker* NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK *Author for correspondence: [email protected]

Clostridium difficile surveillance allows outbreaks of cases clustered in time and space to be identified and further transmission prevented. Traditionally, manual detection of groups of cases diagnosed in the same ward or hospital, often followed by retrospective reference laboratory genotyping, has been used to identify outbreaks. However, integrated healthcare databases offer the prospect of automated real-time outbreak detection based on statistically robust methods, and accounting for contacts between cases, including those distant to the ward of diagnosis. Complementary to this, rapid benchtop whole genome sequencing, and other highly discriminatory genotyping, has the potential to distinguish which cases are part of an outbreak with high precision and in clinically relevant timescales. These new technologies are likely to shape future surveillance. KEYWORDS: Clostridium difficile • electronic medical record • genotyping • integrated healthcare database • outbreak • surveillance • whole genome sequencing

The principle focus of healthcare-associated infection surveillance is to reduce infections and improve patient safety [1]. Population-level surveillance traditionally provides data on incidence, patient outcomes including morbidity and mortality, and changes in the population at risk or infecting strains [2]. In contrast, surveillance within institutions allows outbreaks of cases clustered in time and space to be identified and further onward transmission prevented. Clostridium difficile infection (CDI) is a leading cause of healthcare-associated diarrhea [3]. Disease typically follows exposure to antibiotics [4], which disrupt the intestinal microbiome [5,6] and allow C. difficile to flourish. Transmission from symptomatic cases and their environment [7-9] is a preventable source of infection and the target of many surveillance and control measures [2,10]. The emergence and global spread of the fluroquinoloneresistant ribotype-027/NAP1/BI strain [11] has been associated with large institutional outbreaks of C. difficile in North America and Europe [12,13]. However, C. difficile is widely distributed in the environment [4,14] and recent www.expert-reviews.com

10.1586/14787210.2013.845987

work using whole genome sequencing (WGS) of a large cohort of cases showed that, in endemic settings following recommended infection control measures, most infections are likely to arise from diverse sources [15], with fewer than 25% of cases acquired from hospital-based contact with other cases [15-17]. Therefore, at an institutional level, C. difficile surveillance is required to detect transmission clusters against a background incidence of disease originating from sources other than symptomatic cases. Regional and national surveillance also needs to be able to identify emerging lineages (e.g., the recent rise in ribotype 078 cases [18]) and possible links between geographically dispersed isolates (e.g., recent apparent point-source outbreak caused by ribotype 244 across Australia [19]). Several new technologies have the potential to significantly enhance C. difficile surveillance at a local and national level. Both multi-locus variable number tandem repeat analysis (MLVA) [20,21] and bacterial WGS [15,21,22] offer enhanced discriminatory power over other genotyping techniques, allowing


2013 Informa UK Ltd

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transmission events to be identified with increased certainty. In a complementary approach, increased use of electronic patient records and integration of hospital databases across multiple departments [23] allow contacts between cases to be much more reliably established. This review discusses the impact of these new technologies on current surveillance activities, particularly at the level of individual institutions, and how they may shape surveillance activities in future. Defining CDI outbreaks

Outbreaks represent localized increases in the incidence of infection. To define CDI outbreaks requires a case definition and a method for determining when an increase in incidence has occurred. Widely used case definitions, classifications and appropriate denominators for incidence data are described in detail in [24]. Briefly, a case of CDI infection requires an appropriate clinical syndrome (clinically significant diarrhea, unformed stool taking the shape of its container, or toxic megacolon), plus evidence of the presence of toxigenic C. difficile, its toxin, or pseudomembranous colitis. CDI is classified by place of onset, healthcare facility (HCF) or community, and timing with regard to previous healthcare exposure. CDI onset >48 h after HCF admission and within 4 weeks of discharge is labeled HCF-associated, between 4 and 12 weeks postdischarge indeterminate (whether community onset or within 48 h of HCF admission), and >12 weeks following last HCF exposure, community-associated. There is not the same consensus around definitions for a C. difficile outbreak. For example, current Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA) clinical practice guidelines suggest, at a minimum, surveillance of HCF-onset, HCF-associated CDI should be conducted to detect outbreaks, but do not provide any specific guidance on how to define an outbreak [2]. Screening for outbreaks is often based on a relative increase in incidence or incidence reaching an absolute threshold, or both [201,202]. Screening thresholds such as two or more cases in a 28-day period on a ward [202] provide straightforward guides for infection control practitioners, but are likely only partially sensitive or specific. The advent of integrated electronic healthcare data offers the prospect of more sophisticated screening for outbreaks, using statistically robust methods [25], but with calculations undertaken in the background, such that systems remain simple to use for clinicians and infection control practitioners. For example, historical data can be used to determine the expected incidence of cases, if necessary accounting for varying rates of CDI with season [26-29], ward or hospital type [27], and trends over time [30]. The numbers of cases expected within a given time window, for example, a week or a month, is assumed to follow a statistical distribution, and extreme values incompatible with chance random variation can then be detected. Such approaches are typically based on regression methods, and it is within this framework that factors such as season, or hospital type can be accounted for [25]. However, they are also similar 1194

