Budget impact analysis of routinely using whole-genomic sequencing of six multidrug-resistant bacterial pathogens in Queensland, Australia

The Next Frontier in Tuberculosis Investigation: Automated Whole Genome Sequencing for Mycobacterium tuberculosis Analysis

A fully automated bacteria whole genome sequencing (WGS) assay was evaluated to characterize Mycobacterium tuberculosis (MTB) and non-tuberculosis Mycobacterium (NTM) clinical isolates. The results generated were highly reproducible, with 100% concordance in species and sub-lineage classification and 92% concordance between antimicrobial resistance (AMR) genotypic and phenotypic profiles. Using extracted deoxyribonucleic acid (DNA) from MTB clinical isolates as starting material, these findings demonstrate that a fully automated WGS assay, with a short turnaround time of 24.5 hours, provides timely and valuable insights into MTB outbreak investigation while providing reliable genotypic AMR profiling consistent with traditional antimicrobial susceptibility tests (AST). This study establishes a favorable proposition for the adoption of end-to-end fully automated WGS solutions for decentralized MTB diagnostics, thereby aiding in World Health Organization's (WHO) vision of tuberculosis eradication.

Evaluating the impact of genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) on hospital infection prevention and control decisions

Genomic epidemiology enhances the ability to detect and refute methicillin-resistant Staphylococcus aureus (MRSA) outbreaks in healthcare settings, but its routine introduction requires further evidence of benefits for patients and resource utilization. We performed a 12 month prospective study at Cambridge University Hospitals NHS Foundation Trust in the UK to capture its impact on hospital infection prevention and control (IPC) decisions. MRSA-positive samples were identified via the hospital microbiology laboratory between November 2018 and November 2019. We included samples from in-patients, clinic out-patients, people reviewed in the Emergency Department and healthcare workers screened by Occupational Health. We sequenced the first MRSA isolate from 823 consecutive individuals, defined their pairwise genetic relatedness, and sought epidemiological links in the hospital and community. Genomic analysis of 823 MRSA isolates identified 72 genetic clusters of two or more isolates containing 339/823 (41 %) of the cases. Epidemiological links were identified between two or more cases for 190 (23 %) individuals in 34/72 clusters. Weekly genomic epidemiology updates were shared with the IPC team, culminating in 49 face-to-face meetings and 21 written communications. Seventeen clusters were identified that were consistent with hospital MRSA transmission, discussion of which led to additional IPC actions in 14 of these. Two outbreaks were also identified where transmission had occurred in the community prior to hospital presentation; these were escalated to relevant IPC teams. We identified 38 instances where two or more in-patients shared a ward location on overlapping dates but carried unrelated MRSA isolates (pseudo-outbreaks); research data led to de-escalation of investigations in six of these. Our findings provide further support for the routine use of genomic epidemiology to enhance and target IPC resources.

Consideration of within-patient diversity highlights transmission pathways and antimicrobial resistance gene variability in vancomycin-resistant Enterococcus faecium

BACKGROUND

WGS is increasingly being applied to healthcare-associated vancomycin-resistant Enterococcus faecium (VREfm) outbreaks. Within-patient diversity could complicate transmission resolution if single colonies are sequenced from identified cases.

OBJECTIVES

Determine the impact of within-patient diversity on transmission resolution of VREfm.

MATERIALS AND METHODS

Fourteen colonies were collected from VREfm positive rectal screens, single colonies were collected from clinical samples and Illumina WGS was performed. Two isolates were selected for Oxford Nanopore sequencing and hybrid genome assembly to generate lineage-specific reference genomes. Mapping to closely related references was used to identify genetic variations and closely related genomes. A transmission network was inferred for the entire genome set using Phyloscanner.

RESULTS AND DISCUSSION

In total, 229 isolates from 11 patients were sequenced. Carriage of two or three sequence types was detected in 27% of patients. Presence of antimicrobial resistance genes and plasmids was variable within genomes from the same patient and sequence type. We identified two dominant sequence types (ST80 and ST1424), with two putative transmission clusters of two patients within ST80, and a single cluster of six patients within ST1424. We found transmission resolution was impaired using fewer than 14 colonies.

