Evaluation of a BioFire multiplex PCR panel for detection of joint infections using retrospective and prospectively collected specimens.

The BioFire Joint Infection Panel (JI panel) is a newly FDA-approved multiplex PCR assay for detection of common bone and joint pathogens with 39 targets which include select Gram-positive and Gram-negative bacteria, yeast, and antimicrobial resistance genes. We evaluated the performance of the JI panel in detecting joint infections in our patient population. Sixty-three frozen, residual joint fluid specimens were retrospectively tested using the JI panel. An additional 104 residual joint fluid specimens were de-identified and prospectively tested within 1 week of collection. Results from routine bacterial cultures were used as the reference standard, which included inoculation to agar plates and blood culture bottles. For the frozen specimens, the JI panel showed a positive percent agreement (PPA) of 92.8% and a negative percent agreement (NPA) of 97.1%. PPA was 71.4% and NPA was 94.8% for fresh specimens. A total of 12 discrepancies were observed among the 167 specimens tested. The JI panel demonstrated good overall agreement with routine culture for the detection of joint infections and may improve timely diagnosis when used in conjunction with bacterial culture. However, potential false-positive and false-negative results were observed in both retrospective and prospective testing of specimens.IMPORTANCEThe BioFire JI panel is a new commercially available multiplex PCR assay for detecting common pathogens causing bone and joint infections. The test is performed directly on joint fluids with a fast turnaround time of 1 hour. Our study shows that while the JI panel overall shows good agreement with routine culture, discrepancies were observed in 7% of cases and results should be interpreted with appropriate clinical context.

Automated detection of methicillin-resistant Staphylococcus aureus with the MRSA CHROM imaging application on BD Kiestra Total Lab Automation System.

The virulence of methicillin-resistant Staphylococcus aureus (MRSA) and its potentially fatal outcome necessitate rapid and accurate detection of patients colonized with MRSA in healthcare settings. Using the BD Kiestra Total Lab Automation (TLA) System in conjunction with the MRSA Application (MRSA App), an imaging application that uses artificial intelligence to interpret colorimetric information (mauve-colored colonies) indicative of MRSA pathogen presence on CHROMagar chromogenic media, anterior nares specimens from three sites were evaluated for the presence of mauve-colored colonies. Results obtained with the MRSA App were compared to manual reading of agar plate images by proficient laboratory technologists. Of 1,593 specimens evaluated, 1,545 (96.98%) were concordant between MRSA App and laboratory technologist reading for the detection of MRSA growth [sensitivity 98.15% (95% CI, 96.03, 99.32) and specificity 96.69% (95% CI, 95.55, 97.60)]. This multi-site study is the first evaluation of the MRSA App in conjunction with the BD Kiestra TLA System. Using the MRSA App, our results showed 98.15% sensitivity and 96.69% specificity for the detection of MRSA from anterior nares specimens. The MRSA App, used in conjunction with laboratory automation, provides an opportunity to improve laboratory efficiency by reducing laboratory technologists' labor associated with the review and interpretation of cultures.

Uncharacterized and lineage-specific accessory genes within the Proteus mirabilis pan-genome landscape.

