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.

Alpha to Omicron: Disease Severity and Clinical Outcomes of Major SARS-CoV-2 Variants.

BACKGROUND: Four severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants predominated in the United States since 2021. Understanding disease severity related to different SARS-CoV-2 variants remains limited. METHOD: Viral genome analysis was performed on SARS-CoV-2 clinical isolates circulating March 2021 through March 2022 in Cleveland, Ohio. Major variants were correlated with disease severity and patient outcomes. RESULTS: In total 2779 patients identified with either Alpha (n 1153), Gamma (n 122), Delta (n 808), or Omicron variants (n 696) were selected for analysis. No difference in frequency of hospitalization, intensive care unit (ICU) admission, and death were found among Alpha, Gamma, and Delta variants. However, patients with Omicron infection were significantly less likely to be admitted to the hospital, require oxygen, or admission to the ICU (2 12.8, P .001; 2 21.6, P .002; 2 9.6, P .01, respectively). In patients whose vaccination status was known, a substantial number had breakthrough infections with Delta or Omicron variants (218/808 [26.9] and 513/696 [73.7], respectively). In breakthrough infections, hospitalization rate was similar regardless of variant by multivariate analysis. No difference in disease severity was identified between Omicron subvariants BA.1 and BA.2. CONCLUSIONS: Disease severity associated with Alpha, Gamma, and Delta variants is comparable while Omicron infections are significantly less severe. Breakthrough disease is significantly more common in patients with Omicron infection.

Tailored Treatment Based on Helicobacter pylori Genetic Markers of Resistance Is Associated With Higher Eradication Success.

INTRODUCTION: Increasing antimicrobial resistance with Helicobacter pylori infection has focused efforts to tailor eradication therapy based on identifying genetic markers of resistance to predict antimicrobial susceptibility. METHODS: In this retrospective study, we report the effect of routine inclusion of antimicrobial susceptibility testing and recommendations for eradication therapy with gastric specimens with H. pylori . RESULTS: The use of a recommended treatment regimen based on genetic markers of resistance was associated with an 84% rate of eradication success and 4.4 greater odds of eradication relative to unrecommended treatment. DISCUSSION: This is the first study describing the use of H. pylori genetic resistance testing as standard of care.

The Use of Machine Learning for Image Analysis Artificial Intelligence in Clinical Microbiology.

The growing transition to digital microbiology in clinical laboratories creates the opportunity to interpret images using software. Software analysis tools can be designed to use human-curated knowledge and expert rules, but more novel artificial intelligence (AI) approaches such as machine learning (ML) are being integrated into clinical microbiology practice. These image analysis AI (IAAI) tools are beginning to penetrate routine clinical microbiology practice, and their scope and impact on routine clinical microbiology practice will continue to grow. This review separates the IAAI applications into 2 broad classification categories: (i) rare event detection/classification or (ii) score-based/categorical classification. Rare event detection can be used for screening purposes or for final identification of a microbe including microscopic detection of mycobacteria in a primary specimen, detection of bacterial colonies growing on nutrient agar, or detection of parasites in a stool preparation or blood smear. Score-based image analysis can be applied to a scoring system that classifies images in toto as its output interpretation and examples include application of the Nugent score for diagnosing bacterial vaginosis and interpretation of urine cultures. The benefits, challenges, development, and implementation strategies of IAAI tools are explored. In conclusion, IAAI is beginning to impact the routine practice of clinical microbiology, and its use can enhance the efficiency and quality of clinical microbiology practice. Although the future of IAAI is promising, currently IAAI only augments human effort and is not a replacement for human expertise.

Multicenter Evaluation of the BIOFIRE Blood Culture Identification 2 Panel for Detection of Bacteria, Yeasts, and Antimicrobial Resistance Genes in Positive Blood Culture Samples.