to some forms of statistical process control charts, for example, those used to detect faults in industrial production processes as they arise [31]. An example of this approach using monthly CDI incidence in a hospital is described here [32]; for a more detailed description see [31,33-35]. For infection control purposes, following the number of cases per month (or other time period) may allow retrospective identification of outbreaks, but may be too slow in some settings to guide intervention. One alternative is to follow the amount of time between successive cases [25,34]. Another approach, initially developed to detect new temporospatial clusters of cancer, uses a scan statistic [36], scanning the number of new cases in different time windows for unexpectedly high counts. A variable window size allows prospective analysis. This method has been applied to healthcare-associated infections in a hospital setting [37], illustrating the potential of integrated healthcare data to transform the ways that infection surveillance is delivered. Beyond surveillance based purely on incidence & location of testing

Surveillance methods based on incidence alone may fail to detect outbreaks because smaller outbreaks may be lost within the noise created by endemic disease, and not reach a large enough size to trigger alert thresholds. One tool to reduce the impact of ‘noise’ from background endemic disease is genotyping. Rather than considering the incidence of all CDI in a given hospital, unit or ward, the incidence of specific genotypes could be monitored. As the expected incidence of any one genotype is likely to be low, the sensitivity of any algorithm for outbreak detection is potentially increased. While it may be impossible to distinguish small variations in low numbers of a specific genotype that are genuinely due to in-hospital transmission versus chance alone, nevertheless genotyping can exclude the possibility of transmission when isolates are unrelated. The need for all isolates from a potential outbreak to share the same genotype forms part of CDI outbreak definitions in use in the UK [202]. However, genotyping is currently almost always undertaken retrospectively as a confirmatory step on isolates sent to a reference laboratory, for example, [38], after initial intervention. While this provides useful feedback for hospitals, the utility of genotyping would be greatly increased if it could be undertaken locally in clinically relevant timescales to allow it to contribute to the initial detection of an outbreak [22]. This is discussed further in following sections. In contrast, surveillance methods based on incidence and location of testing may fail to detect outbreaks because relevant prior exposures to CDI cases, that is, possible transmission events, at other locations may be missed. For example, SHEA/ IDSA guidelines state, “it is not known whether tracking of healthcare-acquired, community-onset CDI (i.e., post-discharge cases) is necessary to detect HCF outbreaks” [2]. FIGURE 1 illustrates this point using a cluster of cases described in [39]; five cases of multi-locus sequence type 63 occurred in a single geographic region within 9 weeks without any other case of the same genotype in the surrounding 3.5-year period. However, Expert Rev. Anti Infect. Ther. 11(11), (2013)

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A

Outpatient Hospital 3: Ward J Hospital 3: Ward I Hospital 3: Ward H Hospital 3: Ward G Hospital 3: Ward F Hospital 3: Ward E Hospital 3: Ward D Hospital 3: Ward C Hospital 3: Ward B Hospital 3: Ward A Hospital 2: Ward D Hospital 2: Ward C Hospital 2: Ward B Hospital 2: Ward A Hospital 1: Ward G Hospital 1: Ward F Hospital 1: Ward E Hospital 1: Ward D Hospital 1: Ward C Hospital 1: Ward B Hospital 1: Ward A 01 Feb 2009 01 Mar 2009

01 Apr 2009 01 May 2009

01 Jun 2009

01 Jul 2009

01 Aug 2009

Admissions and positive test dates Patient K

Patient L

Patient M

Patient N

Patient O

K

B

L ST63 M N O 1980

1990

2000

2010

Figure 1. Ward stays and whole genome data for 5 multi-locus sequence type 63 (ST63) cases. Panel (A) shows ward stays across three hospitals for five cases with ST63 CDI. Ward stays are shown as horizontal lines with capped ends. Enzyme immunoassay positive culture positive samples are shown as diamonds with the vertical placement indicating the location of testing. Although the first positive test in each case is in a different location, three cases can be linked by shared time and space on the same ward (Hospital 1: Ward D), and two cases by shared time and space on a different ward (Hospital 3: Wards G and H). Panel (B) shows a time-scaled ClonalFrame phylogenetic tree based on whole genome sequences obtained from the five cases. The blue bars indicate the 95% credibility interval for the calendar time of each internal node. Cases K, L, M are all genetically indistinguishable, and cases N and O have a most recent common ancestor compatible with transmission [39].