CONCLUSIONS

Patients can carry multiple sequence types of VREfm, and even within related lineages the presence of mobile genetic elements and antimicrobial resistance genes can vary. VREfm within-patient diversity could be considered in future to aid accurate resolution of transmission networks.

Active surveillance of carbapenemase-producing Enterobacterales using genomic sequencing for hospital-based infection control interventions

BACKGROUND

Whole-genome sequencing (WGS) is increasingly used to characterize hospital outbreaks of carbapenemase-producing Enterobacterales (CPE). However, access to WGS is variable and testing is often centralized, leading to delays in reporting of results.

OBJECTIVE

We describe the utility of a local sequencing service to promptly respond to facility needs over an 8-year period.

METHODS

The study was conducted at Royal Prince Alfred Hospital in Sydney, Australia. All CPE isolated from patient (screening and clinical) and environmental samples from 2015 onward underwent prospective WGS. Results were notified to the infection control unit in real time. When outbreaks were identified, WGS reports were also provided to senior clinicians and the hospital executive administration. Enhanced infection control interventions were refined based on the genomic data.

RESULTS

In total, 141 CPE isolates were detected from 123 patients and 5 environmental samples. We identified 9 outbreaks, 4 of which occurred in high-risk wards (intensive care unit and/or solid-organ transplant ward). The largest outbreak involved Enterobacterales containing an NDM gene. WGS detected unexpected links among patients, which led to further investigation of epidemiological data that uncovered the outpatient setting and contaminated equipment as reservoirs for ongoing transmission. Targeted interventions as part of outbreak management halted further transmission.

CONCLUSIONS

WGS has transitioned from an emerging technology to an integral part of local CPE control strategies. Our results show the value of embedding this technology in routine surveillance, with timely reports generated in clinically relevant timeframes to inform and optimize local control measures for greatest impact.

Comparative genomic analysis of antibiotic resistance and virulence genes in Staphylococcus aureus isolates from patients and retail meat

Introduction

Staphylococcus aureus is a significant human pathogen that poses a threat to public health due to its association with foodborne contamination and a variety of infections. The factors contributing to the pathogenicity of S. aureus include virulence, drug resistance, and toxin production, making it essential to monitor their prevalence and genetic profiles. This study investigated and compared the genomic characteristics of S. aureus isolates from retail meat and patients in Saudi Arabia.

Methods

A total of 136 S. aureus isolates were obtained between October 2021 and June 2022:84 from patients and 53 from meat samples in Riyadh, Saudi Arabia. S. aureus isolates were identified using conventional methods and MALDI-TOF MS, and methicillin-resistant S. aureus (MRSA) was identified using VITEK2 and BD Phoenix systems. MRSA was confirmed phenotypically using chromogenic agar, and genotypically by detecting mecA. Genomic data were analyzed using BactopiaV2 pipeline, local BLAST, and MLST databases.

Results

Antibiotic resistance genes were prevalent in both meat and patient S. aureus isolates, with high prevalence of tet38, blaZ, and fosB. Notably, all S. aureus isolates from patients carried multidrug-resistant (MDR) genes, and a high percentage of S. aureus isolates from meat also harbored MDR genes. Phenotypically, 43% of the S. aureus isolates from meat and 100% of the patients' isolates were MDR. Enterotoxin genes, including selX, sem, and sei, exhibited high compatibility between meat and patient S. aureus isolates. Virulence genes such as cap, hly/hla, sbi, and isd were found in all S. aureus isolates from both sources.

Conclusion

Our study established a genetic connection between S. aureus isolates from meat and patients, showing shared antibiotic resistance and virulence genes. The presence of these genes in meat derived isolates underscores its role as a reservoir. Genomic relatedness also suggests potential transmission of resistance between different settings. These findings emphasize the necessity for a comprehensive approach to monitor and control S. aureus infections in both animals and humans.

