Impact of discontinuation of contact precautions on surveillance- and whole genome sequencing-defined methicillin-resistant Staphylococcus aureus healthcare-associated infections.

Objective: Prior studies evaluating the impact of discontinuation of contact precautions (DcCP) on methicillin-resistant Staphylococcus aureus (MRSA) outcomes have characterized all healthcare-associated infections (HAIs) rather than those likely preventable by contact precautions. We aimed to analyze the impact of DcCP on the rate of MRSA HAI including transmission events identified through whole genome sequencing (WGS) surveillance. Design: Quasi experimental interrupted time series. Setting: Acute care medical center. Participants: Inpatients. Methods: The effect of DcCP (use of gowns and gloves) for encounters among patients with MRSA carriage was evaluated using time series analysis of MRSA HAI rates from January 2019 through December 2022, compared to WGS-defined attributable transmission events before and after DcCP in December 2020. Results: The MRSA HAI rate was 4.22/10,000 patient days before and 2.98/10,000 patient days after DcCP (incidence rate ratio [IRR] 0.71 [95% confidence interval 0.56-0.89]) with a significant immediate decrease (P = .001). There were 7 WGS-defined attributable transmission events before and 11 events after DcCP (incident rate ratio 0.90 [95% confidence interval 0.30-2.55]). Conclusions: DcCP did not result in an increase in MRSA HAI or, in WGS-defined attributable transmission events. Comprehensive analyses of the effect of transmission prevention measures should include outcomes specifically measuring transmission-associated HAI.

Genomic sequencing surveillance of patients colonized with vancomycin-resistant Enterococcus (VRE) improves detection of hospital-associated transmission.

Background: Vancomycin-resistant enterococcal (VRE) infections pose significant challenges in healthcare. Transmission dynamics of VRE are complex, often involving patient colonization and subsequent transmission through various healthcare-associated vectors. We utilized a whole genome sequencing (WGS) surveillance program at our institution to better understand the contribution of clinical and colonizing isolates to VRE transmission. Methods: We performed whole genome sequencing on 352 VRE clinical isolates collected over 34 months and 891 rectal screening isolates collected over a 9-month nested period, and used single nucleotide polymorphisms to assess relatedness. We then performed a geo-temporal transmission analysis considering both clinical and rectal screening isolates compared with clinical isolates alone, and calculated 30-day outcomes of patients. Results: VRE rectal carriage constituted 87.3% of VRE acquisition, with an average monthly acquisition rate of 7.6 per 1000 patient days. We identified 185 genetically related clusters containing 2-42 isolates and encompassing 69.6% of all isolates in the dataset. The inclusion of rectal swab isolates increased the detection of clinical isolate clusters (from 53% to 67%, P<0.01). Geo-temporal analysis identified hotspot locations of VRE transmission. Patients with clinical VRE isolates that were closely related to previously sampled rectal swab isolates experienced 30-day ICU admission (17.5%), hospital readmission (9.2%), and death (13.3%). Conclusions: Our findings describe the high burden of VRE transmission at our hospital and shed light on the importance of using WGS surveillance of both clinical and rectal screening isolates to better understand the transmission of this pathogen. This study highlights the potential utility of incorporating WGS surveillance of VRE into routine hospital practice for improving infection prevention and patient safety.

Real-time genomic epidemiologic investigation of a multispecies plasmid-associated hospital outbreak of NDM-5-producing Enterobacterales infections.

OBJECTIVES: New Delhi metallo-ß-lactamase (NDM) is an emergent mechanism of carbapenem resistance associated with high mortality and limited treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods 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 performed weekly. To resolve NDM-encoding plasmids, we performed long-read sequencing and constructed hybrid assemblies. WGS data for suspected outbreaks was shared with the IP&C team for assessment and intervention. RESULTS: 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 7 bacterial species encoding the same NDM-5 plasmid. WGS surveillance and epidemiologic investigation characterized 10 horizontal plasmid transfer events and 6 bacterial transmission events between patients in varying hospital units. CONCLUSIONS: Our investigation revealed a complex, multispecies outbreak of NDM involving multiple plasmid transfer and bacterial transmission events. We highlight the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks.

Two Artificial Tears Outbreak-Associated Cases of Extensively Drug-Resistant Pseudomonas aeruginosa Detected Through Whole Genome Sequencing-Based Surveillance.