Diagnostic tools that can rapidly identify and characterize microbes growing in blood cultures are important components of clinical microbiology practice because they help to provide timely information that can be used to optimize patient management. This publication describes the bioMérieux BIOFIRE Blood Culture Identification 2 (BCID2) Panel clinical study that was submitted to the U.S. Food & Drug Administration. Results obtained with the BIOFIRE BCID2 Panel were compared to standard-of-care (SoC) results, sequencing results, PCR results, and reference laboratory antimicrobial susceptibility testing results to evaluate the accuracy of its performance. Results for 1,093 retrospectively and prospectively collected positive blood culture samples were initially enrolled, and 1,074 samples met the study criteria and were included in the final analyses. The BIOFIRE BCID2 Panel demonstrated an overall sensitivity of 98.9% (1,712/1,731) and an overall specificity of 99.6% (33,592/33,711) for Gram-positive bacteria, Gram-negative bacteria and yeast targets which the panel is designed to detect. One hundred eighteen off-panel organisms, which the BIOFIRE BCID2 Panel is not designed to detect, were identified by SoC in 10.6% (114/1,074) of samples. The BIOFIRE BCID2 Panel also demonstrated an overall positive percent agreement (PPA) of 97.9% (325/332) and an overall negative percent agreement (NPA) of 99.9% (2,465/2,767) for antimicrobial resistance determinants which the panel is designed to detect. The presence or absence of resistance markers in Enterobacterales correlated closely with phenotypic susceptibility and resistance. We conclude that the BIOFIRE BCID2 Panel produced accurate results in this clinical trial.

Intra-host mutation rate of acute SARS-CoV-2 infection during the initial pandemic wave.

SARS-CoV-2 mutation is minimized through a proofreading function encoded by NSP-14. Most estimates of the SARS-CoV-2 mutation rate are derived from population based sequence data. Our understanding of SARS-CoV-2 evolution might be enhanced through analysis of intra-host viral mutation rates in specific populations. Viral genome analysis was performed between paired samples and mutations quantified at allele frequencies (AF) ≥ 0.25, ≥ 0.5 and ≥ 0.75. Mutation rate was determined employing F81 and JC69 evolution models and compared between isolates with (ΔNSP-14) and without (wtNSP-14) non-synonymous mutations in NSP-14 and by patient comorbidity. Forty paired samples with median interval of 13 days [IQR 8.5-20] were analyzed. The estimated mutation rate by F81 modeling was 93.6 (95%CI 90.8-96.4], 40.7 (95%CI 38.9-42.6) and 34.7 (95%CI 33.0-36.4) substitutions/genome/year at AF ≥ 0.25, ≥ 0.5, ≥ 0.75 respectively. Mutation rate in ΔNSP-14 were significantly elevated at AF ≥ 0.25 vs wtNSP-14. Patients with immune comorbidities had higher mutation rate at all allele frequencies. Intra-host SARS-CoV-2 mutation rates are substantially higher than those reported through population analysis. Virus strains with altered NSP-14 have accelerated mutation rate at low AF. Immunosuppressed patients have elevated mutation rate at all AF. Understanding intra-host virus evolution will aid in current and future pandemic modeling.

Clinical and Infection Prevention Applications of Severe Acute Respiratory Syndrome Coronavirus 2 Genotyping: An Infectious Diseases Society of America/American Society for Microbiology Consensus Review Document.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged into a world of maturing pathogen genomics, with >2 million genomes sequenced at this writing. The rise of more transmissible variants of concern that affect vaccine and therapeutic effectiveness has led to widespread interest in SARS-CoV-2 evolution. Clinicians are also eager to take advantage of the information provided by SARS-CoV-2 genotyping beyond surveillance purposes. Here, we review the potential role of SARS-CoV-2 genotyping in clinical care. The review covers clinical use cases for SARS-CoV-2 genotyping, methods of SARS-CoV-2 genotyping, assay validation and regulatory requirements, clinical reporting for laboratories, and emerging issues in clinical SARS-CoV-2 sequencing. While clinical uses of SARS-CoV-2 genotyping are currently limited, rapid technological change along with a growing ability to interpret variants in real time foretell a growing role for SARS-CoV-2 genotyping in clinical care as continuing data emerge on vaccine and therapeutic efficacy.

Meningococcal Urethritis: Old and New.

Neisseria meningitidis is a common commensal bacterium found in the respiratory tract, but it can also cause severe, invasive disease. Vaccines have been employed which have been successful in helping to prevent invasive disease caused by encapsulated N. meningitidis from the A, C, W, Y, and B serogroups. Currently, nonencapsulated N. meningitidis groups are more common commensals in the population than in the prevaccine era. One emerging nonencapsulated group of bacteria is the U.S. N. meningitidis urethritis clade (US_NmUC), which can cause meningococcal urethritis in men. US_NmUC has unique genotypic and phenotypic features that may increase its fitness in the male urethra. It is diagnostically challenging to identify and distinguish meningococcal urethritis from Neisseria gonorrhoeae, as the clinical presentation and microbiological findings are overlapping. In this review, the history of meningococcal urethritis, emergence of US_NmUC, laboratory diagnosis, and clinical treatment are all explored.

