Whole-genome sequencing reveals nosocomial Clostridioides difficile transmission and a previously unsuspected epidemic scenario

Impact of an Intervention Program on Clostridioides difficile Infections: Comparison of 2 Hospital Cohorts

Background

Clostridioides difficile infection (CDI) occurs in various contexts and care settings and is managed by multiple specialists who are not experts in its management. While there are many initiatives to improve the diagnosis and avoid overdiagnosis, there is less focus on the overall management of the infection.

Methods

We studied a cohort of patients with a positive test result for toxigenic C difficile in 2 hospitals. Hospital A has a program that provides advice from an infectious disease specialist (IDS) and promotes continuity of care by providing a phone number to contact the IDS. Hospital B does not have any specific CDI program. The evaluation assessed the proportion of patients not treated (carriers or self-limited disease), adherence to Infectious Diseases Society of America guidelines, access to novel therapies, recurrence and mortality rates, and readmission and emergency department visits due to CDI. We assessed the program's effectiveness through a logistic regression model adjusted for covariates chosen by clinical criteria.

Results

Hospital A avoided more unnecessary treatments (19.3% vs 11.5%), provided access to novel therapies more frequently (35.3% vs 13%), and adhered more closely to current guidelines (95.8% vs 71.3%). Although the mortality and recurrence rates did not differ, the absence of an intervention program was associated with greater odds of admission due to recurrence (odds ratio, 4.19; P = .037) and more visits to the emergency department due to CDI (odds ratio, 8.74; P = .001).

Conclusions

Implementation of a CDI intervention program based on recommendations from IDSs and improved access to specialized care during the follow-up is associated with enhanced quality of CDI management and potential reductions in hospital resource utilization.

Early identification of a ward-based outbreak of Clostridioides difficile using prospective multilocus sequence type-based Oxford Nanopore genomic surveillance

OBJECTIVE

To describe an outbreak of sequence type (ST)2 Clostridioides difficile infection (CDI) detected by a recently implemented multilocus sequence type (MLST)-based prospective genomic surveillance system using Oxford Nanopore Technologies (ONT) sequencing.

SETTING

Hemato-oncology ward of a public tertiary referral centre.

METHODS

From February 2022, we began prospectively sequencing all C. difficile isolated from inpatients at our institution on the ONT MinION device, with the output being an MLST. Bed-movement data are used to construct real-time ST-specific incidence charts based on ward exposures over the preceding three months.

RESULTS

Between February and October 2022, 76 of 118 (64.4%) CDI cases were successfully sequenced. There was wide ST variation across cases and the hospital, with only four different STs being seen in >4 patients. A clear predominance of ST2 CDI cases emerged among patients with exposure to our hemato-oncology ward between May and October 2022, which totalled ten patients. There was no detectable rise in overall CDI incidence for the ward or hospital due to the outbreak. Following a change in cleaning product to an accelerated hydrogen peroxide wipe and several other interventions, no further outbreak-associated ST2 cases were detected. A retrospective phylogenetic analysis using original sequence data showed clustering of the suspected outbreak cases, with the exception of two cases that were retrospectively excluded from the outbreak.

CONCLUSIONS

Prospective genomic surveillance of C. difficile using ONT sequencing permitted the identification of an outbreak of ST2 CDI that would have otherwise gone undetected.

Defining the phylogenetics and resistome of the major Clostridioides difficile ribotypes circulating in Australia

