August 14, 2020 | New COVID-19 risk model developed by Cleveland Clinic and published findings from a retesting study. Plus, symptom trackers deployed by colleges for Back to School and quantified synthetic virus fragments available from NIST.
Cleveland Clinic researchers have developed and validated a risk prediction model (called a nomogram) that can help physicians predict which patients who have recently tested positive for SARS-CoV-2 are at greatest risk for hospitalization. This new model, published in PLOS One, is the second COVID-19-related nomogram that the research team has developed. Their earlier model forecasts an individual patient's likelihood of testing positive for the virus. The team's newest model was developed and validated using retrospective patient data from more than 4,500 patients who tested positive for COVID-19 at Cleveland Clinic locations in Northeast Ohio and Florida during a three-month time period (early March to early June). Data scientists used statistical algorithms to transform data from registry patients' electronic medical records into the risk prediction model. DOI: 10.1371/journal.pone.0237419
The 40-hospital University of Pittsburgh Medical Center health system reported its findings on clinician-directed retesting of patients for presence of SARS-CoV-2, the virus that causes COVID-19, in the journal Infection Control & Hospital Epidemiology. While retesting was uncommon, the UPMC analysis found that patients positive for COVID-19 stayed positive for an average of three weeks and repeating tests in patients who were initially negative very rarely led to a positive result. DOI:10.1017/ice.2020.413
Researchers at the National Institute of Standards and Technology (NIST) have produced synthetic gene fragments from SARS-CoV-2. This material, which is non-infectious and safe to handle, can help manufacturers produce more accurate and reliable diagnostic tests for the disease. A negative result COVID-19 test does not necessarily mean that a person is disease-free. It could be that the amount of virus is too low for the test to detect, which is especially possible during the first days after catching the virus. The NIST team produced synthetic fragments of the virus’s genes, and then measured very carefully how many fragments are in each vial they ship. Using this material, researchers can measure sensitivity by running tests against known quantities of viral RNA. They can also use it to develop more sensitive tests or new types of tests that are faster or easier to administer. NIST is providing this material at no cost to researchers, kit manufacturers and testing labs to help them achieve accurate measurements. More information.
Zyter, a leading digital health and IoT-enablement platform, announced today the availability of the Zyter COVID-19 Suite, consisting of four integrated digital screening, thermal imaging, contact tracing and monitoring tools to help organizations keep personnel safe from spreading the COVID-19 virus in their work environment. Also available as stand-alone solutions, each component of the suite is available now and can be deployed quickly in hospitals, healthcare settings, office buildings, stadiums, across school campuses, retail distribution centers, enterprise offices, and any organization in which large groups of people are in close proximity. More information.
To prepare for fall instruction, universities across the Commonwealth of Virginia will deploy the COVID Health Check a COVID-19 symptom and exposure tracking tool developed by a team at the George Mason College of Health and Human Services. Since its launch at Mason, the tool has helped the University detect early cases by tracking symptoms and possible exposure to the virus. Members of Mason’s community complete an assessment of symptoms and exposure using the Health Check™, which provides a recommended course of action. Users also complete a Daily Symptoms Journal to provide on-going outbreak tracking which can help the university trace contacts when a member of the community test positive. Since the tool’s launch, more than 16,000 students, faculty, and staff have completed the assessment and more than 1,200 symptoms and exposures have been tracked. Press release.
Researchers at the University of Notre Dame, the University of Pittsburgh, and Guangdong Provincial People’s Hospital in China are working together to identify the visual features of COVID-19-related pneumonia through analysis of 3D data from CT scans. The team is working to combine the analysis software with off-the-shelf hardware for a light-weight mobile device that can be easily and immediately integrated in clinics around the country. 3D CT scans are so large, it’s nearly impossible to detect specific features and extract them efficiently and accurately on plug-and-play mobile devices. So the team is developing a novel method inspired by Independent Component Analysis, using a statistical architecture to break each image into smaller segments, which will allow deep neural networks to target COVID-related features within large 3D images. Press release.