Multicenter Evaluation of the Acuitas AMR Gene Panel for Detection of an Extended Panel of Antimicrobial Resistance Genes among Bacterial Isolates.

The Acuitas antimicrobial resistance (AMR) gene panel is a qualitative, multiplex, nucleic acid-based in vitro diagnostic test for the detection and differentiation of 28 antimicrobial resistance markers associated with not susceptible results (NS; i.e., intermediate or resistant) to one or more antimicrobial agents among cultured isolates of select Enterobacterales, Pseudomonas aeruginosa, and Enterococcus faecalis. This study was conducted at four sites and included testing of 1,224 deidentified stocks created from 584 retrospectively collected isolates and 83 prospectively collected clinical isolates. The Acuitas results were compared with a combined reference standard including whole-genome sequencing, organism identification, and phenotypic antimicrobial susceptibility testing. The positive percent agreement (PPA) for FDA-cleared AMR targets ranged from 94.4% for MCR-1 to 100% for armA, CTX-M-2, DHA, IMP, OXA-9, SHV, vanA, and VEB. The negative percent agreement (NPA) for the majority of targets was ≥99%, except for AAC, AAD, CMY-41, P. aeruginosa gyrA mutant, Sul1, Sul2, and TEM targets (range, 96.5% to 98.5%). Three AMR markers did not meet FDA inclusion criteria (GES, SPM, and MCR-2). For each organism, 1 to 22 AMR targets met the minimum reportable PPA/NPA and correlated with ≥80% positive predictive value with associated NS results for at least one agent (i.e., the probability of an organism carrying an AMR marker testing NS to the associated agent). We demonstrate that the Acuitas AMR gene panel is an accurate method to detect a broad array of AMR markers among cultured isolates. The AMR markers were further associated with expected NS results for specific agent-organism combinations.

Clinical and Infection Prevention Applications of Severe Acute Respiratory Syndrome Coronavirus 2 Genotyping: an Infectious Diseases Society of America/American Society for Microbiology Consensus Review Document.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged into a world of maturing pathogen genomics, with more than 2 million genomes sequenced at the time of writing. The rise of more transmissible variants of concern that impact vaccine and therapeutic effectiveness has led to widespread interest in SARS-CoV-2 evolution. Clinicians are also eager to take advantage of the information provided by SARS-CoV-2 genotyping beyond surveillance purposes. Here, we review the potential role of SARS-CoV-2 genotyping in clinical care. The review covers clinical use cases for SARS-CoV-2 genotyping, methods of SARS-CoV-2 genotyping, assay validation and regulatory requirements, and clinical reporting for laboratories, as well as emerging issues in clinical SARS-CoV-2 sequencing. While clinical uses of SARS-CoV-2 genotyping are currently limited, rapid technological change along with a growing ability to interpret variants in real time foretells a growing role for SARS-CoV-2 genotyping in clinical care as continuing data emerge on vaccine and therapeutic efficacy.

Partial ORF1ab Gene Target Failure with Omicron BA.2.12.1.

Mutations in the genome of SARS-CoV-2 can affect the performance of molecular diagnostic assays. In some cases, such as S-gene target failure, the impact can serve as a unique indicator of a particular SARS-CoV-2 variant and provide a method for rapid detection. Here, we describe partial ORF1ab gene target failure (pOGTF) on the cobas SARS-CoV-2 assays, defined by a ≥2-thermocycle delay in detection of the ORF1ab gene compared to that of the E-gene. We demonstrate that pOGTF is 98.6% sensitive and 99.9% specific for SARS-CoV-2 lineage BA.2.12.1, an emerging variant in the United States with spike L452Q and S704L mutations that may affect transmission, infectivity, and/or immune evasion. Increasing rates of pOGTF closely mirrored rates of BA.2.12.1 sequences uploaded to public databases, and, importantly, increasing local rates of pOGTF also mirrored increasing overall test positivity. Use of pOGTF as a proxy for BA.2.12.1 provides faster tracking of the variant than whole-genome sequencing and can benefit laboratories without sequencing capabilities.

Deep Convolutional Neural Networks Implementation for the Analysis of Urine Culture.

