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

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-β-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.

Microbiome and infectious disease: diagnostics to therapeutics

Over 300 years of research on the microbial world has revealed their importance in human health and disease. This review explores the impact and potential of microbial-based detection methods and therapeutic interventions, integrating research of early microbiologists, current findings, and future perspectives.

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.

Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It

With the rapid advancement of artificial intelligence (AI), the field of infectious diseases (ID) faces both innovation and disruption. AI and its subfields including machine learning, deep learning, and large language models can support ID clinicians' decision making and streamline their workflow. AI models may help ensure earlier detection of disease, more personalized empiric treatment recommendations, and allocation of human resources to support higher-yield antimicrobial stewardship and infection prevention strategies. AI is unlikely to replace the role of ID experts, but could instead augment it. However, its limitations will need to be carefully addressed and mitigated to ensure safe and effective implementation. ID experts can be engaged in AI implementation by participating in training and education, identifying use cases for AI to help improve patient care, designing, validating and evaluating algorithms, and continuing to advocate for their vital role in patient care.

Harnessing the Diversity of Burkholderia spp. Prophages for Therapeutic Potential

Burkholderia spp. are often resistant to antibiotics, and infections with these organisms are difficult to treat. A potential alternative treatment for Burkholderia spp. infections is bacteriophage (phage) therapy; however, it can be difficult to locate phages that target these bacteria. Prophages incorporated into the bacterial genome have been identified within Burkholderia spp. and may represent a source of useful phages for therapy. Here, we investigate whether prophages within Burkholderia spp. clinical isolates can kill conspecific and heterospecific isolates. Thirty-two Burkholderia spp. isolates were induced for prophage release, and harvested phages were tested for lytic activity against the same 32 isolates. Temperate phages were passaged and their host ranges were determined, resulting in four unique phages of prophage origin that showed different ranges of lytic activity. We also analyzed the prophage content of 35 Burkholderia spp. clinical isolate genomes and identified several prophages present in the genomes of multiple isolates of the same species. Finally, we observed that Burkholdera cenocepacia isolates were more phage-susceptible than Burkholderia multivorans isolates. Overall, our findings suggest that prophages present within Burkholderia spp. genomes are a potentially useful starting point for the isolation and development of novel phages for use in phage therapy.

Complete genome sequence of Stenotrophomonas maltophilia complex strain STEN00241

We report here the complete, closed genome sequence of Stenotrophomonas maltophilia complex strain STEN00241, which was isolated from patient sputum. The genome is a single contig with a total length of 4,751,329 nucleotides and a GC content of 66.5%.

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.

Identification of a novel CG307 sub-clade in third-generation-cephalosporin-resistant Klebsiella pneumoniae causing invasive infections in the USA

Despite the notable clinical impact, recent molecular epidemiology regarding third-generation-cephalosporin-resistant (3GC-R) Klebsiella pneumoniae in the USA remains limited. We performed whole-genome sequencing of 3GC-R K. pneumoniae bacteraemia isolates collected from March 2016 to May 2022 at a tertiary care cancer centre in Houston, TX, USA, using Illumina and Oxford Nanopore Technologies platforms. A comprehensive comparative genomic analysis was performed to dissect population structure, transmission dynamics and pan-genomic signatures of our 3GC-R K. pneumoniae population. Of the 178 3GC-R K. pneumoniae bacteraemias that occurred during our study time frame, we were able to analyse 153 (86 %) bacteraemia isolates, 126 initial and 27 recurrent isolates. While isolates belonging to the widely prevalent clonal group (CG) 258 were rarely observed, the predominant CG, 307, accounted for 37 (29 %) index isolates and displayed a significant correlation (Pearson correlation test P value=0.03) with the annual frequency of 3GC-R K. pneumoniae bacteraemia. Interestingly, only 11 % (4/37) of CG307 isolates belonged to the commonly detected 'Texas-specific' clade that has been observed in previous Texas-based K. pneumoniae antimicrobial-resistance surveillance studies. We identified nearly half of our CG307 isolates (n=18) belonged to a novel, monophyletic CG307 sub-clade characterized by the chromosomally encoded bla SHV-205 and unique accessory genome content. This CG307 sub-clade was detected in various regions of the USA, with genome sequences from 24 additional strains becoming recently available in the National Center for Biotechnology Information (NCBI) SRA database. Collectively, this study underscores the emergence and dissemination of a distinct CG307 sub-clade that is a prevalent cause of 3GC-R K. pneumoniae bacteraemia among cancer patients seen in Houston, TX, and has recently been isolated throughout the USA.