Clostridioides difficile infection (CDI) remains a significant public health threat globally. New interventions to treat CDI rely on an understanding of the evolution and epidemiology of circulating strains. Here we provide longitudinal genomic data on strain diversity, transmission dynamics and antimicrobial resistance (AMR) of C. difficile ribotypes (RTs) 014/020 (n=169), 002 (n=77) and 056 (n=36), the three most prominent C. difficile strains causing CDI in Australia. Genome scrutiny showed that AMR was uncommon in these lineages, with resistance-conferring alleles present in only 15/169 RT014/020 strains (8.9 %), 1/36 RT056 strains (2.78 %) and none of 77 RT002 strains. Notably, ~90 % of strains were resistant to MLSB agents in vitro, but only ~5.9 % harboured known resistance alleles, highlighting an incongruence between AMR genotype and phenotype. Core genome analyses revealed all three RTs contained genetically heterogeneous strain populations with limited evidence of clonal transmission between CDI cases. The average number of pairwise core genome SNP (cgSNP) differences within each RT group ranged from 23.3 (RT056, ST34, n=36) to 115.6 (RT002, ST8, n=77) and 315.9 (RT014/020, STs 2, 13, 14, 49, n=169). Just 19 clonal groups (encompassing 40 isolates), defined as isolates differing by ≤2 cgSNPs, were identified across all three RTs (RT014/020, n=14; RT002, n=3; RT056, n=2). Of these clonal groups, 63 % (12/19) comprised isolates from the same Australian State and 37 % (7/19) comprised isolates from different States. The low number of plausible transmission events found for these major RTs (and previously documented populations in animal and environmental sources/reservoirs) points to widespread and persistent community sources of diverse C. difficile strains as opposed to ongoing nationwide healthcare outbreaks dominated by a single clone. Together, these data provide new insights into the evolution of major lineages causing CDI in Australia and highlight the urgent need for enhanced surveillance, and for public health interventions to move beyond the healthcare setting and into a One Health paradigm to effectively combat this complex pathogen.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Comparative Genomics of Clostridioides difficile

Clostridioides difficile, a Gram-positive spore-forming anaerobic bacterium, has rapidly emerged as the leading cause of nosocomial diarrhoea in hospitals. The availability of large numbers of genome sequences, mainly due to the use of next-generation sequencing methods, has undoubtedly shown their immense advantages in the determination of C. difficile population structure. The implementation of fine-scale comparative genomic approaches has paved the way for global transmission and recurrence studies, as well as more targeted studies, such as the PaLoc or CRISPR/Cas systems. In this chapter, we provide an overview of recent and significant findings on C. difficile using comparative genomic studies with implications for epidemiology, infection control and understanding of the evolution of C. difficile.

A novel approach to correcting attribution of Clostridioides difficile in a healthcare setting

Objective

To describe a novel attribution metric estimating the causal source location of healthcare-associated Clostridioides difficile and compare it with the current US National Healthcare Safety Network (NHSN) surveillance reporting standard.

Design

Quality improvement study.

Setting

Two acute care facilities.

Methods

A novel attribution metric assigned days of attribution to locations where patients were located for 14 days before and the day of their C. difficile diagnosis. We correlated the NHSN-assigned unit attribution with the novel attribution measure and compared the proportion of attribution assigned to inpatient units.

Results

During a 30-month period, there were 727 NHSN C. difficile healthcare-associated infections (HAIs) and 409 non-HAIs; the novel metric attributed 17,034 days. The correlation coefficients for NHSN and novel attributions among non-ICU units were 0.79 (95% CI, 0.76-0.82) and 0.74 (95% CI, 0.70-0.78) and among ICU units were 0.70 (95% CI, 0.63-0.76) and 0.69 (95% CI, 0.60-0.77) at facilities A and B, respectively. The distribution of difference in percent attribution showed higher inpatient unit attribution using NHSN measure than the novel attribution metric: 38% of ICU units and 15% of non-ICU units in facility A, and 20% of ICU units and 25% of non-ICU units in facility B had a median difference >0; no inpatient units showed a greater attribution using the novel attribution metric.

Conclusion

The novel attribution metric shifts attribution from inpatient units to other settings and correlates modestly with NHSN methodology of attribution. If validated, the attribution metric may more accurately target C. difficile reduction efforts.