one sample was obtained after hospital discharge and the others were obtained in three separate hospitals, on four different wards. Therefore any ward or hospital-based surveillance system may have missed these cases, which were in fact confirmed as a pair and a triplet of cases consistent with transmission based on earlier shared time and space on the same wards and WGS of isolates [39]. Effective surveillance systems therefore not only need to monitor location-based incidence, but also need to be able to reconstruct contact networks between cases, who may move frequently between hospital wards in healthcare systems seeking to maximize bed occupancy [203]. The fact that longterm care facilities may be a source of new acquisitions in www.expert-reviews.com

recently discharged patients [40] complicates surveillance further. Such surveillance is possible providing laboratory information systems can be integrated with patient admission and ward movement data, increasingly possible with integrated healthcare systems. Sharing of data across different healthcare institutions (and ideally long-term care facilities) is required for optimal surveillance, especially in settings with multiple healthcare providers. Contacts between cases not recorded in healthcare databases may also hinder identification of outbreaks. Nevertheless, highly discriminatory genotyping, for example, using WGS, may allow such cases still to be linked and investigated using traditional field epidemiology [15]. 1195

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Although most routine surveillance is based on the results of testing of suspected cases, asymptomatic individuals [40-42], and the hospital [7] and wider environment [14] represent potentially important additional sources of C. difficile [15]. Quantification of the proportion cases attributable to asymptomatic individuals varies [41,42] and is difficult to determine for many environmental sources [14]. Therefore surveillance of these sources is not routinely recommended at present [2]. It may be possible in future to link sources more precisely to cases using WGS, but this remains a question for further research. Existing genotyping technologies

Multiple genotyping schemes exist for C. difficile that enable potential outbreaks to be identified against a background of endemic disease [43]. The most widely used schemes are polymerase chain reaction (PCR)-ribotyping [44] and pulsed-field gel electrophoresis (PFGE) [45] in Europe and North America, respectively. Other typing schemes include restriction endonuclease analysis (REA) [46] and the revised multi-locus sequence typing (MLST) scheme of Griffiths et al. [47]. The relative discriminatory power of these methods can be assessed using Simpson’s index of diversity (DI), a numeric index of discrimination based on the probability that any two unrelated strains sampled from the test population will be placed into different typing groups [48]. The index is dependent both on the performance of the typing scheme and the underlying population sampled. Data from three studies are presented in TABLE 1: Killgore et al. compared seven typing schemes across 42 isolates from Canada, The Netherlands, the UK and the USA [49]; Griffiths et al. [47] compared MLST and PCRribotyping within a collection of 102 UK clinical isolates; Tenover et al. compared 350 isolates from seven laboratories across North America [50]. PCR-ribotyping and the revised MLST scheme perform similarly [47]. The earlier, smaller study by Killgore et al. suggests that REA and PFGE may be more discriminating than PCRribotyping (and the original MLST scheme), however, this is in the context of a global strain collection, rather than routine clinical practice in a local area [49]. In contrast, a recent comparison of REA, PFGE and PCR-ribotyping suggests that although the three methods produce broadly congruent results, PCR-ribotyping offers the greatest level of discrimination [50]. With the exception of MLST [204], other schemes rely on interpretation of gel banding patterns and do not have a publically available central database. PCR ribotyping based on capillary gel electrophoresis improves accuracy and reproducibility [51], and efforts are also underway to develop a standard protocol and database [43]. New genotyping technology

Using typing schemes in widespread use, the data in TABLE 1 show 1–2 in every 10 randomly chosen pairs of cases will share the same genotype by chance, resulting in false-positive outbreak confirmation. This problem is exacerbated in populations 1196

