Molecular surveillance of methicillin-resistant Staphylococcus aureus genomes in hospital unexpectedly reveals discordance between temporal and genetic clustering

Whole-genome sequencing of carbapenem-resistant Enterobacterales isolates in southeast Louisiana reveals persistent genetic clusters spanning multiple locations

BACKGROUND

We investigated 51 g-negative carbapenem-resistant Enterobacterales (CRE) isolates collected from 22 patients over a five-year period from six health care institutions in the Ochsner Health network in southeast Louisiana.

METHODS

Short genomic reads were generated using Illumina sequencing and assembled for each isolate. Isolates were classified as Enterobacter spp. (n = 20), Klebsiella spp. (n = 30), and Escherichia coli (n = 1) and grouped into 19 different multi-locus sequence types (MLST). Species and patient-specific core genomes were constructed representing ∼50% of the chromosomal genome.

RESULTS

We identified two sets of patients with genetically related infections; in both cases, the related isolates were collected > 6 months apart, and in one case, the isolates were collected in different locations. On the other hand, we identified four sets of patients with isolates of the same species collected within 21 days from the same location; however, none had genetically related infections. Genes associated with resistance to carbapenem drugs (blaKPC and/or blaCTX-M-15) were found in 76% of the isolates. We found three blaKPC variants (blaKPC-2, blaKPC-3, and blaKPC-4) associated with four different Enterobacter MLST variants, and two blaKPC variants (blaKPC-2, blaKPC-3) associated with seven different Klebsiella MLST variants.

CONCLUSIONS

Molecular surveillance is increasingly becoming a powerful tool to understand bacterial spread in both community and clinical settings. This study provides evidence that genetically related infections in clinical settings do not necessarily reflect temporal associations, and vice versa. Our results also highlight the regional genomic and resistance diversity within related bacterial lineages.

Genomic Epidemiologic Investigation of a Multispecies Hospital Outbreak of NDM-5-Producing Enterobacterales Infections

Background

New Delhi metallo-β-lactamase (NDM) represents an emergent mechanism of carbapenem resistance associated with high mortality and limited antimicrobial treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods like speciation, antimicrobial resistance testing, and reactive whole genome sequencing (WGS) may not detect plasmid transfer in multispecies outbreaks.

Methods

Initial outbreak detection of NDM-producing Enterobacterales identified at an acute care hospital occurred via traditional IP&C methods and was supplemented by real-time WGS surveillance, which was performed weekly using the Illumina platform. To resolve NDM-encoding plasmids, we performed long-read Oxford Nanopore sequencing and constructed hybrid assemblies using Illumina and Nanopore sequencing data. Reports of relatedness between NDM-producing organisms and reactive WGS for suspected outbreaks were shared with the IP&C team for assessment and intervention.

Findings

We observed a multispecies outbreak of NDM-5-producing Enterobacterales isolated from 15 patients between February 2021 and February 2023. The 19 clinical and surveillance isolates sequenced included seven bacterial species and each encoded the same NDM-5 plasmid, which showed high homology to NDM plasmids previously observed in Asia. WGS surveillance and epidemiologic investigation characterized ten horizontal plasmid transfer events and six bacterial transmission events between patients housed in varying hospital units. Transmission prevention focused on enhanced observation and adherence to basic infection prevention measures.

Interpretation

Our investigation revealed a complex, multispecies outbreak of NDM that involved multiple plasmid transfer and bacterial transmission events, increasing the complexity of outbreak identification and transmission prevention. Our investigation highlights the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks.

Funding

This work was funded in part by the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH) (R01AI127472) (R21AI1783691).

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.

SARS-CoV-2 genomic surveillance as an evidence-based infection control approach in an offshore petroleum employee population

BACKGROUND

Industrial hygienists (IH) in the oil and gas business instituted an extraordinary number of safety protocols to limit spread of SARS-CoV-2 onto offshore platforms in the Gulf of Mexico. We used genomic surveillance to provide actionable information concerning the efficacy of their efforts.

METHODS

Over 6 months, employees at a single company were serology and PCR tested during a 1-5 day predeployment quarantine and when postdeployment symptoms were reported. From each positive test (n = 49), SARS-CoV-2 genomes were sequenced. Phylogenetic analysis was used to investigate the epidemiology of transmissions.

RESULTS

Genomic surveillance confirmed 2 viral strains were infecting 18 offshore workers. Genomic data combined with epidemiological data suggested that a change in quarantine protocols contributed to these outbreaks. A pre-deployment outbreak involved a WHO variant of interest (Theta) that had infected 4 international workers. Two additional predeployment clusters of infections were identified.

CONCLUSIONS

Our findings support that IH quarantine/testing protocols limited viral transmissions, halted offshore outbreaks, and stopped the spread of a variant of interest. The study demonstrates how genomic data can be used to understand viral transmission dynamics in employee populations and evaluate safety protocols in the offshore oil and gas industry.

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.