Proteus mirabilis is a Gram-negative bacterium recognized for its unique swarming motility and urease activity. A previous proteomic report on four strains hypothesized that, unlike other Gram-negative bacteria, P. mirabilis may not exhibit significant intraspecies variation in gene content. However, there has not been a comprehensive analysis of large numbers of P. mirabilis genomes from various sources to support or refute this hypothesis. We performed comparative genomic analysis on 2,060 Proteus genomes. We sequenced the genomes of 893 isolates recovered from clinical specimens from three large US academic medical centers, combined with 1,006 genomes from NCBI Assembly and 161 genomes assembled from Illumina reads in the public domain. We used average nucleotide identity (ANI) to delineate species and subspecies, core genome phylogenetic analysis to identify clusters of highly related P. mirabilis genomes, and pan-genome annotation to identify genes of interest not present in the model P. mirabilis strain HI4320. Within our cohort, Proteus is composed of 10 named species and 5 uncharacterized genomospecies. P. mirabilis can be subdivided into three subspecies; subspecies 1 represented 96.7% (1,822/1,883) of all genomes. The P. mirabilis pan-genome includes 15,399 genes outside of HI4320, and 34.3% (5,282/15,399) of these genes have no putative assigned function. Subspecies 1 is composed of several highly related clonal groups. Prophages and gene clusters encoding putatively extracellular-facing proteins are associated with clonal groups. Uncharacterized genes not present in the model strain P. mirabilis HI4320 but with homology to known virulence-associated operons can be identified within the pan-genome. IMPORTANCE Gram-negative bacteria use a variety of extracellular facing factors to interact with eukaryotic hosts. Due to intraspecies genetic variability, these factors may not be present in the model strain for a given organism, potentially providing incomplete understanding of host-microbial interactions. In contrast to previous reports on P. mirabilis, but similar to other Gram-negative bacteria, P. mirabilis has a mosaic genome with a linkage between phylogenetic position and accessory genome content. P. mirabilis encodes a variety of genes that may impact host-microbe dynamics beyond what is represented in the model strain HI4320. The diverse, whole-genome characterized strain bank from this work can be used in conjunction with reverse genetic and infection models to better understand the impact of accessory genome content on bacterial physiology and pathogenesis of infection.

Proceedings of the Clinical Microbiology Open 2018 and 2019 - a Discussion about Emerging Trends, Challenges, and the Future of Clinical Microbiology.

Clinical Microbiology Open (CMO), a meeting supported by the American Society for Microbiology's Clinical and Public Health Microbiology Committee (CPHMC) and Corporate Council, provides a unique interactive platform for leaders from diagnostic microbiology laboratories, industry, and federal agencies to discuss the current and future state of the clinical microbiology laboratory. The purpose is to leverage the group's diverse views and expertise to address critical challenges, and discuss potential collaborative opportunities for diagnostic microbiology, through the utilization of varied resources. The first and second CMO meetings were held in 2018 and 2019, respectively. Discussions were focused on the diagnostic potential of innovative technologies and laboratory diagnostic stewardship, including expansion of next-generation sequencing into clinical diagnostics, improvement and advancement of molecular diagnostics, emerging diagnostics, including rapid antimicrobial susceptibility and point of care testing (POCT), harnessing big data through artificial intelligence, and staffing in the clinical microbiology laboratory. Shortly after CMO 2019, the coronavirus disease 2019 (COVID-19) pandemic further highlighted the need for the diagnostic microbiology community to work together to utilize and expand on resources to respond to the pandemic. The issues, challenges, and potential collaborative efforts discussed during the past two CMO meetings proved critical in addressing the COVID-19 response by diagnostic laboratories, industry partners, and federal organizations. Planning for a third CMO (CMO 2022) is underway and will transition from a discussion-based meeting to an action-based meeting. The primary focus will be to reflect on the lessons learned from the COVID-19 pandemic and better prepare for future pandemics.

Performance Evaluation of the BD SARS-CoV-2 Reagents for the BD MAX System.