BACKGROUND: Urine culture images collected using bacteriology automation are currently interpreted by technologists during routine standard-of-care workflows. Machine learning may be able to improve the harmonization of and assist with these interpretations. METHODS: A deep learning model, BacterioSight, was developed, trained, and tested on standard BD-Kiestra images of routine blood agar urine cultures from 2 different medical centers. RESULTS: BacterioSight displayed performance on par with standard-of-care-trained technologist interpretations. BacterioSight accuracy ranged from 97% when compared to standard-of-care (single technologist) and reached 100% when compared to a consensus reached by a group of technologists (gold standard in this study). Variability in image interpretation by trained technologists was identified and annotation "fuzziness" was quantified and found to correlate with reduced confidence in BacterioSight interpretation. Intra-testing (training and testing performed within the same institution) performed well giving Area Under the Curve (AUC) ≥0.98 for negative and positive plates, whereas, cross-testing on images (trained on one institution's images and tested on images from another institution) showed decreased performance with AUC ≥0.90 for negative and positive plates. CONCLUSIONS: Our study provides a roadmap on how BacterioSight or similar deep learning prototypes may be implemented to screen for microbial growth, flag difficult cases for multi-personnel review, or auto-verify a subset of cultures with high confidence. In addition, our results highlight image interpretation variability by trained technologist within an institution and globally across institutions. We propose a model in which deep learning can enhance patient care by identifying inherent sample annotation variability and improving personnel training.

Stenotrophomonas maltophilia Susceptibility Testing Challenges and Strategies.

Stenotrophomonas maltophilia is intrinsically resistant to many beta-lactam antibiotics, including carbapenems, and is resistant to aminoglycosides, which limits the therapeutic repertoire for managing S. maltophilia infections. Additionally, employing automated in vitro susceptibility testing of S. maltophilia is challenging because commercial test systems' performance is limited (A. Khan, C. A. Arias, A. Abbott, J. Dien Bard, et al., J Clin Microbiol 59:e00654-21, 2021, https://doi.org/10.1128/JCM.00654-21). This commentary will briefly discuss the opportunity to use automated commercial susceptibility testing systems with S. maltophilia, with a focus on how to implement their use practically while mitigating risk of error.

Specificity of SARS-CoV-2 Real-Time PCR Improved by Deep Learning Analysis.

Real-time PCR (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data are traditionally analyzed by estimating the threshold cycle (CT ) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the CT value are based on approximations that do not adequately account for the stochastic variations of the fluorescence signal that is detected during RT-PCR. Less common deviations become more apparent as the sample size increases, as is the case in the current SARS-CoV-2 pandemic. In this work, we employ a method independent of CT value to interpret RT-PCR data. In this novel approach, we built and trained a deep learning model, qPCRdeepNet, to analyze the fluorescent readings obtained during RT-PCR. We describe how this model can be deployed as a quality assurance tool to monitor result interpretation in real time. The model's performance with the TaqPath COVID19 Combo Kit assay, widely used for SARS-CoV-2 detection, is described. This model can be applied broadly for the primary interpretation of RT-PCR assays and potentially replace the CT interpretive paradigm.

Considerations from the College of American Pathologists for Implementation of an Assay for SARS-CoV-2 Testing after a Change in Regulatory Status.

The U.S. Food & Drug Administration (FDA) regulates the marketing of manufacturers' in vitro diagnostic tests (IVDs), including assays for the detection of SARS-CoV-2. The U.S. government's Clinical Laboratory Improvement Amendments (CLIA) of 1988 regulates the studies that a clinical diagnostic laboratory needs to perform for an IVD before placing it into use. Until recently, the FDA has authorized the marketing of SARS-CoV-2 IVDs exclusively through the Emergency Use Authorization (EUA) pathway. The regulatory landscape continues to evolve, and IVDs will eventually be required to pass through conventional non-EUA FDA review pathways once the emergency declaration is terminated, in order to continue to be marketed as an IVD in the United States. When FDA regulatory status of an IVD changes or is anticipated to change, the laboratory should review manufacturer information and previously performed internal verification studies to determine what, if any, additional studies are needed before implementing the non-EUA version of the IVD in accordance with CLIA regulations. Herein, the College of American Pathologists' Microbiology Committee provides guidance for how to approach regulatory considerations when an IVD is converted from EUA to non-EUA status.

Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review.