Staphylococcus epidermidis joint isolates: Whole-genome sequencing demonstrates evidence of hospital transmission and common antimicrobial resistance

OBJECTIVE

We investigated genetic, epidemiologic, and environmental factors contributing to positive Staphylococcus epidermidis joint cultures.

DESIGN

Retrospective cohort study with whole-genome sequencing (WGS).

PATIENTS

We identified S. epidermidis isolates from hip or knee cultures in patients with 1 or more prior corresponding intra-articular procedure at our hospital.

METHODS

WGS and single-nucleotide polymorphism-based clonality analyses were performed, including species identification, in silico multilocus sequence typing (MLST), phylogenomic analysis, and genotypic assessment of the prevalence of specific antibiotic resistance and virulence genes. Epidemiologic review was performed to compare cluster and noncluster cases.

RESULTS

In total, 60 phenotypically distinct S. epidermidis isolates were identified. After removal of duplicates and impure samples, 48 isolates were used for the phylogenomic analysis, and 45 (93.7%) isolates were included in the clonality analysis. Notably, 5 S. epidermidis strains (10.4%) showed phenotypic susceptibility to oxacillin yet harbored mecA, and 3 (6.2%) strains showed phenotypic resistance despite not having mecA. Smr was found in all isolates, and mupA positivity was not observed. We also identified 6 clonal clusters from the clonality analysis, which accounted for 14 (31.1%) of the 45 S. epidermidis isolates. Our epidemiologic investigation revealed ties to common aspirations or operative procedures, although no specific common source was identified.

CONCLUSIONS

Most S. epidermidis isolates from clinical joint samples are diverse in origin, but we identified an important subset of 31.1% that belonged to subclinical healthcare-associated clusters. Clusters appeared to resolve spontaneously over time, suggesting the benefit of routine hospital infection control and disinfection practices.

Harnessing the diversity of Burkholderia spp. prophages for therapeutic potential

Burkholderia spp. are often resistant to antibiotics, and infections with these organisms are difficult to treat. A potential alternative treatment for Burkholderia spp. infections is bacteriophage (phage) therapy; however, it can be difficult to locate phages that target these bacteria. Prophages incorporated into the bacterial genome have been identified within Burkholderia spp. and may represent a source of useful phages for therapy. Here we investigate whether prophages within Burkholderia spp. clinical isolates can kill conspecific and heterospecific isolates. Thirty-two Burkholderia spp. isolates were induced for prophage release, and harvested prophages were tested for lytic activity against the same 32 isolates. Lytic phages were passaged and their host ranges were determined, resulting in four unique phages of prophage origin that showed different ranges of lytic activity. We also analyzed the prophage content of 35 Burkholderia spp. clinical isolate genomes, and identified several prophages present in the genomes of multiple isolates of the same species. Finally, we observed that B. cenocepacia isolates were more phage-susceptible than Burkholderia multivorans isolates. Overall, our findings suggest that prophages present within Burkholderia spp. genomes are a potentially useful starting point for the isolation and development of novel phages for use in phage therapy.

Liberation of host heme by Clostridioides difficile-mediated damage enhances Enterococcus faecalis fitness during infection

IMPORTANCE

Clostridioides difficile and Enterococcus faecalis are two pathogens of great public health importance. Both bacteria colonize the human gastrointestinal tract where they are known to interact in ways that worsen disease outcomes. We show that the damage associated with C. difficile infection (CDI) releases nutrients that benefit E. faecalis. One particular nutrient, heme, allows E. faecalis to use oxygen to generate energy and grow better in the gut. Understanding the mechanisms of these interspecies interactions could inform therapeutic strategies for CDI.

Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

Machine learning to identify clinically relevant Candida yeast species

Fungal infections, especially due to Candida species, are on the rise. Multi-drug resistant organisms such as Candida auris are difficult and time consuming to identify accurately. Machine learning is increasingly being used in health care, especially in medical imaging. In this study, we evaluated the effectiveness of six convolutional neural networks (CNNs) to identify four clinically important Candida species. Wet-mounted images were captured using bright field live-cell microscopy followed by separating single-cells, budding-cells, and cell-group images which were then subjected to different machine learning algorithms (custom CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB7) to learn and predict Candida species. Among the six algorithms tested, the InceptionV3 model performed best in predicting Candida species from microscopy images. All models performed poorly on raw images obtained directly from the microscope. The performance of all models increased when trained on single and budding cell images. The InceptionV3 model identified budding cells of C. albicans, C. auris, C. glabrata (Nakaseomyces glabrata), and C. haemulonii in 97.0%, 74.0%, 68.0%, and 66.0% cases, respectively. For single cells of C. albicans, C. auris, C. glabrata, and C. haemulonii InceptionV3 identified 97.0%, 73.0%, 69.0%, and 73.0% cases, respectively. The sensitivity and specificity of InceptionV3 were 77.1% and 92.4%, respectively. Overall, this study provides proof of the concept that microscopy images from wet-mounted slides can be used to identify Candida yeast species using machine learning quickly and accurately.

The type VI secretion system of the emerging pathogen Stenotrophomonas maltophilia complex has antibacterial properties

IMPORTANCE

Infections with the opportunistic pathogen Stenotrophomonas maltophilia complex can be fatal for immunocompromised patients. The mechanisms used by the bacterium to compete against other prokaryotes are not well understood. We found that the type VI secretion system (T6SS) allows S. maltophilia complex to eliminate other bacteria and contributes to the competitive fitness against a co-infecting isolate. The presence of T6SS genes in isolates across the globe highlights the importance of this apparatus as a weapon in the antibacterial arsenal of S. maltophilia complex. The T6SS may confer survival advantages to S. maltophilia complex isolates in polymicrobial communities in both environmental settings and during infections.

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.

Evolution of extended-spectrum β-lactamase-producing ST131 Escherichia coli at a single hospital over 15 years

Escherichia coli belonging to sequence type ST131 constitute a globally distributed pandemic lineage that causes multidrug-resistant extra-intestinal infections. ST131 E. coli frequently produce extended-spectrum β-lactamases (ESBLs), which confer resistance to many β-lactam antibiotics and make infections difficult to treat. We sequenced the genomes of 154 ESBL-producing E. coli clinical isolates belonging to the ST131 lineage from patients at the University of Pittsburgh Medical Center (UPMC) between 2004 and 2018. Isolates belonged to the well described ST131 clades A (8%), B (3%), C1 (33%), and C2 (54%). An additional four isolates belonged to another distinct subclade within clade C and encoded genomic characteristics that have not been previously described. Time-dated phylogenetic analysis estimated that the most recent common ancestor (MRCA) for all clade C isolates from UPMC emerged around 1989, consistent with previous studies. We identified multiple genes potentially under selection in clade C, including the cell wall assembly gene ftsI, the LPS biosynthesis gene arnC, and the yersiniabactin uptake receptor fyuA. Diverse ESBL genes belonging to the blaCTX-M, blaSHV, and blaTEM families were identified; these genes were found at varying numbers of loci and in variable numbers of copies across isolates. Analysis of ESBL flanking regions revealed diverse mobile elements that varied by ESBL type. Overall, our findings show that ST131 subclades C1 and C2 dominated and were stably maintained among patients in the same hospital and uncover possible signals of ongoing adaptation within the clade C ST131 lineage.

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.

Nationwide Genomic Surveillance and Response to COVID-19: The VA SeqFORCE and SeqCURE Consortiums

Background

The US Department of Veterans Affairs (VA) has dedicated significant resources toward countering the COVID-19 pandemic. Sequencing for Research Clinical and Epidemiology (SeqFORCE) and Sequencing Collaborations United for Research and Epidemiology (SeqCURE) were developed as clinical and research consortiums, respectively, focused on the genetic COVID-19 surveillance.

Observations

Through genetic sequencing, VA SeqFORCE and SeqCURE collaborations contributed to the COVID-19 pandemic response and scientific understanding. Future directions for each program include the assessment of the unique impact of COVID-19 on the veteran population, as well as the adaptation of these programs to future infectious disease threats. We foresee the use of these established platforms beyond infectious diseases.

Conclusions

VA SeqFORCE and SeqCURE were established as clinical and research programs dedicated to sequencing COVID-19 as part of ongoing clinical and surveillance efforts. In the future, we anticipate that having these programs embedded within the largest integrated health care system in the US will enable the study of pathogens and pandemics beyond COVID-19 and at an unprecedented scale. The investment in these programs will form an integral part of our nation's response to emerging infectious diseases, with future applications to precision medicine and beyond.