A local-scale One Health genomic surveillance of Clostridioides difficile demonstrates highly related strains from humans, canines, and the environment

Although infections caused by Clostridioides difficile have historically been attributed to hospital acquisition, growing evidence supports the role of community acquisition in C. difficile infection (CDI). Symptoms of CDI can range from mild, self-resolving diarrhoea to toxic megacolon, pseudomembranous colitis, and death. In this study, we sampled C. difficile from clinical, environmental, and canine reservoirs in Flagstaff, Arizona, USA, to understand the distribution and transmission of the pathogen in a One Health framework; Flagstaff is a medium-sized, geographically isolated city with a single hospital system, making it an ideal site to characterize genomic overlap between sequenced C. difficile isolates across reservoirs. An analysis of 562 genomes from Flagstaff isolates identified 65 sequence types (STs), with eight STs being found across all three reservoirs and another nine found across two reservoirs. A screen of toxin genes in the pathogenicity locus identified nine STs where all isolates lost the toxin genes needed for CDI manifestation (tcdB, tcdA), demonstrating the widespread distribution of non-toxigenic C. difficile (NTCD) isolates in all three reservoirs; 15 NTCD genomes were sequenced from symptomatic, clinical samples, including two from mixed infections that contained both tcdB+ and tcdB- isolates. A comparative single nucleotide polymorphism (SNP) analysis of clinically derived isolates identified 78 genomes falling within clusters separated by ≤2 SNPs, indicating that ~19 % of clinical isolates are associated with potential healthcare-associated transmission clusters; only symptomatic cases were sampled in this study, and we did not sample asymptomatic transmission. Using this same SNP threshold, we identified genomic overlap between canine and soil isolates, as well as putative transmission between environmental and human reservoirs. The core genome of isolates sequenced in this study plus a representative set of public C. difficile genomes (n=136), was 2690 coding region sequences, which constitutes ~70 % of an individual C. difficile genome; this number is significantly higher than has been published in some other studies, suggesting that genome data quality is important in understanding the minimal number of genes needed by C. difficile. This study demonstrates the close genomic overlap among isolates sampled across reservoirs, which was facilitated by maximizing the genomic search space used for comprehensive identification of potential transmission events. Understanding the distribution of toxigenic and non-toxigenic C. difficile across reservoirs has implications for surveillance sampling strategies, characterizing routes of infections, and implementing mitigation measures to limit human infection.

Particular genomic and virulence traits associated with preterm infant-derived toxigenic Clostridium perfringens strains

Clostridium perfringens is an anaerobic toxin-producing bacterium associated with intestinal diseases, particularly in neonatal humans and animals. Infant gut microbiome studies have recently indicated a link between C. perfringens and the preterm infant disease necrotizing enterocolitis (NEC), with specific NEC cases associated with overabundant C. perfringens termed C. perfringens-associated NEC (CPA-NEC). In the present study, we carried out whole-genome sequencing of 272 C. perfringens isolates from 70 infants across 5 hospitals in the United Kingdom. In this retrospective analysis, we performed in-depth genomic analyses (virulence profiling, strain tracking and plasmid analysis) and experimentally characterized pathogenic traits of 31 strains, including 4 from CPA-NEC patients. We found that the gene encoding toxin perfringolysin O, pfoA, was largely deficient in a human-derived hypovirulent lineage, as well as certain colonization factors, in contrast to typical pfoA-encoding virulent lineages. We determined that infant-associated pfoA+ strains caused significantly more cellular damage than pfoA- strains in vitro, and further confirmed this virulence trait in vivo using an oral-challenge C57BL/6 murine model. These findings suggest both the importance of pfoA+ C. perfringens as a gut pathogen in preterm infants and areas for further investigation, including potential intervention and therapeutic strategies.

Whole Genome Sequencing Evidences High Rates of Relapse in Clostridioides difficile Infection Caused by the Epidemic Ribotype 106

An increasing prevalence and spread of Clostridioides difficile infection (CDI) caused by DH/NAP11/106/ST-42 has been observed worldwide, probably fostered by its great capacity to produce spores or by the higher resistance rates observed for some strains. Based on the results of our previous study where RT106 showed higher recurrence rates than other relevant ribotypes, a genetic analysis by whole-genome sequencing (WGS) of primary and recurrent RT106 isolates from ten patients was performed to determine whether the higher rate of recurrence associated with RT106 is due to relapses, caused by the same strain, or reinfections, caused by different strains. MLST profiles, resistance mutations, and phylogenetic relatedness were determined by comparative single nucleotide variant (SNV) analysis. All isolates were classified as ST42, and those belonging to the same patient were isogenic, with one exception; strains belonging to different patients were not with two exceptions, pointing to putative transmission events. Phylogenetic analysis also suggested the presence of similar local epidemic lineages associated with moxifloxacin resistance, except for one patient whose isolates clustered with different nonresistant US strains. Our results show that recurrent CDIs caused by RT06/ST42 are mainly due to relapses caused by the primary strains, showing the higher capacity of RT106/ST42 to persist and cause recurrences as compared to other ribotypes.