Nucleic acid amplification testing (NAAT) for SARS-CoV-2 is the standard approach for confirming COVID-19 cases. This study compared results between two emergency use authorization (EUA) NAATs, with two additional EUA NAATs utilized for discrepant testing. The limits of detection (LOD) for the BD SARS-CoV-2 reagents for the BD MAX system (MAX SARS-CoV-2 assay), the bioMérieux BioFire respiratory panel 2.1 (BioFire SARS-CoV-2 assay), the Roche cobas SARS-CoV-2 assay (cobas SARS-CoV-2 assay), and the Hologic Aptima SARS-CoV-2 assay Panther (Aptima SARS-CoV-2 assay) NAAT systems were determined using a total of 84 contrived nasopharyngeal specimens with 7 target levels for each comparator. The positive and negative percent agreement (PPA and NPA, respectively) of the MAX SARS-CoV-2 assay, compared to the Aptima SARS-CoV-2 assay, was evaluated in a postmarket clinical study utilizing 708 nasopharyngeal specimens collected from suspected COVID-19 cases. Discordant testing was achieved using the cobas and BioFire SARS-CoV-2 NAATs. In this study, the measured LOD for the MAX SARS-CoV-2 assay (251 copies/ml; 95% confidence interval [CI], 186 to 427) was comparable to the cobas SARS-CoV-2 assay (298 copies/ml; 95% CI, 225 to 509) and the BioFire SARS-CoV-2 assay (302 copies/ml; 95% CI, 219 to 565); the Aptima SARS-CoV-2 assay had an LOD of 612 copies/ml (95% CI, 474 to 918). The MAX SARS-CoV-2 assay had a PPA of 100% (95% CI, 97.3% to 100.0%) and an NPA of 96.7% (95% CI, 94.9% to 97.9%) compared to the Aptima SARS-CoV-2 assay. The clinical performance of the MAX SARS-CoV-2 assay agreed with another sensitive EUA assay.

Transfusion-Transmitted Malaria: Two Pediatric Cases From the United States and Their Relevance in an Increasingly Globalized World.

In non-endemic settings, transfusion-transmitted malaria (TTM) is rare but potentially fatal and becoming more common with globalization. We present two pediatric cases that demonstrate donor screening using questionnaires is subject to error and that TTM should be considered with fever following numerous transfusions in children, particularly sickle cell patients.

From Testing to Decision-Making: A Data-Driven Analytics COVID-19 Response.

In March 2020, NorthShore University Health System laboratories mobilized to develop and validate polymerase chain reaction based testing for detection of SARS-CoV-2. Using laboratory data, NorthShore University Health System created the Data Coronavirus Analytics Research Team to track activities affected by SARS-CoV-2 across the organization. Operational leaders used data insights and predictions from Data Coronavirus Analytics Research Team to redeploy critical care resources across the hospital system, and real-time data were used daily to make adjustments to staffing and supply decisions. Geographical data were used to triage patients to other hospitals in our system when COVID-19 detected pavilions were at capacity. Additionally, one of the consequences of COVID-19 was the inability for patients to receive elective care leading to extended periods of pain and uncertainty about a disease or treatment. After shutting down elective surgeries beginning in March of 2020, NorthShore University Health System set a recovery goal to achieve 80% of our historical volumes by October 1, 2020. Using the Data Coronavirus Analytics Research Team, our operational and clinical teams were able to achieve 89% of our historical volumes a month ahead of schedule, allowing rapid recovery of surgical volume and financial stability. The Data Coronavirus Analytics Research Team also was used to demonstrate that the accelerated recovery period had no negative impact with regard to iatrogenic COVID-19 infection and did not result in increased deep vein thrombosis, pulmonary embolisms, or cerebrovascular accident. These achievements demonstrate how a coordinated and transparent data-driven effort that was built upon a robust laboratory testing capability was essential to the operational response and recovery from the COVID-19 crisis.

The Value and Institutional Impact of an In-System Laboratory Testing During the COVID-19 Pandemic.

In-system clinical laboratories have proven themselves to be a fundamentally important resource to their institutions during the COVID-19 pandemic of the past year. The ability to provide SARS-CoV-2 molecular testing to our hospital system allowed us to offer the best possible care to our patients, and to support neighboring hospitals and nursing homes. In-house testing led to significant revenue enhancement to the laboratory and institution, and attracted new patients to the system. Timely testing of inpatients allowed the majority who did not have COVID-19 infection to be removed from respiratory and contact isolation, conserving valuable personal protective equipment and staff resources at a time that both were in short supply. As 2020 evolved and our institution restarted delivery of routine care, the availability of in-system laboratory testing to deliver both accurate and timely results was absolutely critical. In this article, we attempt to demonstrate the value and impact of an in-system laboratory during the COVID-19 pandemic. A strong in-house laboratory service was absolutely critical to institutional operational and financial success during 2020, and will ensure resiliency in the future as well.