where common genotypes account for much disease [30,52,53]. This has led to interest in typing approaches that can differentiate isolates of the NAP1/BI/027 lineage in particular [54-57]. MLVA and WGS are relatively new typing technologies that offer significant improvements over previous methods; in a recent study the chance of randomly chosen pairs of samples being sufficiently similar to be considered compatible with transmission was 3% using MLVA and 2% using WGS [21]. Both analyze different parts of the C. difficile genome and reflect different evolutionary processes. MLVA exploits variations in the copy number at 7 [20,57] or more [58,59] tandemrepeat loci to distinguish isolates. Differences between isolates are summarized by the sum of the absolute differences in repeat copy number at each locus, summed tandem-repeat difference. With WGS data, differences between isolates are usually determined using single nucleotide variants (SNVs)/single nucleotide polymorphisms found in the non-repetitive core genome, which accounts for approximately 80–90% of the approximately 4.1–4.3 million base pair C. difficile genome [22,39,60]. Repetitive regions are excluded, as short reads generated by current WGS technologies cannot be reliably assigned to the correct genome location when repetition makes more than one location plausible. Comparisons are restricted to core genome regions to exclude mobile genetic elements that are only variably present. For an organism such as C. difficile where recombination occurs at a low but non-trivial rate, it is also important to consider whether multiple SNVs identified are sufficiently clustered to plausibly represent a single recombination rather than multiple mutations [15,39]. In a head-to-head comparison, WGS had marginally more discriminatory power than MLVA, and had less intrinsic assay and within host variation; both were effective tools for outbreak investigation and had very similar findings when applied in 61 potential outbreaks [21]. WGS and MLVA had similar reagent costs, US$ 65 and US$ 42, respectively, and similar amounts of laboratory hands on time per sample (16 h/96 samples, ~10 min / sample) when run in large batches [21]. However, WGS also requires considerable setup costs. The application of WGS as a typing tool is discussed in more detail below, but many of the points made apply to the application of any new typing technology, including MLVA. Whole genome sequencing as a tool to exclude transmission in potential outbreaks

Both MLVA and WGS differ from categorical typing schemes like ribotyping and PFGE, as they grade the relatedness of isolates continuously. This has significant advantages, but also poses a challenge. Previously, with categorical genotyping, differing types were considered a sufficient criterion to exclude transmission [16,17]. Using WGS, if the number of SNV differences are sufficiently large, and not plausibly due to a single recombination event, it is still possible to determine that transmission is extremely unlikely to have occurred and for practical purposes to exclude transmission. An approximation to this approach has been taken in WGS outbreak studies of other Expert Rev. Anti Infect. Ther. 11(11), (2013)

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Table 1. Discriminatory performance of Clostridium difficile typing schemes. Typing scheme

DI

95% CI

Ref.

REA

0.93

0.88–0.98

[49]

PFGE

0.84

0.75–0.93

[49]

PCR-ribotyping

0.70

0.56–0.83

[49]

MLST (previous scheme)

0.70

0.56–0.83

[49]

MLST (Griffiths et al. scheme)

0.90

-

[47]

PCR-ribotyping

0.92

-

[47]

REA

0.78

0.76–0.81

[50]

PFGE

0.65

0.61–0.69

[50]

PCR-ribotyping

0.83

0.80–0.85

[50]

MLVA

0.97

-

[21]

WGS

0.98

-

[21]

DI: Simpson’s index of diversity, the probability of two randomly chosen samples having differing types. Comparisons of discrimination can be made within studies, but care should be taken when comparing values across studies as the underlying study population differs.

pathogens [61-63], where the low number of variants observed between outbreak strains has been contrasted with the larger diversity found in the wider bacterial population (environmental or unrelated carriage/case isolates). However, this approach leaves uncertainty around the plausible upper limit on diversity consistent with direct case-to-case transmission. In an epidemic setting, sequencing all strains from a particular outbreak may allow this limit to be estimated directly [64] as transmission from outside the outbreak can be excluded. However, with endemic disease, such as C. difficile, uncertainty surrounding which cases are involved in an outbreak means that the limit on variation consistent with transmission must be estimated indirectly. This requires the degree of assay variation (i.e., reproducibility), within-host diversity, short-term evolution and the genetic changes seen with transmission from one host to the next (e.g., as a result of evolutionary bottlenecks) to be understood. Data on within-host diversity and short-term evolution have been used to inform a number of outbreak studies, for example, involving Mycobacterium tuberculosis [65], Mycobacterium abscessus [66] and Staphylococcus aureus [22,67,68]. Several studies in C. difficile provide important context for interpreting WGS data in potential outbreaks. As has been undertaken with other typing methods, including MVLA [57,69], the reproducibility of WGS data has been assessed by repeatedly sequencing the same isolate. Two single SNV errors were observed in 180 replicate sequences, 1 error per 90 genomes sequenced [15]. However, unlike MLVA where numbers of repeats are calculated from a contiguous PCR product for a small number of genomic locations, WGS technology requires the genome to be assembled from thousands of variable quality short reads. While the power of WGS comes from multiple short reads covering each genomic location, there is intrinsic variability in the quality of each base in each read, and variable certainty surrounding the best location for each read in the www.expert-reviews.com