Molecular epidemiology of Clostridioides difficile in companion animals: Genetic overlap with human strains and public health concerns

Introduction

The changing epidemiology of Clostridioides difficile reflects a well-established and intricate community transmission network. With rising numbers of reported community-acquired infections, recent studies tried to identify the role played by non-human reservoirs in the pathogen's transmission chain. This study aimed at describing the C. difficile strains circulating in canine and feline populations, and to evaluate their genetic overlap with human strains to assess the possibility of interspecies transmission.

Methods

Fecal samples from dogs (n = 335) and cats (n = 140) were collected from two populations (group A and group B) in Portugal. C. difficile isolates were characterized for toxigenic profile and PCR-ribotyping. The presence of genetic determinants of antimicrobial resistance was assessed in all phenotypically resistant isolates. To evaluate the genetic overlap between companion animals and human isolates from Portugal, RT106 (n = 42) and RT014/020 (n = 41) strains from both sources were subjected to whole genome sequencing and integrated with previously sequenced RT106 (n = 43) and RT014/020 (n = 142) genomes from different countries. The genetic overlap was assessed based on core-single nucleotide polymorphism (SNP) using a threshold of 2 SNP.

Results

The overall positivity rate for C. difficile was 26% (76/292) in group A and 18.6% (34/183) in group B. Toxigenic strains accounted for 50% (38/76) and 52.9% (18/34) of animal carriage rates, respectively. The most prevalent ribotypes (RT) were the toxigenic RT106 and RT014/020, and the non-toxigenic RT010 and RT009. Antimicrobial resistance was found for clindamycin (27.9%), metronidazole (17.1%) and moxifloxacin (12.4%), associated with the presence of the ermB gene, the pCD-METRO plasmid and point mutations in the gyrA gene, respectively. Both RT106 and RT014/020 genetic analysis revealed several clusters integrating isolates from animal and human sources, supporting the possibility of clonal interspecies transmission or a shared environmental contamination source.

Discussion

This study shows that companion animals may constitute a source of infection of toxigenic and antimicrobial resistant human associated C. difficile isolates. Additionally, it contributes with important data on the genetic proximity between C. difficile isolates from both sources, adding new information to guide future work on the role of animal reservoirs in the establishment of community associated transmission networks and alerting for potential public health risk.

Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary

BACKGROUND

Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings.

METHODS

We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021.

RESULTS

Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways.

CONCLUSIONS

WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.

Risk for Asymptomatic Household Transmission of Clostridioides difficile Infection Associated with Recently Hospitalized Family Members

We evaluated whether hospitalized patients without diagnosed Clostridioides difficile infection (CDI) increased the risk for CDI among their family members after discharge. We used 2001–2017 US insurance claims data to compare monthly CDI incidence between persons in households with and without a family member hospitalized in the previous 60 days. CDI incidence among insurance enrollees exposed to a recently hospitalized family member was 73% greater than enrollees not exposed, and incidence increased with length of hospitalization among family members. We identified a dose-response relationship between total days of within-household hospitalization and CDI incidence rate ratio. Compared with persons whose family members were hospitalized <1 day, the incidence rate ratio increased from 1.30 (95% CI 1.19–1.41) for 1–3 days of hospitalization to 2.45 (95% CI 1.66–3.60) for >30 days of hospitalization. Asymptomatic C. difficile carriers discharged from hospitals could be a major source of community-associated CDI cases.