We describe 2 cases of extensively drug-resistant Pseudomonas aeruginosa infection caused by a strain of public health concern, as it was recently associated with a nationwide outbreak of contaminated artificial tears. Both cases were detected through database review of genomes in the Enhanced Detection System for Hospital-Associated Transmission (EDS-HAT), a routine genome sequencing-based surveillance program. We generated a high-quality reference genome for the outbreak strain from an isolate from our center and examined the mobile elements encoding blaVIM-80 and bla-GES-9 carbapenemases. We used publicly available Pseudomonas aeruginosa genomes to explore the genetic relatedness and antimicrobial resistance genes of the outbreak strain.

The utility of whole-genome sequencing to inform epidemiologic investigations of SARS-CoV-2 clusters in acute-care hospitals.

OBJECTIVE: To evaluate the utility of selective reactive whole-genome sequencing (WGS) in aiding healthcare-associated cluster investigations. DESIGN: Mixed-methods quality-improvement study. SETTING: Thes study was conducted across 8 acute-care facilities in an integrated health system. METHODS: We analyzed healthcare-associated coronavirus disease 2019 (COVID-19) clusters between May 2020 and July 2022 for which facility infection prevention and control (IPC) teams selectively requested reactive WGS to aid the epidemiologic investigation. WGS was performed with real-time results provided to IPC teams, including genetic relatedness of sequenced isolates. We conducted structured interviews with IPC teams on the informativeness of WGS for transmission investigation and prevention. RESULTS: In total, 8 IPC teams requested WGS to aid the investigation of 17 COVID-19 clusters comprising 226 cases and 116 (51%) sequenced isolates. Of these, 16 (94%) clusters had at least 1 WGS-defined transmission event. IPC teams hypothesized transmission pathways in 14 (82%) of 17 clusters and used data visualizations to characterize these pathways in 11 clusters (65%). The teams reported that in 15 clusters (88%), WGS identified a transmission pathway; the WGS-defined pathway was not one that was predicted by epidemiologic investigation in 7 clusters (41%). WGS changed the understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in 8 clusters (47%) and altered infection prevention interventions in 8 clusters (47%). CONCLUSIONS: Selectively utilizing reactive WGS helped identify cryptic SARS-CoV-2 transmission pathways and frequently changed the understanding and response to SARS-CoV-2 outbreaks. Until WGS is widely adopted, a selective reactive WGS approach may be highly impactful in response to healthcare-associated cluster investigations.

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

Background: New Delhi metallo-ß-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).

Two artificial tears outbreak-associated cases of XDR Pseudomonas aeruginosa detected through whole genome sequencing-based surveillance.

We describe two cases of XDR Pseudomonas aeruginosa infection caused by a strain of public health concern recently associated with a nationwide outbreak of contaminated artificial tears. Both cases were detected through database review of genomes in the Enhanced Detection System for Hospital-Associated Transmission (EDS-HAT), a routine genome sequencing-based surveillance program. We generated a high-quality reference genome for the outbreak strain from one of the case isolates from our center and examined the mobile elements encoding bla VIM-80 and bla GES-9 carbapenemases. We then used publicly available P. aeruginosa genomes to explore the genetic relatedness and antimicrobial resistance genes of the outbreak strain.

Genomic Epidemiology of Severe Acute Respiratory Syndrome Coronavirus 2 Transmission Among University Students in Western Pennsylvania.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) control on college campuses is challenging given communal living and student social dynamics. Understanding SARS-CoV-2 transmission among college students is important for the development of optimal control strategies. METHODS: SARS-CoV-2 nasal swab samples were collected from University of Pittsburgh students for symptomatic testing and asymptomatic surveillance from August 2020 through April 2021 from 3 campuses. Whole-genome sequencing (WGS) was performed on 308 samples, and contact tracing information collected from students was used to identify transmission clusters. RESULTS: We identified 31 Pangolin lineages of SARS-CoV-2, the majority belonging to B.1.1.7 (Alpha) and B.1.2 lineages. Contact tracing identified 142 students (46%) clustering with each other; WGS identified 53 putative transmission clusters involving 216 students (70%). WGS identified transmissions that were missed by contact tracing. However, 84 cases (27%) could not be linked by either WGS or contact tracing. Clusters were most frequently linked to students residing in the same dormitory, off-campus roommates, friends, or athletic activities. CONCLUSIONS: The majority of SARS-CoV-2-positive samples clustered by WGS, indicating significant transmission across campuses. The combination of WGS and contact tracing maximized the identification of SARS-CoV-2 transmission on campus. WGS can be used as a strategy to mitigate, and further prevent transmission among students.