Evaluating the Rapid Emergence of Daptomycin Resistance in Corynebacterium: a Multicenter Study.

Members of the genus Corynebacterium are increasingly recognized as pathobionts and can be very resistant to antimicrobial agents. Previous studies have demonstrated that Corynebacterium striatum can rapidly develop high-level daptomycin resistance (HLDR) (MIC, ≥256 µg/ml). Here, we conducted a multicenter study to assay for this in vitro phenotype in diverse Corynebacterium species. Corynebacterium clinical isolates (n = 157) from four medical centers were evaluated. MIC values to daptomycin, vancomycin, and telavancin were determined before and after overnight exposure to daptomycin to identify isolates able to rapidly develop daptomycin nonsusceptibility. To investigate assay reproducibility, 18 isolates were evaluated at three study sites. In addition, the stability of daptomycin nonsusceptibility was tested using repeated subculture without selective pressure. The impact of different medium brands was also investigated. Daptomycin nonsusceptibility emerged in 12 of 23 species evaluated in this study (C. afermentans, C. amycolatum, C. aurimucosum, C. bovis, C. jeikeium, C. macginleyi, C. pseudodiphtheriticum, C. resistens, C. simulans, C. striatum, C. tuberculostearicum, and C. ulcerans) and was detected in 50 of 157 (31.8%) isolates tested. All isolates displayed low (susceptible) MIC values to vancomycin and telavancin before and after daptomycin exposure. Repeated subculture demonstrated that 2 of 9 isolates (22.2%) exhibiting HLDR reverted to a susceptible phenotype. Of 30 isolates tested on three medium brands, 13 (43.3%) had differences in daptomycin MIC values between brands. Multiple Corynebacterium species can rapidly develop daptomycin nonsusceptibility, including HLDR, after a short daptomycin exposure period.

Panels and Syndromic Testing in Clinical Microbiology.

Syndromic panels have allowed clinical microbiology laboratories to rapidly identify bacteria, viruses, fungi, and parasites and are now fully integrated into the standard testing practices of many clinical laboratories. To maximize the benefit of syndromic testing, laboratories must implement strict measures to ensure that syndromic panels are being used responsibly. This article discusses commercially available syndromic panels, the benefits and limitations of testing, and how diagnostic and laboratory stewardship can be used to optimize testing and improve patient care while keeping costs at a minimum.

Responding to the Challenges of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Perspectives from the Association for Molecular Pathology Infectious Disease Subdivision Leadership.

Clinical molecular laboratory professionals are at the frontline of the response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, providing accurate, high-quality laboratory results to aid in diagnosis, treatment, and epidemiology. In this role, we have encountered numerous regulatory, reimbursement, supply-chain, logistical, and systems challenges that we have struggled to overcome to fulfill our calling to provide patient care. In this Perspective from the Association for Molecular Pathology Infectious Disease Subdivision Leadership team, we review how our members have risen to these challenges, provide recommendations for managing the current pandemic, and outline the steps we can take as a community to better prepare for future pandemics.

Machine Learning Takes Laboratory Automation to the Next Level.

Clinical microbiology laboratories face challenges with workload and understaffing that other clinical laboratory sections have addressed with automation. In this issue of the Journal of Clinical Microbiology, M. L. Faron, B. W. Buchan, R. F. Relich, J. Clark, and N. A. Ledeboer (J Clin Microbiol 58:e01683-19, 2020, https://doi.org/10.1128/JCM.01683-19) evaluate the performance of automated image analysis software to screen urine cultures for further workup according to their total number of CFU. Urine cultures are the highest volume specimen type for most laboratories, so this software has the potential for tremendous gains in laboratory efficiency and quality due to the consistency of colony quantification.

Faropenem resistance causes in vitro cross-resistance to carbapenems in ESBL-producing Escherichia coli.