A review of epidemiological models of Clostridioides difficile transmission and control (2009-2021)

Clostridioides difficile is the leading cause of infectious diarrhea and one of the most common healthcare-acquired infections worldwide. We performed a systematic search and a bibliometric analysis of mathematical and computational models for Clostridioides difficile transmission. We identified 33 publications from 2009 to 2021. Models have underscored the importance of asymptomatic colonized patients in maintaining transmission in health-care settings. Infection control, antimicrobial stewardship, active testing, and vaccination have often been evaluated in models. Despite active testing and vaccination being not currently implemented, they are the most commonly evaluated interventions. Some aspects of C. difficile transmission, such community transmission and interventions in health-care settings other than in acute-care hospitals, remained less evaluated through modeling.

Computational approaches to understanding Clostridioides difficile metabolism and virulence

The progress of infection by Clostridioides difficile is strongly influenced by metabolic cues it encounters as it colonizes the gastrointestinal tract. Both colonization and regulation of virulence have a multi-factorial interaction between host, microbiome, and gene expression cascades. While these connections with metabolism have been understood for some time, many mechanisms of control have remained difficult to directly assay due to high metabolic variability among C. difficile isolates and difficult genetic systems. Computational systems offer a means to interrogate structure of complex or noisy datasets and generate useful, tractable hypotheses to be tested in the laboratory. Recently, in silico techniques have provided powerful insights into metabolic elements of C. difficile infection ranging from virulence regulation to interactions with the gut microbiota. In this review, we introduce and provide context to the methods of computational modeling that have been applied to C. difficile metabolism and virulence thus far. The techniques discussed here have laid the foundation for future multi-scale efforts aimed at understanding the complex interplay of metabolic activity between pathogen, host, and surrounding microbial community in the regulation of C. difficile pathogenesis.

Pathobionts: mechanisms of survival, expansion, and interaction with host with a focus on Clostridioides difficile

Pathobionts are opportunistic microbes that emerge as a result of perturbations in the healthy microbiome due to complex interactions of various genetic, exposomal, microbial, and host factors that lead to their selection and expansion. Their proliferations can aggravate inflammatory manifestations, trigger autoimmune diseases, and lead to severe life-threatening conditions. Current surge in microbiome research is unwinding these complex interplays between disease development and protection against pathobionts. This review summarizes the current knowledge of pathobiont emergence with a focus on Clostridioides difficile and the recent findings on the roles of immune cells such as iTreg cells, Th17 cells, innate lymphoid cells, and cytokines in protection against pathobionts. The review calls for adoption of innovative tools and cutting-edge technologies in clinical diagnostics and therapeutics to provide insights in identification and quantification of pathobionts.

Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection

BACKGROUND

Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively.

METHODS

We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared to traditional IP practice during the same period.

RESULTS

Of 3,165 isolates, there were 2,752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates were identified by WGS; clusters ranged from 2-14 patients. At least one transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real-time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192,408 - $692,532.

CONCLUSION

EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.

Unraveling a Nosocomial Outbreak of COVID-19: The Role of Whole-Genome Sequence Analysis

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic poses many epidemiological challenges. The investigation of nosocomial transmission is usually performed via thorough investigation of an index case and subsequent contact tracing. Notably, this approach has a subjective component, and there is accumulating evidence that whole-genome sequencing of the virus may provide more objective insight.

METHODS

We report a large nosocomial outbreak in 1 of the medicine departments in our institution. Following intensive epidemiological investigation, we discovered that 1 of the patients involved was suffering from persistent COVID-19 while initially thought to be a recovering patient. She was therefore deemed to be the most likely source of the outbreak. We then performed whole-genome sequencing of the virus of 14 infected individuals involved in the outbreak.

RESULTS

Surprisingly, the results of whole-genome sequencing refuted our initial hypothesis. A phylogenetic tree of the samples showed multiple introductions of the virus into the ward, 1 of which led to a cluster of 10 of the infected individuals. Importantly, the results pointed in the direction of a specific index patient that was different from the 1 that arose from our initial investigation.

CONCLUSIONS

These results underscore the important added value of using whole-genome sequencing in epidemiological investigations as it may reveal unexpected connections between cases and aid in understanding transmission dynamics, especially in the setting of a pandemic where multiple possible index cases exist simultaneously.

