The epidemiology of pediatric outpatient acute respiratory tract infections in the US: a multi-facility analysis of multiplex PCR testing from 2018 to 2023.

IMPORTANCE: Post-pandemic, it is essential to understand the epidemiology of pediatric acute respiratory tract infections (ARTIs). Our multi-facility study elucidates the outpatient epidemiology of pediatric ARTI using highly multiplexed PCR testing, providing critical insights into the evolving landscape of the etiological agents with a particular focus on the years following the emergence of SARS-CoV-2. Utilizing data from two different multiplex PCR panels, our research provides a comprehensive analysis of respiratory pathogen positivity from 2018 to 2023. Our findings indicate that over half of the annual test results identified at least one pathogen, primarily of viral origin. Intriguingly, despite the surge in testing during the COVID-19 pandemic, pathogen detection rates remain similar to the pre-pandemic era. These data hold significant implications for directing antimicrobial stewardship strategies, curbing unnecessary antibiotic use in pediatric respiratory diseases, and the value of multiplex PCR testing in the outpatient setting among pediatrics.

Candida auris rates in blood culture on the rise: results of US surveillance.

Candida auris is an emerging pathogen that poses a significant public health risk. Its multidrug resistance has led to high mortality, making rapid detection crucial for effective treatment and prevention of transmission. Recent data from the Centers for Disease Control and Prevention indicate a substantial increase in C. auris cases in the United States, with a 95% rise in 2021. To provide an update on the detection rates of C. auris, we analyzed blood culture results from a near real-time cloud-based surveillance network, BioFire Trend. From January 2021 to April 2023, 34 C. auris detections were observed. The analysis showed a notable increase in detections in 2023 compared to previous years. The detection rate in 2023 was higher in all four US Census Regions, except for the Northeast, where it remained constant. The findings emphasize the continuous rise in C. auris cases and highlight the importance of near real-time surveillance systems in monitoring this emerging pathogen.

Epidemiology of Antimicrobial Resistance Among Blood and Respiratory Specimens in the United States Using Genotypic Analysis From a Cloud-Based Population Surveillance Network.

Background: Antimicrobial resistance (AMR) surveillance is critical in informing strategies for infection control in slowing the spread of resistant organisms and for antimicrobial stewardship in the care of patients. However, significant challenges exist in timely and comprehensive AMR surveillance. Methods: Using BioFire Pneumonia and Blood Culture 2 Panels data from BioFire Syndromic Trends (Trend), a cloud-based population surveillance network, we described the detection rate of AMR among a US cohort. Data were included from 2019 to 2021 for Gram-positive and -negative organisms and their related AMR genomic-resistant determinants as well as for detections of Candida auris. Regional and between panel AMR detection rate differences were compared. In addition, AMR codetections and detection rate per organism were evaluated for Gram-negative organisms. Results: A total of 26 912 tests were performed, primarily in the Midwest. Overall, AMR detection rate was highest in the South and more common for respiratory specimens than blood. methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus detection rates were 34.9% and 15.9%, respectively, whereas AMR for Gram-negative organisms was lower with 7.0% CTX-M and 2.9% carbapenemases. In addition, 10 mcr-1 and 4 C auris detections were observed. For Gram-negative organisms, Klebsiella pneumoniae and Escherichia coli were most likely to be detected with an AMR gene, and of Gram-negative organisms, K pneumoniae was most often associated with 2 or more AMR genes. Conclusions: Our study provides important in-depth evaluation of the epidemiology of AMR among respiratory and blood specimens for Gram-positive and -negative organism in the United States. The Trend surveillance network allows for near real-time surveillance of AMR.

A Hierarchical Genotyping Framework Using DNA Melting Temperatures Applied to Adenovirus Species Typing.

Known genetic variation, in conjunction with post-PCR melting curve analysis, can be leveraged to provide increased taxonomic detail for pathogen identification in commercial molecular diagnostic tests. Increased taxonomic detail may be used by clinicians and public health decision-makers to observe circulation patterns, monitor for outbreaks, and inform testing practices. We propose a method for expanding the taxonomic resolution of PCR diagnostic systems by incorporating a priori knowledge of assay design and sequence information into a genotyping classification model. For multiplexed PCR systems, this framework is generalized to incorporate information from multiple assays to increase classification accuracy. An illustrative hierarchical classification model for human adenovirus (HAdV) species was developed and demonstrated ~95% cross-validated accuracy on a labeled dataset. The model was then applied to a near-real-time surveillance dataset in which deidentified adenovirus detected patient test data from 2018 through 2021 were classified into one of six adenovirus species. These results show a marked change in both the predicted prevalence for HAdV and the species makeup with the onset of the COVID-19 pandemic. HAdV-B decreased from a pre-pandemic predicted prevalence of up to 40% to less than 5% in 2021, while HAdV-A and HAdV-F species both increased in predicted prevalence.

Enterovirus D68 outbreak detection through a syndromic disease epidemiology network.

BACKGROUND: In 2014, enterovirus D68 (EV-D68) was responsible for an outbreak of severe respiratory illness in children, with 1,153 EV-D68 cases reported across 49 states. Despite this, there is no commercial assay for its detection in routine clinical care. BioFire® Syndromic Trends (Trend) is an epidemiological network that collects, in near real-time, deidentified. BioFire test results worldwide, including data from the BioFire® Respiratory Panel (RP). OBJECTIVES: Using the RP version 1.7 (which was not explicitly designed to differentiate EV-D68 from other picornaviruses), we formulate a model, Pathogen Extended Resolution (PER), to distinguish EV-D68 from other human rhinoviruses/enteroviruses (RV/EV) tested for in the panel. Using PER in conjunction with Trend, we survey for historical evidence of EVD68 positivity and demonstrate a method for prospective real-time outbreak monitoring within the network. STUDY DESIGN: PER incorporates real-time polymerase chain reaction metrics from the RPRV/EV assays. Six institutions in the United States and Europe contributed to the model creation, providing data from 1,619 samples spanning two years, confirmed by EV-D68 gold-standard molecular methods. We estimate outbreak periods by applying PER to over 600,000 historical Trend RP tests since 2014. Additionally, we used PER as a prospective monitoring tool during the 2018 outbreak. RESULTS: The final PER algorithm demonstrated an overall sensitivity and specificity of 87.1% and 86.1%, respectively, among the gold-standard dataset. During the 2018 outbreak monitoring period, PER alerted the research network of EV-D68 emergence in July. One of the first sites to experience a significant increase, Nationwide Children's Hospital, confirmed the outbreak and implemented EV-D68 testing at the institution in response. Applying PER to the historical Trend dataset to determine rates among RP tests, we find three potential outbreaks with predicted regional EV-D68 rates as high as 37% in 2014, 16% in 2016, and 29% in 2018. CONCLUSIONS: Using PER within the Trend network was shown to both accurately predict outbreaks of EV-D68 and to provide timely notifications of its circulation to participating clinical laboratories.