genome. This means that outputs must be filtered to remove variants that are likely to be a consequence only of underlying variability in WGS technology or misplacement in the genome, such that only well-supported “high quality" variants are analyzed. Therefore, reproducibility depends not only on the sequencing platform used, but also on the data processing pipeline. Although testing of replicate sequences has been included in relatively few bacterial WGS studies to date, there is a consensus that in future sequencing platforms and pipelines should conform to quality control standards, including measures of sequencing reproducibility. Two broad strategies are available for processing WGS data; reads can be aligned or mapped to a reference genome (using the known sequence to guide how to reconstruct the unknown sequence from the short reads), or assembled de novo (relying on similarities between overlapping reads to connect them together). The relative merits of both approaches are discussed in more detail elsewhere [70-72], but for the purposes of outbreak investigation, nearly all studies to date have used mapped data [15,22,39,62,63,65-68,73], as this provides multiple quality metrics, which facilitate filtering for high quality variants. Most current de novo assembly software produces fewer quality indicators to guide SNV identification, but other filtering approaches have been undertaken using this approach [64]. A potential limitation of referencebased mapping is that only variants that occur within sequence also found in the reference genome are identified. Therefore if the samples analyzed are very different to the reference used, discriminatory power may be lost, and metrics such as the rate of within host diversity or evolution may not be generalizable. If no pre-existing close reference exists, one can be generated using another platform [63], or a de novo assembly of one of the outbreak genomes can be made and all isolates mapped to this [74]. 1197

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To exclude transmission, rates of within-host diversity and evolution can be used to set upper bounds on the expected number of SNVs between transmitted isolates, given the time between the samples being obtained, based on the assumption that differences observed within a host over time will be similar to differences observed between two hosts in a transmission chain over the same timescale. For example, using the evolutionary model and rates obtained in [15], to obtain a prediction interval, ‡95% of transmitted isolates sampled up to 123 days apart are expected be between 0 and 2 SNVs different. Rates of short-term evolution and within-host diversity have been explored using serial samples from the patients with recurrent or on-going CDI [15,39], asymptomatic children [21] and an in vitro gut model of CDI [75]. Rates of short-term evolution have also been assessed using sampling dates and sequences from 027/NAP1/BI strains [11]. Estimated rates of evolution from these studies are similar at approximately 1–2 SNVs/genome/year. From sequencing multiple colonies from the same fecal sample [39], and serially sampled patients [15,21,39], average within-host diversity at a single timepoint is approximately 0.3 SNVs; as would be expected, minority variants are observed and either lost or reach fixation over time [39,75]. Whole genome sequencing to reconstruct transmission chains

Where no CDI is related within an upper bound on the number of SNVs plausibly consistent with transmission, given C. difficile is widely found in the environment [14] and asymptomatic individuals [41,76], these are a more plausible source. However, where the number of variants between isolates is plausibly consistent with transmission the continuous nature of SNVs can be exploited. Rather than simply using SNV differences, sequence data can be used directly to reconstruct evolutionary histories, using maximum likelihood [77] or Bayesian methods [78] (these methods are also able to ‘average over’ uncertainty that arises from occasional missing data in sequences produced by WGS). The topology of the phylogenetic tree generated can be used to reconstruct transmission chains by tracking the accumulation of changes over time and different hosts in a more refined way than is possible with SNV differences alone. However, as with all data, care must be taken to consider the degree of uncertainty surrounding the estimation of the phylogenetic tree; for example, using bootstrap information in a maximum likelihood analysis or the degree of posterior support for each node in a Bayesian phylogeny. It is worth noting that an ideal metric for reconstructing transmission chains would change monotonically with time, without significant back mutation. The extent to which the spore state in C. difficile changes rates of evolution, and hinders the ability to reconstruct the sequence of CDI transmission, is not clear, but could be investigated, for example, by serial sequencing of stored spores. Importantly, evolution should be slow enough that transmitted isolates are clearly related, but sufficiently fast relative to the rate of transmission for changes 1198