Genetic determinants underlying the progressive phenotype of β-lactam/β-lactamase inhibitor resistance in Escherichia coli

IMPORTANCE

The increased feasibility of whole-genome sequencing has generated significant interest in using such molecular diagnostic approaches to characterize difficult-to-treat, antimicrobial-resistant (AMR) infections. Nevertheless, there are current limitations in the accurate prediction of AMR phenotypes based on existing AMR gene database approaches, which primarily correlate a phenotype with the presence/absence of a single AMR gene. Our study utilized a large cohort of cephalosporin-susceptible Escherichia coli bacteremia samples to determine how increasing the dosage of narrow-spectrum β-lactamase-encoding genes in conjunction with other diverse β-lactam/β-lactamase inhibitor (BL/BLI) genetic determinants contributes to progressively more severe BL/BLI phenotypes. We were able to characterize the complexity of the genetic mechanisms underlying progressive BL/BLI resistance including the critical role of β-lactamase encoding gene amplification. For the diverse array of AMR phenotypes with complex mechanisms involving multiple genomic factors, our study provides an example of how composite risk scores may improve understanding of AMR genotype/phenotype correlations.

Genomics for public health and international surveillance of antimicrobial resistance

Historically, epidemiological investigation and surveillance for bacterial antimicrobial resistance (AMR) has relied on low-resolution isolate-based phenotypic analyses undertaken at local and national reference laboratories. Genomic sequencing has the potential to provide a far more high-resolution picture of AMR evolution and transmission, and is already beginning to revolutionise how public health surveillance networks monitor and tackle bacterial AMR. However, the routine integration of genomics in surveillance pipelines still has considerable barriers to overcome. In 2022, a workshop series and online consultation brought together international experts in AMR and pathogen genomics to assess the status of genomic applications for AMR surveillance in a range of settings. Here we focus on discussions around the use of genomics for public health and international AMR surveillance, noting the potential advantages of, and barriers to, implementation, and proposing recommendations from the working group to help to drive the adoption of genomics in public health AMR surveillance. These recommendations include the need to build capacity for genome sequencing and analysis, harmonising and standardising surveillance systems, developing equitable data sharing and governance frameworks, and strengthening interactions and relationships among stakeholders at multiple levels.

Economic evaluations of whole-genome sequencing for pathogen identification in public health surveillance and health-care-associated infections: a systematic review

Whole-genome sequencing (WGS) has resulted in improvements to pathogen characterisation for the rapid investigation and management of disease outbreaks and surveillance. We conducted a systematic review to synthesise the economic evidence of WGS implementation for pathogen identification and surveillance. Of the 2285 unique publications identified through online database searches, 19 studies met the inclusion criteria. The economic evidence to support the broader application of WGS as a front-line pathogen characterisation and surveillance tool is insufficient and of low quality. WGS has been evaluated in various clinical settings, but these evaluations are predominantly investigations of a single pathogen. There are also considerable variations in the evaluation approach. Economic evaluations of costs, effectiveness, and cost-effectiveness are needed to support the implementation of WGS in public health settings.

Whole-genome sequencing links cases dispersed in time, place, and person while supporting healthcare worker management in an outbreak of Panton-Valentine leucocidin meticillin-resistant Staphylococcus aureus; and a review of literature

This is a report on an outbreak of Panton-Valentine leucocidin-producing meticillin-resistant Staphylococcus aureus (PVL-MRSA) in an intensive care unit (ICU) during the COVID-19 pandemic that affected seven patients and a member of staff. Six patients were infected over a period of ten months on ICU by the same strain of PVL-MRSA, and a historic case identified outside of the ICU. All cases were linked to a healthcare worker (HCW) who was colonized with the organism. Failed topical decolonization therapy, without systemic antibiotic therapy, resulted in ongoing transmission and one preventable acquisition of PVL-MRSA. The outbreak identifies the support that may be needed for HCWs implicated in outbreaks. It also demonstrates the role of whole-genome sequencing in identifying dispersed and historic cases related to the outbreak, which in turn aids decision-making in outbreak management and HCW support. This report also includes a review of literature of PVL-MRSA-associated outbreaks in healthcare and highlights the need for review of current national guidance in the management of HCWs' decolonization regimen and return-to-work recommendations in such outbreaks.