Sensitivity of National Healthcare Safety Network definitions to capture healthcare-associated transmission identified by whole-genome sequencing surveillance

The National Healthcare Safety Network (NHSN) definitions are critical for standardizing healthcare-associated infection surveillance in US healthcare facilities. However, their use in accurately detecting healthcare-associated transmission (HAT) has not been measured. Using whole-genome sequencing surveillance data, we show that the NHSN has a sensitivity of 44.4% in detecting HAT.

Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician

BACKGROUND

Artificial intelligence (AI), machine learning and deep learning (including generative AI) are increasingly being investigated in the context of research and management of human infection.

OBJECTIVES

We summarise recent and potential future applications of AI and its relevance to clinical infection practice.

METHODS

1617 PubMed results were screened, with priority given to clinical trials, systematic reviews and meta-analyses. This narrative review focusses on studies using prospectively collected real-world data with clinical validation, and on research with translational potential, such as novel drug discovery and microbiome-based interventions.

RESULTS

There is some evidence of clinical utility of AI applied to laboratory diagnostics (e.g. digital culture plate reading, malaria diagnosis, antimicrobial resistance profiling), clinical imaging analysis (e.g. pulmonary tuberculosis diagnosis), clinical decision support tools (e.g. sepsis prediction, antimicrobial prescribing) and public health outbreak management (e.g. COVID-19). Most studies to date lack any real-world validation or clinical utility metrics. Significant heterogeneity in study design and reporting limits comparability. Many practical and ethical issues exist, including algorithm transparency and risk of bias.

CONCLUSIONS

Interest in and development of AI-based tools for infection research and management are undoubtedly gaining pace, although the real-world clinical utility to date appears much more modest.

In vitro activity of cefiderocol against Pseudomonas aeruginosa demonstrating evolved resistance to novel β-lactam/β-lactamase inhibitors

Background

Cefiderocol demonstrates excellent activity against MDR Pseudomonas aeruginosa; however, the activity against isolates from patients previously treated with β-lactam agents is unknown. We aimed to determine the activity of cefiderocol against P. aeruginosa collected before and after treatment with traditional β-lactams and new β-lactam/β-lactamase inhibitors.

Methods

Cefiderocol MICs were determined in triplicate in iron-depleted cation-adjusted Mueller-Hinton broth and compared with β-lactam MICs tested by standard methods. All isolates underwent WGS analysis to identify mutations associated with resistance.

Results

One hundred and seventy-eight P. aeruginosa isolates were evaluated; 48% (86/178) were non-susceptible to ceftazidime/avibactam, ceftolozane/tazobactam and/or imipenem/relebactam. The cefiderocol MIC50 and MIC90 were 0.12 and 1 mg/L, respectively. Median cefiderocol MICs did not vary against isolates classified as MDR, XDR, or those non-susceptible to ceftazidime/avibactam, ceftolozane/tazobactam and/or imipenem/relebactam when compared with non-MDR isolates. Against isolates collected from patients previously treated with ceftolozane/tazobactam, cefiderocol MICs were increased 4-fold compared with baseline. Cross-resistance to cefiderocol was identified in 21% (3/14) of patients who developed treatment-emergent resistance to ceftolozane/tazobactam. Overall, 6% (11/178) of isolates demonstrated cefiderocol MICs ≥2 mg/L, which were disproportionately collected from patients previously treated with ceftolozane/tazobactam (73%; 8/11). Isolates with reduced cefiderocol susceptibility harboured mutations in ampC, tonB-dependent receptors, the response regulator pirR and ftsI.

Conclusions

Cefiderocol demonstrates excellent in vitro activity against P. aeruginosa isolates exposed to other novel β-lactam agents; however, some exceptions were identified. Cross-resistance between cefiderocol and ceftolozane/tazobactam was evident, but not with ceftazidime/avibactam or imipenem/relebactam. Reduced cefiderocol susceptibility was mediated by mutations in ampC and tonB-dependent receptors.

Does Emerging Carbapenem Resistance in Acinetobacter baumannii Increase the Case Fatality Rate? Systematic Review and Meta-Analysis

BACKGROUND

In the era of rising carbapenem resistance, we aimed to investigate the change in mortality rate and positivity of carbapenemase genes in Acinetobacter baumannii.

METHODS

Preferred Reporting Items for Systematic Review (PRISMA) guidelines were adopted in this systematic review. Our literature search included the Cochrane Library, Pubmed, Scopus, Web of Science, Medline, Tubitak TR Dizin, and Harman databases for studies dating back from 2003 to 2023 reporting bloodstream A. baumannii infections in Türkiye. A simple linear regression model was used to determine the association between resistance, mortality, and time.