Evaluation of a Combined Multilocus Sequence Typing and Whole-Genome Sequencing Two-Step Algorithm for Routine Typing of Clostridioides difficile

Multilocus sequence typing (MLST) is a low-resolution but rapid genotyping method for Clostridioides difficile Whole-genome sequencing (WGS) has emerged as the new gold standard for C. difficile typing, but cost and lack of standardization still limit broad utilization. In this study, we evaluated the potential to combine the portability of MLST with the increased resolution of WGS for a cost-saving approach to routine C. difficile typing. C. difficile strains from two New York City hospitals (hospital A and hospital B) were selected. WGS single-nucleotide polymorphism (wgSNP) was performed using established methods. Sequence types (ST) were determined using PubMLST, while wgSNP analysis was performed using the Bionumerics software. An additional analysis of a subset of data (hospital A) was made comparing the Bionumerics software to the CosmosID pipeline. Cost and turnaround time to results were compared for the algorithmic approach of MLST followed by wgSNP versus direct wgSNP. Among the 202 C. difficile isolates typed, 91% (n = 185/203) clustered within the representative ST, showing a high agreement between MLST and wgSNP. While clustering was similar between the Bionumerics and CosmosID pipelines, large differences in the overall number of SNPs were noted. A two-step algorithm for routine typing results in significantly lower cost than routine use of WGS. Our results suggest that using MLST as a first step in routine typing of C. difficile followed by WGS for MLST concordant strains is a less technically demanding, cost-saving approach for performing C. difficile typing than WGS alone without loss of discriminatory power.

Whole-genome sequencing of clinical Clostridioides difficile isolates reveals molecular epidemiology and discrepancies with conventional laboratory diagnostic testing

BACKGROUND

The high clinical burden of Clostridioides difficile infections merits rapid and sensitive identification of affected individuals. However, effective diagnosis remains challenging. Current best practice guidelines recommend molecular and/or direct toxin detection-based screening for symptomatic individuals, but previous work has called into question the concordance and performance of extant clinical assays.

AIM

To better correlate the genomic and phenotypic properties of clinical C. difficile isolates with laboratory testing outcomes in both C. difficile-infected patients and asymptomatic carriers.

METHODS

Whole-genome sequencing of clinical C. difficile isolates collected from an inpatient population at a single healthcare institution was performed, enabling examination of their molecular epidemiology and toxigenic gene content. Genomic findings were compared with clinical testing outcomes, identifying multiple diagnostic discrepancies.

FINDINGS

Toxigenic culture, considered a 'reference standard', provided perfect sensitivity and specificity in predicting toxigenic gene content, whereas reduced performance was observed for Simplexa C. difficile Direct Assay (100% specificity, 88% sensitivity), Gene Xpert CD/Epi Assay (86% specificity, 83% sensitivity), and Quick Check Complete Tox A/B (100% specificity, 30% sensitivity). Genomic analysis additionally revealed variability in toxin gene sequences among C. difficile strains, phylogenomic equivalency between isolates from affected patients and carriers, and patient carriage with uncommon environmentally derived C. difficile lineages, as well as presenting opportunities for tracing pathogen transmission events.

CONCLUSION

These results highlight the variable performance of clinical stool-based testing approaches as well as the potential diagnostic utility of whole-genome sequencing as an alternative to conventional testing algorithms.

Impact of the coronavirus disease 2019 (COVID-19) pandemic on nosocomial Clostridioides difficile infection

OBJECTIVES

The coronavirus disease 2019 (COVID-19) pandemic has induced a reinforcement of infection control measures in the hospital setting. Here, we assess the impact of the COVID-19 pandemic on the incidence of nosocomial Clostridioides difficile infection (CDI).

METHODS

We retrospectively compared the incidence density (cases per 10,000 patient days) of healthcare-facility-associated (HCFA) CDI in a tertiary-care hospital in Madrid, Spain, during the maximum incidence of COVID-19 (March 11 to May 11, 2020) with the same period of the previous year (control period). We also assessed the aggregate in-hospital antibiotic use (ie, defined daily doses [DDD] per 100 occupied bed days [BD]) and incidence density (ie, movements per 1,000 patient days) of patient mobility during both periods.