Economic and health impact modelling of a whole genome sequencing-led intervention strategy for bacterial healthcare-associated infections for England and for the USA

Bacterial healthcare-associated infections (HAIs) are a substantial source of global morbidity and mortality. The estimated cost associated with HAIs ranges from $35 to $45 billion in the USA alone. The costs and accessibility of whole genome sequencing (WGS) of bacteria and the lack of sufficiently accurate, high-resolution, scalable and accessible analysis for strain identification are being addressed. Thus, it is timely to determine the economic viability and impact of routine diagnostic bacterial genomics. The aim of this study was to model the economic impact of a WGS surveillance system that proactively detects and directs interventions for nosocomial infections and outbreaks compared to the current standard of care, without WGS. Using a synthesis of published models, inputs from national statistics, and peer-reviewed articles, the economic impacts of conducting a WGS-led surveillance system addressing the 11 most common nosocomial pathogen groups in England and the USA were modelled. This was followed by a series of sensitivity analyses. England was used to establish the baseline model because of the greater availability of underpinning data, and this was then modified using USA-specific parameters where available. The model for the NHS in England shows bacterial HAIs currently cost the NHS around £3 billion. WGS-based surveillance delivery is predicted to cost £61.1 million associated with the prevention of 74 408 HAIs and 1257 deaths. The net cost saving was £478.3 million, of which £65.8 million were from directly incurred savings (antibiotics, consumables, etc.) and £412.5 million from opportunity cost savings due to re-allocation of hospital beds and healthcare professionals. The USA model indicates that the bacterial HAI care baseline costs are around $18.3 billion. WGS surveillance costs $169.2 million, and resulted in a net saving of ca.$3.2 billion, while preventing 169 260 HAIs and 4862 deaths. From a 'return on investment' perspective, the model predicts a return to the hospitals of £7.83 per £1 invested in diagnostic WGS in the UK, and US$18.74 per $1 in the USA. Sensitivity analyses show that substantial savings are retained when inputs to the model are varied within a wide range of upper and lower limits. Modelling a proactive WGS system addressing HAI pathogens shows significant improvement in morbidity and mortality while simultaneously achieving substantial savings to healthcare facilities that more than offset the cost of implementing diagnostic genomics surveillance.

CamPype: an open-source workflow for automated bacterial whole-genome sequencing analysis focused on Campylobacter

BACKGROUND

The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge.

RESULTS

To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation.

CONCLUSIONS

The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype .

Economic evaluations of interventions to prevent and control health-care-associated infections: a systematic review

Almost 9 million health-care-associated infections have been estimated to occur each year in European hospitals and long-term care facilities, and these lead to an increase in morbidity, mortality, bed occupancy, and duration of hospital stay. The aim of this systematic review was to review the cost-effectiveness of interventions to limit the spread of health-care-associated infections), framed by WHO infection prevention and control core components. The Embase, National Health Service Economic Evaluation Database, Database of Abstracts of Reviews of Effects, Health Technology Assessment, Cinahl, Scopus, Pediatric Economic Database Evaluation, and Global Index Medicus databases, plus grey literature were searched for studies between Jan 1, 2009, and Aug 10, 2022. Studies were included if they reported interventions including hand hygiene, personal protective equipment, national-level or facility-level infection prevention and control programmes, education and training programmes, environmental cleaning, and surveillance. The British Medical Journal checklist was used to assess the quality of economic evaluations. 67 studies were included in the review. 25 studies evaluated methicillin-resistant Staphylococcus aureus outcomes. 31 studies evaluated screening strategies. The assessed studies that met the minimum quality criteria consisted of economic models. There was some evidence that hand hygiene, environmental cleaning, surveillance, and multimodal interventions were cost-effective. There were few or no studies investigating education and training, personal protective equipment or monitoring, and evaluation of interventions. This Review provides a map of cost-effectiveness data, so that policy makers and researchers can identify the relevant data and then assess the quality and generalisability for their setting.

Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples

Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.

Clinical Implementation of Routine Whole-genome Sequencing for Hospital Infection Control of Multi-drug Resistant Pathogens

BACKGROUND

Prospective whole-genome sequencing (WGS)-based surveillance may be the optimal approach to rapidly identify transmission of multi-drug resistant (MDR) bacteria in the healthcare setting.