Genomic characterization of SARS-CoV-2 from vaccine breakthrough cases in Allegheny County, Pennsylvania.

We performed whole genome sequencing on SARS-CoV-2 from 59 vaccinated individuals from southwest Pennsylvania who tested positive between February and September, 2021. A comparison of mutations among vaccine breakthrough cases to a time-matched control group identified potential adaptive responses of SARS-CoV-2 to vaccination.

Predictive model for severe COVID-19 using SARS-CoV-2 whole-genome sequencing and electronic health record data, March 2020-May 2021.

OBJECTIVE: We used SARS-CoV-2 whole-genome sequencing (WGS) and electronic health record (EHR) data to investigate the associations between viral genomes and clinical characteristics and severe outcomes among hospitalized COVID-19 patients. METHODS: We conducted a case-control study of severe COVID-19 infection among patients hospitalized at a large academic referral hospital between March 2020 and May 2021. SARS-CoV-2 WGS was performed, and demographic and clinical characteristics were obtained from the EHR. Severe COVID-19 (case patients) was defined as having one or more of the following: requirement for supplemental oxygen, mechanical ventilation, or death during hospital admission. Controls were hospitalized patients diagnosed with COVID-19 who did not meet the criteria for severe infection. We constructed predictive models incorporating clinical and demographic variables as well as WGS data including lineage, clade, and SARS-CoV-2 SNP/GWAS data for severe COVID-19 using multiple logistic regression. RESULTS: Of 1,802 hospitalized SARS-CoV-2-positive patients, we performed WGS on samples collected from 590 patients, of whom 396 were case patients and 194 were controls. Age (p = 0.001), BMI (p = 0.032), test positive time period (p = 0.001), Charlson comorbidity index (p = 0.001), history of chronic heart failure (p = 0.003), atrial fibrillation (p = 0.002), or diabetes (p = 0.007) were significantly associated with case-control status. SARS-CoV-2 WGS data did not appreciably change the results of the above risk factor analysis, though infection with clade 20A was associated with a higher risk of severe disease, after adjusting for confounder variables (p = 0.024, OR = 3.25; 95%CI: 1.31-8.06). CONCLUSIONS: Among people hospitalized with COVID-19, older age, higher BMI, earlier test positive period, history of chronic heart failure, atrial fibrillation, or diabetes, and infection with clade 20A SARS-CoV-2 strains can predict severe COVID-19.

Genomic Diversity of Hospital-Acquired Infections Revealed through Prospective Whole-Genome Sequencing-Based Surveillance.

Healthcare-associated infections (HAIs) cause mortality, morbidity, and waste of health care resources. HAIs are also an important driver of antimicrobial resistance, which is increasing around the world. Beginning in November 2016, we instituted an initiative to detect outbreaks of HAIs using prospective whole-genome sequencing-based surveillance of bacterial pathogens collected from hospitalized patients. Here, we describe the diversity of bacteria sampled from hospitalized patients at a single center, as revealed through systematic analysis of bacterial isolate genomes. We sequenced the genomes of 3,004 bacterial isolates from hospitalized patients collected over a 25-month period. We identified bacteria belonging to 97 distinct species, which were distributed among 14 groups of related species. Within these groups, isolates could be distinguished from one another by both average nucleotide identity (ANI) and principal-component analysis of accessory genes (PCA-A). Core genome genetic distances and rates of evolution varied among species, which has practical implications for defining shared ancestry during outbreaks and for our broader understanding of the origins of bacterial strains and species. Finally, antimicrobial resistance genes and putative mobile genetic elements were frequently observed, and our systematic analysis revealed patterns of occurrence across the different species sampled from our hospital. Overall, this study shows how understanding the population structure of diverse pathogens circulating in a single health care setting can improve the discriminatory power of genomic epidemiology studies and can help define the processes leading to strain and species differentiation. IMPORTANCE Hospitalized patients are at increased risk of becoming infected with antibiotic-resistant organisms. We used whole-genome sequencing to survey and compare over 3,000 clinical bacterial isolates collected from hospitalized patients at a large medical center over a 2-year period. We identified nearly 100 different bacterial species, which we divided into 14 different groups of related species. When we examined how genetic relatedness differed between species, we found that different species were likely evolving at different rates within our hospital. This is significant because the identification of bacterial outbreaks in the hospital currently relies on genetic similarity cutoffs, which are often applied uniformly across organisms. Finally, we found that antibiotic resistance genes and mobile genetic elements were abundant and were shared among the bacterial isolates we sampled. Overall, this study provides an in-depth view of the genomic diversity and evolutionary processes of bacteria sampled from hospitalized patients, as well as genetic similarity estimates that can inform hospital outbreak detection and prevention efforts.

