Improving Nasopharyngeal Swab Technique via Simulation for Frontline Workers.

OBJECTIVES/HYPOTHESIS: Nasopharyngeal swabs currently remain the gold standard for COVID-19 sample collection. A surge in testing volume has resulted in a large number of health care workers who are unfamiliar with nasal anatomy performing this test, which can lead to improper collection practices culminating in false-negative results and complications. Therefore, we aimed to assess the accuracy and educational potential of a realistic 3D-printed nasal swab simulator to expedite health care workers' skill acquisition. STUDY DESIGN: Prospective pre-post interventional study. METHODS: A nasal swab task trainer (NSTT) was developed to scale from computed tomography data with a deviated septum. Frontline workers at COVID-19 testing sites in Ontario, Canada, were recruited to use the NSTT for nasopharyngeal swab training. Integrated video recording capability allowed participants to self-evaluate procedure accuracy. A five-point Likert scale was collected regarding the NSTT's educational value and procedural fidelity. RESULTS: Sixty-two frontline workers included in the study were primarily registered nurses (52%) or paramedics (16%). Following simulator use, self-assessed accuracy improved in 77% of all participants and 100% of participants who expressed low confidence before training. Ninety-four percent reported that the NSTT provided a complete educational experience, and 82% regarded the system as a more effective training approach than what is currently available. Eighty-one indicated that the simulator should be used at all COVID-19 testing sites, with 77% stating province-wide implementation was warranted. CONCLUSIONS: The nasal swab task trainer is an effective educational tool that appears well-suited for improved skill acquisition in COVID-19 testing and may be useful for training other nasal swab applications. LEVEL OF EVIDENCE: 3 Laryngoscope, 2022.

Safety and efficacy of mycophenolate in COVID-19: a nonrandomised prospective study in western India.

Background: Antivirals and immunosuppressive agents are used with variable success in the treatment of COVID-19. Mycophenolate, an inhibitor of enzyme inosine monophosphate dehydrogenase, is an immunosuppressant used to prevent allograft rejection and other autoimmune diseases. Few laboratory studies have also reported antiviral properties of mycophenolate. The current study tried to assess the safety and efficacy of mycophenolate in patients hospitalised with COVID-19. Methods: This was a prospective non-randomised open label study with the objective to assess the effect of addition of mycophenolate to the standard of care on mortality due to COVID-19 and duration of hospital stay. The target study population was comprised of patients requiring inpatient treatment for COVID-19 during the period from Jan 15-April 15, 2021. The study was registered with Clinical Trial Registry of India (CTRI/2021/01/030477, registered on date-14/01/2021). Adult patients (n = 106) requiring hospitalisation for COVID-19 received mycophenolate, 360 mg, one tablet daily for one month. Mycophenolate was initiated within 48 h of the diagnosis of SARS-CoV-2 infection by RT‒PCR. While patients who did not consent for mycophenolate (n = 106), received only standard of care, and were considered as control group. The relevant clinical data including NEWS2 scores and high-resolution computed tomography of the thorax were collected and analysed. Findings: The mortality and hospital stay were significantly lower in the study group compared to the control group. Mycophenolate significantly reduced mortality after adjustment for other predictors (adjusted odds ratio: 0.082 with 95% CI: 0.012-0.567). Mycophenolate was an independent predictor of survival in patients hospitalised due to COVID-19. There was also no evidence of secondary bacterial infections and post-COVID complications. Interpretation: Mycophenolate administration is safe in COVID-19. Mycophenolate reduces mortality and duration of hospital stay in patients with COVID-19. Funding: Shri Janai Research Foundation, India.

Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review.

BACKGROUND: The application of machine learning algorithms for assessing the auditory brainstem response has gained interest over recent years with a considerable number of publications in the literature. In this systematic review, we explore how machine learning has been used to develop algorithms to assess auditory brainstem responses. A clear and comprehensive overview is provided to allow clinicians and researchers to explore the domain and the potential translation to clinical care. METHODS: The systematic review was performed based on PRISMA guidelines. A search was conducted of PubMed, IEEE-Xplore, and Scopus databases focusing on human studies that have used machine learning to assess auditory brainstem responses. The duration of the search was from January 1, 1990, to April 3, 2021. The Covidence systematic review platform (www.covidence.org) was used throughout the process. RESULTS: A total of 5812 studies were found through the database search and 451 duplicates were removed. The title and abstract screening process further reduced the article count to 89 and in the proceeding full-text screening, 34 articles met our full inclusion criteria. CONCLUSION: Three categories of applications were found, namely neurologic diagnosis, hearing threshold estimation, and other (does not relate to neurologic or hearing threshold estimation). Neural networks and support vector machines were the most commonly used machine learning algorithms in all three categories. Only one study had conducted a clinical trial to evaluate the algorithm after development. Challenges remain in the amount of data required to train machine learning models. Suggestions for future research avenues are mentioned with recommended reporting methods for researchers.

An open repository of real-time COVID-19 indicators.

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.