METHODS

We prospectively collected methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), carbapenem-resistant Acinetobacter baumannii (CRAB), extended-spectrum beta-lactamase (ESBL-E), and carbapenemase-producing Enterobacterales (CPE) isolated from blood cultures, sterile sites, or screening specimens across three large tertiary referral hospitals (2 adult, 1 paediatric) in Brisbane, Australia. WGS was used to determine in silico multi-locus sequence typing (MLST) and resistance gene profiling via a bespoke genomic analysis pipeline. Putative transmission events were identified by comparison of core genome single nucleotide polymorphisms (SNPs). Relevant clinical meta-data were combined with genomic analyses via customised automation, collated into hospital-specific reports regularly distributed to infection control teams.

RESULTS

Over 4 years (April 2017 to July 2021) 2660 isolates were sequenced. This included MDR gram-negative bacilli (n = 293 CPE, n = 1309 ESBL), MRSA (n = 620), and VRE (n = 433). A total of 379 clinical reports were issued. Core genome SNP data identified that 33% of isolates formed 76 distinct clusters. Of the 76 clusters, 43 were contained to the 3 target hospitals, suggesting ongoing transmission within the clinical environment. The remaining 33 clusters represented possible inter-hospital transmission events or strains circulating in the community. In 1 hospital, proven negligible transmission of non-multi-resistant MRSA enabled changes to infection control policy.

CONCLUSIONS

Implementation of routine WGS for MDR pathogens in clinical laboratories is feasible and can enable targeted infection prevention and control interventions.

A systematic review of economic evaluations of whole-genome sequencing for the surveillance of bacterial pathogens

Whole-genome sequencing (WGS) has unparalleled ability to distinguish between bacteria, with many public health applications. The generation and analysis of WGS data require significant financial investment. We describe a systematic review summarizing economic analyses of genomic surveillance of bacterial pathogens, reviewing the evidence for economic viability. The protocol was registered on PROSPERO (CRD42021289030). Six databases were searched on 8 November 2021 using terms related to 'WGS', 'population surveillance' and 'economic analysis'. Quality was assessed with the Drummond-Jefferson checklist. Following data extraction, a narrative synthesis approach was taken. Six hundred and eighty-one articles were identified, of which 49 proceeded to full-text screening, with 9 selected for inclusion. All had been published since 2019. Heterogeneity was high. Five studies assessed WGS for hospital surveillance and four analysed foodborne pathogens. Four were cost-benefit analyses, one was a cost-utility analysis, one was a cost-effectiveness analysis, one was a combined cost-effectiveness and cost-utility analysis, one combined cost-effectiveness and cost-benefit analyses and one was a partial analysis. All studies supported the use of WGS as a surveillance tool on economic grounds. The available evidence supports the use of WGS for pathogen surveillance but is limited by marked heterogeneity. Further work should include analysis relevant to low- and middle-income countries and should use real-world effectiveness data.

ESBL plasmids in Klebsiella pneumoniae: diversity, transmission and contribution to infection burden in the hospital setting

Background

Resistance to third-generation cephalosporins, often mediated by extended-spectrum beta-lactamases (ESBLs), is a considerable issue in hospital-associated infections as few drugs remain for treatment. ESBL genes are often located on large plasmids that transfer horizontally between strains and species of Enterobacteriaceae and frequently confer resistance to additional drug classes. Whilst plasmid transmission is recognised to occur in the hospital setting, the frequency and impact of plasmid transmission on infection burden, compared to ESBL + strain transmission, is not well understood.

Methods

We sequenced the genomes of clinical and carriage isolates of Klebsiella pneumoniae species complex from a year-long hospital surveillance study to investigate ESBL burden and plasmid transmission in an Australian hospital. Long-term persistence of a key transmitted ESBL + plasmid was investigated via sequencing of ceftriaxone-resistant isolates during 4 years of follow-up, beginning 3 years after the initial study.

Results

We found 25 distinct ESBL plasmids. We identified one plasmid, which we called Plasmid A, that carried bla_CTX-M-15 in an IncF backbone similar to pKPN-307. Plasmid A was transmitted at least four times into different Klebsiella species/lineages and was responsible for half of all ESBL episodes during the initial 1-year study period. Three of the Plasmid A-positive strains persisted locally 3–6 years later, and Plasmid A was detected in two additional strain backgrounds. Overall Plasmid A accounted for 21% of ESBL + infections in the follow-up period.