to accumulate in successive hosts. Given rates of C. difficile evolution, in a rapidly spreading outbreak, differences between transmitted isolates may be driven more by pre-existing withinhost diversity than evolution, and in fact successive isolates in a transmission chain are often likely to be indistinguishable. This is a concern that has also been raised with Mycobacterium tuberculosis [73], which evolves even more slowly [65] than C. difficile. However, with longer C. difficile outbreaks spanning several months, SNV changes are likely accumulate, and the stochastic nature of evolutionary events may occasionally help in shorter outbreaks too. Despite this, even with WGS, additional epidemiological data may be required to reconstruct the sequence of transmission accurately, and to assess the likelihood that transmission occurred from an un-sampled environmental or asymptomatic source. As such the principal benefit of current WGS technologies may be to separate out which strains are not part of a CDI outbreak, by discriminating these unambiguously across the population diversity present, rather than to reconstruct, unaided by epidemiological data, the precise chain of events within transmitted isolates. The extent to which evolutionary bottlenecks are important in the accumulation of SNVs during a CDI outbreak is currently uncertain. Although the infectious dose of C. difficile is very low [79], and number of colony forming units in stool is very high [80], onward transmission from cases, in the context of appropriate infection control, appears to be infrequent [15-17]. This may reflect the fact that each infection is established as a rare event and therefore by a single clone. This could in theory allow minority within-host variants to reach fixation more quickly, and raise the rate at which SNVs accumulate during an outbreak [81], although as yet there are no C. difficile data to support this hypothesis. Alternatively within-host diversity may be preserved across transmission, with multiple clones potentially present in a donor all initiating an infection in a recipient, but with restricted exposure to C. difficile in potential recipients the reason for limited onward spread. This observation would be consistent with the within-host diversity observed shortly after disease onset [15,21] and the relatively short incubation periods in many CDIs [7,8]. This is an area that merits further investigation in C. difficile and other pathogens. Additional considerations when using whole genome data

Although not extensively explored to date, incorporating other genetic variation, including short insertions and deletions, in comparisons between isolates could potentially increase the discriminatory power of WGS in outbreak investigations. Similarly, loss and gain of mobile genetic elements can be established from sequence data; this may also be associated with changes in anti-microbial resistance [82]. At present, the short read lengths from available WGS technologies are not sufficient to resolve all the repetitive variable number tandem repeat regions used in MLVA [21] and hence these are currently inaccessible with de novo or mapping WGS approaches, but the development of strand sequencing Expert Rev. Anti Infect. Ther. 11(11), (2013)

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Harnessing new technologies to control transmission

platforms [70] may make predicting MLVA from WGS, or also using these regions to compare isolates, tractable. Increased discriminatory power from analyzing genetic material other than SNVs may make reconstruction of outbreaks from genetic data more achievable, provided the additional information gained behaves predictably and as discussed earlier. For example, if mobile genetic elements can be lost and gained rapidly within a host during an outbreak, as suggested for S. aureus, then this would render them unusable for transmission analyses [22]. As an alternative to using SNVs across the genome and reconstructing outbreak phylogenies, a gene-by-gene strategy has also been proposed. This is analogous to a ‘super-MLST’ scheme, where a database of alleles for all genes is constructed and isolates are compared by the number of genes at which they differ [61,83]. While this approach has the attraction of rapid comparison of genomes (it scales linearly with each additional genome) and that specific lineages can be described in terms of combinations of allele identifiers, it only summarizes some of the information available from phylogenies based on core genome SNVs. Further, applications to date have been based on de novo assembled data, which is more challenging to filter accurately to ensure only high quality variants are included in the analysis. Undetected mixed infections can potentially complicate attempts to exclude transmission based on genotyping. If a mixed infection is present in either a transmission source or recipient and only one isolate is typed from each, it is possible that the genotypes obtained may differ despite an identical strain being present in both cases and possibly responsible for disease. Therefore transmission could be incorrectly excluded. C. difficile mixed infection rates have been estimated at around approximately 7–13% [84-88], suggesting that it has the potential to contributed to a substantial minority of transmission events without other sources. However, using a novel algorithm for detecting mixed infection in primary cultures using whole genome data, onward transmission from mixed infection appears to be uncommon [89]. Nevertheless, this algorithm still provides a useful tool where it is important to be certain that mixed infection has not contributed to transmission. Studies using whole genome data also need to actively consider the impact of recombination, which can introduce multiple SNVs between isolates in a single event, and therefore may be another reason why transmission could be erroneously excluded. Eleven percent of the first C. difficile whole genome sequenced consisted of mobile elements [60], and analysis of the evolutionary history of 15 C. difficile lineages over tens to hundreds of years using ClonalFrame [90] showed the relative number of substitutions introduced by recombination compared with mutation ranged from 0.03 to 7.49, with larger values tending to be seen in older lineages [39]. However, analysis over shorter periods, relevant to direct person-to-person transmission, suggests that such events are uncommon over shorter time scales [15,21]. www.expert-reviews.com