2317. Persistent Bacterial Carriage and Hospital Transmission Detected by Whole Genome Sequence Surveillance.

Background: Healthcare-associated infections can be acquired via transmission of pathogens within the healthcare setting. Often, patients are assumed to have short duration (< 90 days) of transmissibility with bacterial pathogens after developing a clinical infection. This assumption may wrongly exclude patients as sources of transmission when they have persistent bacterial carriage. We studied patients with persistent carriage and associated transmission using whole genome sequencing surveillance. Methods: Patient culture positive isolates for select bacterial pathogens between 11/2016 and 11/2019 were collected if the patient was housed in the hospital for ≥3 days or had a recent healthcare exposure in the prior 30 days. Isolates were considered genetically related with ≤15 SNPs for all organisms except C. difficile (≤2 SNPs). Patients with serial isolates separated by >100 days were examined for other patients with related isolates and epidemiological commonalities between the first and last culture dates. Results: There were 779 related isolates from 369 unique patients (range 2-11 isolates/patient). The mean time from first to last culture date was 81.9 days (median 33 days, range 1-899 days) (Figure 1). 77 patients had isolates that were related to another patient of which 18 (23%) patients had >100 days between their first and last isolate (median 216, range 103-899). Of these, 9 (50%) patients had epidemiological links with another patient between their first and last isolate culture dates. The median time from exposure to positive culture date of the exposed patient was 14 days (mean 34, range 2-115). An example of potential transmission is shown in Figure 2.Days between the first and last related isolates within the same patientExample of patient with persistent carriage of K. pneumoniae and evidence of transmission to another patient. Conclusion: Some patients had persistent carriage with the same strain for over two years and appear to be a potential source of ongoing transmission to other patients. WGS surveillance, in addition to detecting outbreaks, can identify patients with persistent colonization as potential a transmission source. Healthcare outbreak investigations should include patients with persistent carriage as potential sources based upon temporal restrictions. Disclosures: Graham Snyder, MD, SM, Infectious Diseases Connect: Advisor/Consultant Daria Van Tyne, PhD, Century Therapeutics, Inc: Advisor/Consultant.

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 with traditional IP practice during the same period. RESULTS: Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2-14 patients. At least 1 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. CONCLUSIONS: 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.

Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance.

BACKGROUND: Traditional methods of outbreak investigations utilize reactive whole genome sequencing (WGS) to confirm or refute the outbreak. We have implemented WGS surveillance and a machine learning (ML) algorithm for the electronic health record (EHR) to retrospectively detect previously unidentified outbreaks and to determine the responsible transmission routes. METHODS: We performed WGS surveillance to identify and characterize clusters of genetically-related Pseudomonas aeruginosa infections during a 24-month period. ML of the EHR was used to identify potential transmission routes. A manual review of the EHR was performed by an infection preventionist to determine the most likely route and results were compared to the ML algorithm. RESULTS: We identified a cluster of 6 genetically related P. aeruginosa cases that occurred during a 7-month period. The ML algorithm identified gastroscopy as a potential transmission route for 4 of the 6 patients. Manual EHR review confirmed gastroscopy as the most likely route for 5 patients. This transmission route was confirmed by identification of a genetically-related P. aeruginosa incidentally cultured from a gastroscope used on 4of the 5 patients. Three infections, 2 of which were blood stream infections, could have been prevented if the ML algorithm had been running in real-time. CONCLUSIONS: WGS surveillance combined with a ML algorithm of the EHR identified a previously undetected outbreak of gastroscope-associated P. aeruginosa infections. These results underscore the value of WGS surveillance and ML of the EHR for enhancing outbreak detection in hospitals and preventing serious infections.