Conclusions

Here, we systematically surveyed ESBL strain and plasmid transmission over 1 year in a single hospital network. Whilst ESBL plasmid transmission events were rare in this setting, they had a significant and sustained impact on the burden of ceftriaxone-resistant and multidrug-resistant infections. If onward transmission of Plasmid A-carrying strains could have been prevented, this may have reduced the number of opportunities for Plasmid A to transmit and create novel ESBL + strains, as well as reducing overall ESBL infection burden.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01103-0.

Using Genomics to Understand the Epidemiology of Infectious Diseases in the Northern Territory of Australia

The Northern Territory (NT) is a geographically remote region of northern and central Australia. Approximately a third of the population are First Nations Australians, many of whom live in remote regions. Due to the physical environment and climate, and scale of social inequity, the rates of many infectious diseases are the highest nationally. Molecular typing and genomic sequencing in research and public health have provided considerable new knowledge on the epidemiology of infectious diseases in the NT. We review the applications of genomic sequencing technology for molecular typing, identification of transmission clusters, phylogenomics, antimicrobial resistance prediction, and pathogen detection. We provide examples where these methodologies have been applied to infectious diseases in the NT and discuss the next steps in public health implementation of this technology.

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.

Multi-site implementation of whole genome sequencing for hospital infection control: A prospective genomic epidemiological analysis

BACKGROUND

Current microbiological methods lack the resolution to accurately identify multidrug-resistant organism (MDRO) transmission, however, whole genome sequencing can identify highly-related patient isolates providing opportunities for precision infection control interventions. We investigated the feasibility and potential impact of a prospective multi-centre genomics workflow for hospital infection control.

METHODS

We conducted a prospective genomics implementation study across eight Australian hospitals over 15 months (2017,2018), collecting all clinical and screening isolates from inpatients with vanA VRE, MRSA, ESBL Escherichia coli (ESBL-Ec), or ESBL Klebsiella pneumoniae (ESBL-Kp). Genomic and epidemiologic data were integrated to assess MDRO transmission.

FINDINGS

In total, 2275 isolates were included from 1970 patients, predominantly ESBL-Ec (40·8%) followed by MRSA (35·6%), vanA VRE (15·2%), and ESBL-Kp (8·3%).Overall, hospital and genomic epidemiology showed 607 patients (30·8%) acquired their MDRO in hospital, including the majority of vanA VRE (266 patients, 86·4%), with lower proportions of ESBL-Ec (186 patients, 23·0%), ESBL-Kp (42 patients, 26·3%), and MRSA (113 patients, 16·3%). Complex patient movements meant the majority of MDRO transmissions would remain undetected without genomic data.The genomics implementation had major impacts, identifying unexpected MDRO transmissions prompting new infection control interventions, and contributing to vanA VRE becoming a notifiable condition. We identified barriers to implementation and recommend strategies for mitigation.

INTERPRETATION

Implementation of a multi-centre genomics-informed infection control workflow is feasible and identifies many unrecognised MDRO transmissions. This provides critical opportunities for interventions to improve patient safety in hospitals.

FUNDING

Melbourne Genomics Health Alliance (supported by State Government of Victoria, Australia), and National Health and Medical Research Council (Australia).

Exploiting genomics to mitigate the public health impact of antimicrobial resistance

Antimicrobial resistance (AMR) is a major global public health threat, which has been largely driven by the excessive use of antimicrobials. Control measures are urgently needed to slow the trajectory of AMR but are hampered by an incomplete understanding of the interplay between pathogens, AMR encoding genes, and mobile genetic elements at a microbial level. These factors, combined with the human, animal, and environmental interactions that underlie AMR dissemination at a population level, make for a highly complex landscape. Whole-genome sequencing (WGS) and, more recently, metagenomic analyses have greatly enhanced our understanding of these processes, and these approaches are informing mitigation strategies for how we better understand and control AMR. This review explores how WGS techniques have advanced global, national, and local AMR surveillance, and how this improved understanding is being applied to inform solutions, such as novel diagnostic methods that allow antimicrobial use to be optimised and vaccination strategies for better controlling AMR. We highlight some future opportunities for AMR control informed by genomic sequencing, along with the remaining challenges that must be overcome to fully realise the potential of WGS approaches for international AMR control.