BACKGROUND: Modern artificial intelligence (AI) and machine learning (ML) methods are now capable of completing tasks with performance characteristics that are comparable to those of expert human operators. As a result, many areas throughout healthcare are incorporating these technologies, including in vitro diagnostics and, more broadly, laboratory medicine. However, there are limited literature reviews of the landscape, likely future, and challenges of the application of AI/ML in laboratory medicine. CONTENT: In this review, we begin with a brief introduction to AI and its subfield of ML. The ensuing sections describe ML systems that are currently in clinical laboratory practice or are being proposed for such use in recent literature, ML systems that use laboratory data outside the clinical laboratory, challenges to the adoption of ML, and future opportunities for ML in laboratory medicine. SUMMARY: AI and ML have and will continue to influence the practice and scope of laboratory medicine dramatically. This has been made possible by advancements in modern computing and the widespread digitization of health information. These technologies are being rapidly developed and described, but in comparison, their implementation thus far has been modest. To spur the implementation of reliable and sophisticated ML-based technologies, we need to establish best practices further and improve our information system and communication infrastructure. The participation of the clinical laboratory community is essential to ensure that laboratory data are sufficiently available and incorporated conscientiously into robust, safe, and clinically effective ML-supported clinical diagnostics.

Monitoring Ceftazidime-Avibactam and Aztreonam Concentrations in the Treatment of a Bloodstream Infection Caused by a Multidrug-Resistant Enterobacter sp. Carrying Both Klebsiella pneumoniae Carbapenemase-4 and New Delhi Metallo-ß-Lactamase-1.

In an infection with an Enterobacter sp. isolate producing Klebsiella pneumoniae Carbapenemase-4 and New Delhi Metallo-ß-Lactamase-1 in the United States, recognition of the molecular basis of carbapenem resistance allowed for successful treatment by combining ceftazidime-avibactam and aztreonam. Antimicrobial synergy testing and therapeutic drug monitoring assessed treatment adequacy.

ARGONAUT II Study of the In Vitro Activity of Plazomicin against Carbapenemase-Producing Klebsiella pneumoniae.

Plazomicin was tested against 697 recently acquired carbapenem-resistant Klebsiella pneumoniae isolates from the Great Lakes region of the United States. Plazomicin MIC50 and MIC90 values were 0.25 and 1 mg/liter, respectively; 680 isolates (97.6%) were susceptible (MICs of ≤2 mg/liter), 9 (1.3%) intermediate (MICs of 4 mg/liter), and 8 (1.1%) resistant (MICs of >32 mg/liter). Resistance was associated with rmtF-, rmtB-, or armA-encoded 16S rRNA methyltransferases in all except 1 isolate.

Computer Vision and Artificial Intelligence Are Emerging Diagnostic Tools for the Clinical Microbiologist.

Artificial intelligence (AI) is increasingly becoming an important component of clinical microbiology informatics. Researchers, microbiologists, laboratorians, and diagnosticians are interested in AI-based testing because these solutions have the potential to improve a test's turnaround time, quality, and cost. A study by Mathison et al. used computer vision AI (B. A. Mathison, J. L. Kohan, J. F. Walker, R. B. Smith, et al., J Clin Microbiol 58:e02053-19, 2020, https://doi.org/10.1128/JCM.02053-19), but additional opportunities for AI applications exist within the clinical microbiology laboratory. Large data sets within clinical microbiology that are amenable to the development of AI diagnostics include genomic information from isolated bacteria, metagenomic microbial findings from primary specimens, mass spectra captured from cultured bacterial isolates, and large digital images, which is the medium that Mathison et al. chose to use. AI in general and computer vision in specific are emerging tools that clinical microbiologists need to study, develop, and implement in order to improve clinical microbiology.

Understanding, Verifying, and Implementing Emergency Use Authorization Molecular Diagnostics for the Detection of SARS-CoV-2 RNA.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has brought a new wave of challenges to health care, particularly in the area of rapid diagnostic test development and implementation. The diagnosis of acute coronavirus disease 2019 (COVID-19) is critically dependent on the detection of SARS-CoV-2 RNA from clinical specimens (e.g., nasopharyngeal swabs). While laboratory-developed testing for SARS-CoV-2 is an essential component of diagnostic testing for this virus, the majority of clinical microbiology laboratories are dependent on commercially available SARS-CoV-2 molecular assays. In contrast to assays approved or cleared by the U.S. Food and Drug Administration (FDA) for in vitro diagnostic use, assays for the detection of SARS-CoV-2 nucleic acids have emergency use authorization (EUA) from the FDA. Outside of highly specialized academic and commercial laboratory settings, clinical microbiology laboratories are likely unfamiliar with the EUA classification, and thus, assay verification can be daunting. Further compounding anxiety for laboratories are major issues with the supply chain that are dramatically affecting the availability of test reagents and requiring laboratories to implement multiple commercial EUA tests. Here, we describe guidance for the verification of assays with EUA for the detection of SARS-CoV-2 nucleic acid from clinical specimens.