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New epidemiological data linkage technologies

Linkage of patient admission and ward movement data with microbiology results and highly discriminatory typing/ sequencing offers the potential for automated outbreak surveillance, that incorporates both the statistical methods described earlier, and contacts between patients occurring outside the ward of CDI diagnosis. Historically, different types of hospital data have been stored in separate databases, so that database management and linkage has been a major impediment to adopting these technologies. However, several commercial database systems incorporating both laboratory and admission data are also starting to provide this kind of functionality. Within the UK, two large academic hospital groups have developed integrated databases that combine microbiology and patient administration data [23,91], one of which is used to drive daily reporting of new CDI cases [91] and the other formed the basis for recently published retrospective studies of C. difficile transmission [15,16]. Linkage of such systems can also support more accurate assignment of classification of cases as healthcare or community associated [92]. Several studies have compared the performance of electronic surveillance systems with conventional methods for healthcare-associated infection outbreak detection based on incidence data. Most studies have focused on USA National Healthcare Safety Network defined healthcareassociated infections [93], and do not include CDI, but a small number have [94-97]. Generally, automated approaches match or exceed the sensitivity of conventional methods, with the additional benefit of greater efficiency and objectivity [98,99], but there are examples of reduced sensitivity [100]. The uptake of such methods varies across healthcare systems, for example, with 23% of 241 California hospitals using automated surveillance in 2008/2009 [101]. As nearly all CDI are in part defined by a positive laboratory result, electronic surveillance systems are likely to have good sensitivity for detecting cases. However C. difficile testing in the absence of an appropriate clinical syndrome, for example, following diarrhea caused by laxative use, may decrease specificity given relatively high rates of C. difficile colonization [102]. These tools are attractive as they use data already generated for routine patient care, and are therefore likely to be relatively inexpensive compared with conventional surveillance [103]. Importantly, electronic surveillance methods may also address a tendency to under-report events in the face of punitive consequences for institutions with high rates [104]. Genotyping and place of sampling data have been prospectively combined to identify Methicillin-resistant Staphylococcus aureus (MRSA) outbreaks, using an outbreak definition of two or more cases within 2 weeks on the same or closely connected wards, sharing a staphylococcal protein A (spa) type. This system detected outbreaks (as defined by a temporal-scan test statistic) with 100% sensitivity and 95% specificity [105]. The challenge is now to combine whole genome and epidemiological data prospectively. Tools that use WGS in conjunction with epidemiological information to reconstruct outbreaks are currently being developed [106,107]. At a population level, there are established tools for determining spatial spread of infection 1199

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New CDI case

24–48 h

Culture isolate Integrated healthcare database

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Screen for clustering based on all healthcare contacts Rapid benchtop sequencing

4–24 h

1–2 h

Potential outbreak

Isolated case

Sequenced genome available Confirmed isolated case

Genetically related case without recorded contact

Confirmed transmission

Transmission excluded

Figure 2. Outbreak surveillance technologies. A workflow for outbreak surveillance is shown using integrated healthcare data and whole genome sequencing. Each new case has an isolate sent for whole genome sequencing. Currently culture is usually required to obtain sufficient DNA for sequencing: however, this is likely to decrease substantially over coming years. While sequencing is undertaken an initial screen is carried out for clustering of the new case with previous cases. Ideally, this uses information on ward and hospitalbased contacts between the new case and previous cases, in addition to information on the ward of diagnosis. Whole genome sequencing data are likely to be available within 24–48 h and are used to refine initial findings. Where isolated cases are found to be highly genetically related to a previous case without observed hospital contact, near to real time contact tracing could be used to identify point source contamination (e.g., food, water), or investigate potential for transmission via unidentified third party, and thus identify additional avenues to improve C. difficile control and prevention.

alongside reconstructing phylogenies [108], which have been applied to the global spread of NAP1/BI/027 C. difficile [11]. Similar approaches can also be applied to track spread of infection between hosts or ecological niches. The development of integrated solutions that combine healthcare databases with sequencing data, and perform and present transmission analyses is currently a major focus for many academic and national institutions, and healthcare providers. Expert commentary & five-year view

Integrated healthcare databases that include microbial sequencing data have the potential to transform surveillance of infectious diseases. Rather than hospital information systems acting as a passive repository for data, these data can act as an intelligent healthcare records system that prospectively detects outbreaks. Within the next 5–10 years, hospital benchtop sequencing is likely to become the standard tool in outbreak investigation; results are likely to be available within hours of a positive culture result [22,71]. C. difficile genotyping and WGS currently depend on obtaining cultured isolates for testing. This is currently a barrier to wider use of typing/WGS, with most hospital laboratories not routinely undertaking C. difficile culture. However, approaches to undertake sequencing directly on clinical specimens are in development [109]. 1200