RESULTS

In total, 2,337 patients with reverse transcription-polymerase chain reaction-confirmed COVID-19 were admitted to the hospital during the COVID-19 period. Also, 12 HCFA CDI cases were reported at this time (incidence density, 2.68 per 10,000 patient days), whereas 34 HCFA CDI cases were identified during the control period (incidence density, 8.54 per 10,000 patient days) (P = .000257). Antibiotic consumption was slightly higher during the COVID-19 period (89.73 DDD per 100 BD) than during the control period (79.16 DDD per 100 BD). The incidence density of patient movements was 587.61 per 1,000 patient days during the control period and was significantly lower during the COVID-19 period (300.86 per 1,000 patient days) (P < .0001).

CONCLUSIONS

The observed reduction of ~70% in the incidence density of HCFA CDI in a context of no reduction in antibiotic use supports the importance of reducing nosocomial transmission by healthcare workers and asymptomatic colonized patients, reinforcing cleaning procedures and reducing patient mobility in the epidemiological control of CDI.

A publicly accessible database for Clostridioides difficile genome sequences supports tracing of transmission chains and epidemics

Clostridioides difficile is the primary infectious cause of antibiotic-associated diarrhea. Local transmissions and international outbreaks of this pathogen have been previously elucidated by bacterial whole-genome sequencing, but comparative genomic analyses at the global scale were hampered by the lack of specific bioinformatic tools. Here we introduce a publicly accessible database within EnteroBase (http://enterobase.warwick.ac.uk) that automatically retrieves and assembles C. difficile short-reads from the public domain, and calls alleles for core-genome multilocus sequence typing (cgMLST). We demonstrate that comparable levels of resolution and precision are attained by EnteroBase cgMLST and single-nucleotide polymorphism analysis. EnteroBase currently contains 18 254 quality-controlled C. difficile genomes, which have been assigned to hierarchical sets of single-linkage clusters by cgMLST distances. This hierarchical clustering is used to identify and name populations of C. difficile at all epidemiological levels, from recent transmission chains through to epidemic and endemic strains. Moreover, it puts newly collected isolates into phylogenetic and epidemiological context by identifying related strains among all previously published genome data. For example, HC2 clusters (i.e. chains of genomes with pairwise distances of up to two cgMLST alleles) were statistically associated with specific hospitals (P<10-4) or single wards (P=0.01) within hospitals, indicating they represented local transmission clusters. We also detected several HC2 clusters spanning more than one hospital that by retrospective epidemiological analysis were confirmed to be associated with inter-hospital patient transfers. In contrast, clustering at level HC150 correlated with k-mer-based classification and was largely compatible with PCR ribotyping, thus enabling comparisons to earlier surveillance data. EnteroBase enables contextual interpretation of a growing collection of assembled, quality-controlled C. difficile genome sequences and their associated metadata. Hierarchical clustering rapidly identifies database entries that are related at multiple levels of genetic distance, facilitating communication among researchers, clinicians and public-health officials who are combatting disease caused by C. difficile.

Molecular epidemiology of predominant and emerging Clostridioides difficile ribotypes

There has been an increase in the incidence and severity of Clostridioides difficile infection (CDI) worldwide, and strategies to control, monitor, and diminish the associated morbidity and mortality have been developed. Several typing methods have been used for typing of isolates and studying the epidemiology of CDI; serotyping was the first typing method, but then was replaced by pulsed-field gel electrophoresis (PFGE). PCR ribotyping is now the gold standard method; however, multi locus sequence typing (MLST) schemes have been developed. New sequencing technologies have allowed comparing whole bacterial genomes to address genetic relatedness with a high level of resolution and discriminatory power to distinguish between closely related strains. Here, we review the most frequent C. difficile ribotypes reported worldwide, with a focus on their epidemiology and genetic characteristics.