RESULTS

A total of 1717 studies were identified through a literature search, and 21 articles were selected based on the availability of the data regarding mortality and resistance rate (four articles) or the molecular epidemiology of carbapenem-resistant A. baumannii (17 articles) in Türkiye. From 2007 to 2018, the carbapenem resistance rate increased (p = 0.025). The OXA-23 and OXA-58 positivities were inversely correlated (p = 0.025).

CONCLUSIONS

Despite the emergence of carbapenem resistance, mortality did not increase in parallel, which may be due to improved medical advancements or the fitness cost of bacteria upon prolonged antimicrobial exposure. Therefore, we suggest further global research with the foresight to assess clonal relatedness that might affect the carbapenem resistance rate.

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).

The Type VI Secretion System of the Emerging Pathogen Stenotrophomonas maltophilia has Antibacterial Properties

Antagonistic behaviors between bacterial cells can have profound effects on microbial populations and disease outcomes. Polymicrobial interactions may be mediated by contact-dependent proteins with antibacterial properties. The Type VI Secretion System (T6SS) is a macromolecular weapon used by Gram-negative bacteria to translocate proteins into adjacent cells. The T6SS is used by pathogens to escape immune cells, eliminate commensal bacteria, and facilitate infection. Stenotrophomonas maltophilia is a Gram-negative opportunistic pathogen that causes a wide range of infections in immunocompromised patients and infects the lungs of patients with cystic fibrosis. Infections with the bacterium can be deadly and are challenging to treat because many isolates are multidrug-resistant. We found that globally dispersed S. maltophilia clinical and environmental strains possess T6SS genes. We demonstrate that the T6SS of an S. maltophilia patient isolate is active and can eliminate other bacteria. Furthermore, we provide evidence that the T6SS contributes to the competitive fitness of S. maltophilia against a co-infecting Pseudomonas aeruginosa isolate, and that the T6SS alters the cellular organization of S. maltophilia and P. aeruginosa co-cultures. This study expands our knowledge of the mechanisms employed by S. maltophilia to secrete antibacterial proteins and compete against other bacteria.

IMPORTANCE

Infections with the opportunistic pathogen Stenotrophomonas maltophilia can be fatal for immunocompromised patients. The mechanisms used by the bacterium to compete against other prokaryotes are not well understood. We found that the T6SS allows S. maltophilia to eliminate other bacteria and contributes to the competitive fitness against a co-infecting isolate. The presence of T6SS genes in isolates across the globe highlights the importance of this apparatus as a weapon in the antibacterial arsenal of S. maltophilia . The T6SS may confer survival advantages to S. maltophilia isolates in polymicrobial communities in both environmental settings and during infections.

Clinical Diagnostics of Bacterial Infections and Their Resistance to Antibiotics-Current State and Whole Genome Sequencing Implementation Perspectives

Antimicrobial resistance (AMR), defined as the ability of microorganisms to withstand antimicrobial treatment, is responsible for millions of deaths annually. The rapid spread of AMR across continents warrants systematic changes in healthcare routines and protocols. One of the fundamental issues with AMR spread is the lack of rapid diagnostic tools for pathogen identification and AMR detection. Resistance profile identification often depends on pathogen culturing and thus may last up to several days. This contributes to the misuse of antibiotics for viral infection, the use of inappropriate antibiotics, the overuse of broad-spectrum antibiotics, or delayed infection treatment. Current DNA sequencing technologies offer the potential to develop rapid infection and AMR diagnostic tools that can provide information in a few hours rather than days. However, these techniques commonly require advanced bioinformatics knowledge and, at present, are not suited for routine lab use. In this review, we give an overview of the AMR burden on healthcare, describe current pathogen identification and AMR screening methods, and provide perspectives on how DNA sequencing may be used for rapid diagnostics. Additionally, we discuss the common steps used for DNA data analysis, currently available pipelines, and tools for analysis. Direct, culture-independent sequencing has the potential to complement current culture-based methods in routine clinical settings. However, there is a need for a minimum set of standards in terms of evaluating the results generated. Additionally, we discuss the use of machine learning algorithms regarding pathogen phenotype detection (resistance/susceptibility to an antibiotic).

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.

The future of automated infection detection: Innovation to transform practice (Part III/III)

Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true "Learning Healthcare System" that will support near-real-time quality improvement efforts and advance the scientific basis for the practice of infection control.

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.