Strengths and caveats of identifying resistance genes from whole genome sequencing data

INTRODUCTION

Antimicrobial resistance (AMR) continues to present major challenges to modern healthcare. Recent advances in whole-genome sequencing (WGS) have made the rapid molecular characterization of AMR a realistic possibility for diagnostic laboratories; yet major barriers to clinical implementation exist.

AREAS COVERED

We describe and compare short- and long-read sequencing platforms, typical components of bioinformatics pipelines, tools for AMR gene detection and the relative merits of read- or assembly-based approaches. The challenges of characterizing mobile genetic elements from genomic data are outlined, as well as the complexities inherent to the prediction of phenotypic resistance from WGS. Practical obstacles to implementation in diagnostic laboratories, the critical role of quality control and external quality assurance, as well as standardized reporting standards are also discussed. Future directions, such as the application of machine-learning and artificial intelligence algorithms, linked to clinically meaningful outcomes, may offer a new paradigm for the clinical application of AMR prediction.

EXPERT OPINION

AMR prediction from WGS data presents an exciting opportunity to advance our capacity to comprehensively characterize infectious pathogens in a rapid manner, ultimately aiming to improve patient outcomes. Collaborative efforts between clinicians, scientists, regulatory bodies and healthcare administrators will be critical to achieve the full promise of this approach.

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.

Queensland Genomics: an adaptive approach for integrating genomics into a public healthcare system

The establishment of genomics in health care systems has been occurring for the past decade. It is recognised that implementing genomics within a health service is challenging without a system-wide approach. Globally, as clinical genomics implementation programs have matured there is a growing body of information around program design and outcomes. Program structures vary depending on local ecosystems including the health system, politics and funding availability, however, lessons from other programs are important to the design of programs in different jurisdictions. Here we describe an adaptive approach to the implementation of genomics into a publicly funded health care system servicing a population of 5.1 million people. The adaptive approach enabled flexibility to facilitate substantial changes during the program in response to learnings and external factors. We report the benefits and challenges experienced by the program, particularly in relation to the engagement of people and services, and the design of both individual projects and the program as a whole.

Cost-effectiveness analysis of whole-genome sequencing during an outbreak of carbapenem-resistant Acinetobacter baumannii

Background:

Whole-genome sequencing (WGS) shotgun metagenomics (metagenomics) attempts to sequence the entire genetic content straight from the sample. Diagnostic advantages lie in the ability to detect unsuspected, uncultivatable, or very slow-growing organisms.

Objective:

To evaluate the clinical and economic effects of using WGS and metagenomics for outbreak management in a large metropolitan hospital.

Design:

Cost-effectiveness study.

Setting:

Intensive care unit and burn unit of large metropolitan hospital.

Patients:

Simulated intensive care unit and burn unit patients.

Methods:

We built a complex simulation model to estimate pathogen transmission, associated hospital costs, and quality-adjusted life years (QALYs) during a 32-month outbreak of carbapenem-resistant Acinetobacter baumannii (CRAB). Model parameters were determined using microbiology surveillance data, genome sequencing results, hospital admission databases, and local clinical knowledge. The model was calibrated to the actual pathogen spread within the intensive care unit and burn unit (scenario 1) and compared with early use of WGS (scenario 2) and early use of WGS and metagenomics (scenario 3) to determine their respective cost-effectiveness. Sensitivity analyses were performed to address model uncertainty.

Results:

On average compared with scenario 1, scenario 2 resulted in 14 fewer patients with CRAB, 59 additional QALYs, and $75,099 cost savings. Scenario 3, compared with scenario 1, resulted in 18 fewer patients with CRAB, 74 additional QALYs, and $93,822 in hospital cost savings. The likelihoods that scenario 2 and scenario 3 were cost-effective were 57% and 60%, respectively.

Conclusions:

The use of WGS and metagenomics in infection control processes were predicted to produce favorable economic and clinical outcomes.