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.

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.

Intraoperative multi-exposure speckle imaging of cerebral blood flow.

Multiple studies have demonstrated that laser speckle contrast imaging (LSCI) has high potential to be a valuable cerebral blood flow monitoring technique during neurosurgery. However, the quantitative accuracy and sensitivity of LSCI is limited, and highly dependent on the exposure time. An extension to LSCI called multi-exposure speckle imaging (MESI) overcomes these limitations, and was evaluated intraoperatively in patients undergoing brain tumor resection. This clinical study ( n = 8) recorded multiple exposure times from the same cortical tissue area spanning 0.5-20 ms, and evaluated images individually as single-exposure LSCI and jointly using the MESI model. This study demonstrated that the MESI estimates provided the broadest flow sensitivity for sampling the flow magnitude in the human brain, closely followed by the shorter exposure times. Conservation of flow analysis on vascular bifurcations was used to validate physiological accuracy, with highly conserved flow estimates (<10%) from both MESI and 1 ms LSCI ( n = 14 branches). The MESI model had high goodness-of-fit with proper image calibration and acquisition, and was used to monitor blood flow changes after tissue cautery. Results from this study demonstrate that intraoperative MESI can be performed with high quantitative accuracy and sensitivity for cerebral blood flow monitoring.

Low-cost laser speckle contrast imaging of blood flow using a webcam.

Laser speckle contrast imaging has become a widely used tool for dynamic imaging of blood flow, both in animal models and in the clinic. Typically, laser speckle contrast imaging is performed using scientific-grade instrumentation. However, due to recent advances in camera technology, these expensive components may not be necessary to produce accurate images. In this paper, we demonstrate that a consumer-grade webcam can be used to visualize changes in flow, both in a microfluidic flow phantom and in vivo in a mouse model. A two-camera setup was used to simultaneously image with a high performance monochrome CCD camera and the webcam for direct comparison. The webcam was also tested with inexpensive aspheric lenses and a laser pointer for a complete low-cost, compact setup ($90, 5.6 cm length, 25 g). The CCD and webcam showed excellent agreement with the two-camera setup, and the inexpensive setup was used to image dynamic blood flow changes before and after a targeted cerebral occlusion.