Unsuspected Clonal Spread of Methicillin-Resistant Staphylococcus aureus Causing Bloodstream Infections in Hospitalized Adults Detected Using Whole Genome Sequencing

BACKGROUND

Though detection of transmission clusters of methicillin-resistant Staphylococcus aureus (MRSA) infections is a priority for infection control personnel in hospitals, the transmission dynamics of MRSA among hospitalized patients with bloodstream infections (BSIs) has not been thoroughly studied. Whole genome sequencing (WGS) of MRSA isolates for surveillance is valuable for detecting outbreaks in hospitals, but the bioinformatic approaches used are diverse and difficult to compare.

METHODS

We combined short-read WGS with genotypic, phenotypic, and epidemiological characteristics of 106 MRSA BSI isolates collected for routine microbiological diagnosis from inpatients in 2 hospitals over 12 months. Clinical data and hospitalization history were abstracted from electronic medical records. We compared 3 genome sequence alignment strategies to assess similarity in cluster ascertainment. We conducted logistic regression to measure the probability of predicting prior hospital overlap between clustered patient isolates by the genetic distance of their isolates.

RESULTS

While the 3 alignment approaches detected similar results, they showed some variation. A gene family-based alignment pipeline was most consistent across MRSA clonal complexes. We identified 9 unique clusters of closely related BSI isolates. Most BSIs were healthcare associated and community onset. Our logistic model showed that with 13 single-nucleotide polymorphisms, the likelihood that any 2 patients in a cluster had overlapped in a hospital was 50%.

CONCLUSIONS

Multiple clusters of closely related MRSA isolates can be identified using WGS among strains cultured from BSI in 2 hospitals. Genomic clustering of these infections suggests that transmission resulted from a mix of community spread and healthcare exposures long before BSI diagnosis.

Enterococci enhance Clostridioides difficile pathogenesis

Enteric pathogens are exposed to a dynamic polymicrobial environment in the gastrointestinal tract¹. This microbial community has been shown to be important during infection, but there are few examples illustrating how microbial interactions can influence the virulence of invading pathogens². Here we show that expansion of a group of antibiotic-resistant, opportunistic pathogens in the gut-the enterococci-enhances the fitness and pathogenesis of Clostridioides difficile. Through a parallel process of nutrient restriction and cross-feeding, enterococci shape the metabolic environment in the gut and reprogramme C. difficile metabolism. Enterococci provide fermentable amino acids, including leucine and ornithine, which increase C. difficile fitness in the antibiotic-perturbed gut. Parallel depletion of arginine by enterococci through arginine catabolism provides a metabolic cue for C. difficile that facilitates increased virulence. We find evidence of microbial interaction between these two pathogenic organisms in multiple mouse models of infection and patients infected with C. difficile. These findings provide mechanistic insights into the role of pathogenic microbiota in the susceptibility to and the severity of C. difficile infection.

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.

Transmission visualizations of healthcare infection clusters: A scoping review

OBJECTIVE

To evaluate infectious pathogen transmission data visualizations in outbreak publications.

DESIGN

Scoping review.

METHODS

Medline was searched for outbreak investigations of infectious diseases within healthcare facilities that included ≥1 data visualization of transmission using data observable by an infection preventionist showing temporal and/or spatial relationships. Abstracted data included the nature of the cluster(s) (pathogen, scope of transmission, and individuals involved) and data visualization characteristics including visualization type, transmission elements, and software.

RESULTS

From 1,957 articles retrieved, we analyzed 30 articles including 37 data visualizations. The median cluster size was 20.5 individuals (range, 7-1,963) and lasted a median of 214 days (range, 12-5,204). Among the data visualization types, 10 (27%) were floor-plan transmission maps, 6 (16%) were timelines, 11 (30%) were transmission networks, 3 (8%) were Gantt charts, 4 (11%) were cluster map, and 4 (11%) were other types. In addition, 26 data visualizations (70%) contained spatial elements, 26 (70%) included person type, and 19 (51%) contained time elements. None of the data visualizations contained contagious periods and only 2 (5%) contained symptom-onset date.

CONCLUSIONS

The data visualizations of healthcare-associated infectious disease outbreaks in the systematic review were diverse in type and visualization elements, though no data visualization contained all elements important to deriving hypotheses about transmission pathways. These findings aid in understanding the visualizing transmission pathways by describing essential elements of the data visualization and will inform the creation of a standardized mapping tool to aid in earlier initiation of interventions to prevent transmission.