OBJECTIVE: Faropenem is an oral penem drug with activity against Gram-positive and Gram-negative bacteria, including CTX-M-15-type extended spectrum beta-lactamase (ESBL)-producing Enterobacteriales and anaerobic bacteria. As there are structural similarities, there is concern for the development of carbapenem cross-resistance; however, there are no studies confirming this. This study examined whether in vitro development of faropenem resistance in Escherichia coli isolates would result in cross-resistance to carbapenems. METHODS: Four well-characterized E. coli isolates from the US Centers for Disease Control and Prevention antibiotic resistance isolate bank were utilized. Three isolates (NSF1, NSF2 and NSF3) are ESBL producers (CTX-M-15) and one (NSF4) is pan-susceptible. Faropenem minimum inhibitory concentrations (MICs) were determined and resistance was induced by serial passaging in increasing concentrations of faropenem. Susceptibility to carbapenems was determined and whole-genome sequencing (WGS) was performed to identify the underlying genetic mechanism leading to carbapenem resistance. RESULTS: Faropenem MIC increased from 1 mg/L to 64 mg/L within 10 days for NSF2 and NSF4 isolates, and from 2 mg/L to 64 mg/L within 7 days for NSF1 and NSF3 isolates. Reduced carbapenem susceptibility (ertapenem MIC ≥8 mg/L, doripenem/meropenem ≥2 mg/L and imipenem ≥1 mg/L) developed among three CTX-M-15-producing isolates that were faropenem-resistant, but not in NSF4 isolate that lacked ESBL enzyme. WGS analysis revealed non-synonymous changes in the ompC gene among three CTX-M-15-producing isolates, and a single nucleotide polymorphism (SNP) in the envZ gene in NSF4 isolate. CONCLUSION: Induced resistance to faropenem causes cross-resistance to carbapenems among E. coli isolates containing CTX-M-15-type ESBL enzymes.

Diagnostic Challenges and Laboratory Considerations for Pediatric Sepsis.

BACKGROUND: Sepsis is a leading cause of death for children in the US and worldwide. There is a lack of consensus how sepsis is clinically defined, and sepsis definitions and diagnostic guidelines for the pediatric population have remained unchanged for more than a decade now. Current pediatric definitions are largely based on adult guidelines and expert opinion rather than evidence based on outcomes in the pediatric populations. Without a clear definition of sepsis, it is challenging to evaluate the performance of new laboratory tests on the diagnosis and management of sepsis. CONTENT: This review provides an overview of common etiologies of sepsis in pediatric populations, challenges in defining and diagnosing pediatric sepsis, and current laboratory tests used to identify and monitor sepsis. Strengths and limitations of emerging diagnostic strategies will also be discussed. SUMMARY: Currently there is no single biomarker that can accurately diagnose or predict sepsis. Current biomarkers such as C-reactive protein and lactate are neither sensitive nor specific for diagnosing sepsis. New biomarkers and rapid pathogen identification assays are much needed. Procalcitonin, although having some limitations, has emerged as a biomarker with demonstrated utility in management of sepsis in adults. Parallel studies analyzing the utility of procalcitonin in pediatric populations are lagging but have shown potential to affect sepsis care in pediatric populations. Multibiomarker approaches and stepwise algorithms show promise in the management of pediatric sepsis. However, a major hurdle is the lack of validated clinical criteria for classification of pediatric sepsis, which is necessary for the development of well-designed studies that can assess the clinical impact of these emerging biomarkers.

Total Laboratory Automation: What Is Gained, What Is Lost, and Who Can Afford It?

The first clinical microbiology laboratory in the United States adopted total automation for bacteriology processing in 2014. Since then, others have followed with installation of either the BD Kiestra TLA or the Copan WASPLab. This article discusses commercially available automated systems in the United States; why automation is needed; and quality improvements, efficiency, and cost savings associated with automation. After learning how these systems are used, gains and losses experienced, and how one can afford the most expensive equipment ever purchased for clinical microbiology laboratories, the question is, how can one afford not to purchase one of these microbiology automation systems?