Association of Household Exposure to Primary Clostridioides difficile Infection With Secondary Infection in Family Members

Importance

Clostridioides difficile infection (CDI) is a common hospital-acquired infection. Whether family members are more likely to experience a CDI following CDI in another separate family member remains to be studied.

Objective

To determine the incidence of potential family transmission of CDI.

Design, Setting, and Participants

In this case-control study comparing the incidence of CDI among individuals with prior exposure to a family member with CDI to those without prior family exposure, individuals were binned into monthly enrollment strata based on exposure status (eg, family exposure) and confounding factors (eg, age, prior antibiotic use). Data were derived from population-based, longitudinal commercial insurance claims from the Truven Marketscan Commercial Claims and Encounters and Medicare Supplemental databases from 2001 to 2017. Households with at least 2 family members continuously enrolled for at least 1 month were eligible. CDI incidence was computed within each stratum. A regression model was used to compare incidence of CDI while controlling for possible confounding characteristics.

Exposures

Index CDI cases were identified using inpatient and outpatient diagnosis codes. Exposure risks 60 days prior to infection included CDI diagnosed in another family member, prior hospitalization, and antibiotic use.

Main Outcomes and Measures

The primary outcome was the incidence of CDI in a given monthly enrollment stratum. Separate analyses were considered for CDI diagnosed in outpatient or hospital settings.

Results

A total of 224 818 cases of CDI, representing 194 424 enrollees (55.9% female; mean [SD] age, 52.8 [22.2] years) occurred in families with at least 2 enrollees. Of these, 1074 CDI events (4.8%) occurred following CDI diagnosis in a separate family member. Prior family exposure was significantly associated with increased incidence of CDI, with an incidence rate ratio (IRR) of 12.47 (95% CI, 8.86-16.97); this prior family exposure represented the factor with the second highest IRR behind hospital exposure (IRR, 16.18 [95% CI, 15.31-17.10]). For community-onset CDI cases without prior hospitalization, the IRR for family exposure was 21.74 (95% CI, 15.12-30.01). Age (IRR, 9.90 [95% CI, 8.92-10.98] for ages ≥65 years compared with ages 0-17 years), antibiotic use (IRR, 3.73 [95% CI, 3.41-4.08] for low-risk and 14.26 [95% CI, 13.27-15.31] for high-risk antibiotics compared with no antibiotics), and female sex (IRR, 1.44 [95% CI, 1.36-1.53]) were also positively associated with incidence.

Conclusions and Relevance

This study found that individuals with family exposure may be at significantly greater risk for acquiring CDI, which highlights the importance of the shared environment in the transmission and acquisition of C difficile.

Development and Implementation of Whole Genome Sequencing-Based Typing Schemes for Clostridioides difficile

Clostridioides difficile is an important nosocomial pathogen increasingly observed in the community and in different non-human reservoirs. The epidemiology and transmissibility of C. difficile has been studied using a variety of typing methods, including more recently developed whole-genome sequence (WGS) analysis that is becoming used routinely for bacterial typing worldwide. Here we review the schemes for WGS-based typing methods available for C. difficile and their applications in the field of human C. difficile infection (CDI). The two main approaches to discover genomic variations are single nucleotide variant (SNV) analysis and methods based on gene-by-gene comparisons (frequently called core genome or whole genome MLST, cgMLST, or wgMLST). SNV analysis currently provides the ultimate resolution, however, typing nomenclature and standardized methodology are missing. On the other hand, gene-by-gene approaches allow portability and standardized nomenclature, and are therefore becoming increasingly popular in bacterial epidemiology and outbreak investigation. Two commercial software packages (BioNumerics and Ridom SeqSphere+) and an open source database (EnteroBase) for allele and sequence type determination for C. difficile are currently available. Proof-of-concept WGS studies have already enabled advances in the investigation of the population structure of C. difficile species, microevolution within the epidemic strains, intercontinental transmission over time and in tracking of transmission events. WGS of clinical C. difficile isolates demonstrated a considerable genetic diversity suggesting diverse reservoirs for CDI. WGS was also shown to aid in resolving relapses and reinfections in recurrent CDI and has potential for use as a tool for assessing hospital infection prevention and control performance.