Integrated healthcare databases and WGS are likely to be applicable across a range of pathogens transmitted within healthcare systems [71], and may be of particular relevance in combating the spread of organisms with extensive antimicrobial resistance [110]. They may also be of considerable use in determining the spread of food-borne infections in the community [111,112]. However C. difficile also remains a substantial problem within many healthcare facilities, both via outbreaks, and in endemic settings with a significant minority of cases the result of preventable transmission from other cases [15]. Applying these technologies to C. difficile, a typical workflow (FIGURE 2) might include, an initial positive CDI result that triggers automated alerts to ward staff, medical teams and infection control services. Isolated cases may generate one level of alert, while clusters of cases, detected with statistically robust surveillance incorporating all ward movements over the preceding 3–6 months, generate a higher level of alert. In the hours before definitive sequence data are available, using hospital admission and ward movement data, analysis of potential ward and hospital wide contacts between the new case and previous cases will be undertaken, providing an early warning of possible on-going sources of transmission. Whole genome data will then refine estimates of the likely source. Where several possible sources of infection exist, probabilistic transmission models may be required to rank the plausibility of different Expert Rev. Anti Infect. Ther. 11(11), (2013)

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Harnessing new technologies to control transmission

transmission sources and routes, which can be updated rapidly with new data, and with outputs summarized and presented in a way appropriate for routine clinical use. Automated surveillance tools will also provide a tool for monitoring institutional and regional infection control performance, which should improve the accuracy and fairness of national surveillance programs [113]. Monitoring the incidence of genetically related cases consistent with transmission will provide more accurate estimates of healthcare-associated transmission than current crude incidence measures. However, monitoring of overall incidence may still be important for assessing the impact of control measures such as antimicrobial stewardship that prevent the transition from exposure to disease. Additionally sequencing of surveillance isolates, combined with data linkage at a national and international level (as has been suggested for monitoring anti-microbial resistance [114]), also allows comparison of the strains causing disease across populations [53] and hosts [14,115] and may provide insights into emerging threats and previously unrecognized sources of transmission, which can then be targeted with specific interventions.

Review

Overall, the huge technological progress in microbial sequencing, data integration and computational power, provides an unprecedented opportunity for intelligent systems that monitor and detect infectious disease outbreaks. There is now a need for standards and integrated software solutions to deliver this promise to routine patient care. Financial & competing interests disclosure

This study was supported by the NIHR Oxford Biomedical Research Centre and the UKCRC Modernising Medical Microbiology Consortium, the latter funded under the UKCRC Translational Infection Research Initiative supported by Medical Research Council, Biotechnology and Biological Sciences Research Council and the National Institute for Health Research on behalf of the Department of Health (Grant G0800778) and the Wellcome Trust (Grant 087646/Z/08/Z). We acknowledge the support of Wellcome Trust core funding (Grant 090532/Z/09/Z). DW Eyre is a NIHR Doctoral Research Fellow. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Key Issues • Clostridium difficile surveillance allows healthcare-associated outbreaks to be detected, and further onward transmission prevented • Traditionally manual review of each new case by infection control practitioners has been used to identify cases clustered in time • •

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• • •

and space; clustering in space is typically assessed based on the patient’s current location or the location of testing Straightforward outbreak screening definitions, such as two cases per ward in two weeks, may provide a guide, but are likely to be only partially sensitive and specific The advent of integrated electronic healthcare records offers the prospect of more sophisticated screening for outbreaks, using statistically robust methods, but with calculations undertaken in the background, such that systems remain simple to use for clinicians and infection control practitioners Systems integrating microbiological results and patient movement data can also account for contacts between patients occurring before diagnosis and at a different location to the place of diagnosis, for example, in cases subsequently diagnosed in the community or on a different ward to plausible transmission contacts Genotyping allows transmission to be excluded between genetically distinct isolates; whole genome sequencing is able to exclude transmission with greater precision than many previous genotyping techniques Whole genome sequencing in combination with epidemiological data may also assist in reconstructing transmission chains Technological advances in microbial sequencing, data integration and computational power, provide an unprecedented opportunity for intelligent systems that monitor and detect infectious disease outbreaks; there is now a need for standards and integrated software solutions to deliver this promise to routine patient care

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