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DC Field | Value | Language |
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dc.contributor.author | Lundon, Dara J | - |
dc.contributor.author | Kelly, Brian D | - |
dc.contributor.author | Nair, Sujit | - |
dc.contributor.author | Bolton, Damien M | - |
dc.contributor.author | Patel, Gopi | - |
dc.contributor.author | Reich, David | - |
dc.contributor.author | Tewari, Ashutosh | - |
dc.date | 2021 | - |
dc.date.accessioned | 2021-05-24T05:45:17Z | - |
dc.date.available | 2021-05-24T05:45:17Z | - |
dc.date.issued | 2021-04 | - |
dc.identifier.citation | Frontiers in Medicine 2021; 8: 563465 | en |
dc.identifier.issn | 2296-858X | |
dc.identifier.uri | https://ahro.austin.org.au/austinjspui/handle/1/26566 | - |
dc.description.abstract | Background: Detecting and isolating cases of COVID-19 are amongst the key elements listed by the WHO to reduce transmission. This approach has been reported to reduce those symptomatic with COVID-19 in the population by over 90%. Testing is part of a strategy that will save lives. Testing everyone maybe ideal, but it is not practical. A risk tool based on patient demographics and clinical parameters has the potential to help identify patients most likely to test negative for SARS-CoV-2. If effective it could be used to aide clinical decision making and reduce the testing burden. Methods: At the time of this analysis, a total of 9,516 patients with symptoms suggestive of Covid-19, were assessed and tested at Mount Sinai Institutions in New York. Patient demographics, clinical parameters and test results were collected. A robust prediction pipeline was used to develop a risk tool to predict the likelihood of a positive test for Covid-19. The risk tool was analyzed in a holdout dataset from the cohort and its discriminative ability, calibration and net benefit assessed. Results: Over 48% of those tested in this cohort, had a positive result. The derived model had an AUC of 0.77, provided reliable risk prediction, and demonstrated a superior net benefit than a strategy of testing everybody. When a risk cut-off of 70% was applied, the model had a negative predictive value of 96%. Conclusion: Such a tool could be used to help aide but not replace clinical decision making and conserve vital resources needed to effectively tackle this pandemic. | en |
dc.language.iso | eng | |
dc.subject | COVID-19 | en |
dc.subject | SARS–CoV-2 | en |
dc.subject | clinical decision aid | en |
dc.subject | resource allocation | en |
dc.subject | risk prediction | en |
dc.title | A COVID-19 Test Triage Tool, Predicting Negative Results and Reducing the Testing Burden on Healthcare Systems During a Pandemic. | en |
dc.type | Journal Article | en |
dc.identifier.journaltitle | Frontiers in Medicine | en |
dc.identifier.affiliation | Department of Infectious Diseases, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY, United States | en |
dc.identifier.affiliation | Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States | en |
dc.identifier.affiliation | Department of Urology, Icahn School of Medicine, Mount Sinai Hospitals, New York, NY, United States | en |
dc.identifier.affiliation | Urology | en |
dc.identifier.doi | 10.3389/fmed.2021.563465 | en |
dc.type.content | Text | en |
dc.identifier.pubmedid | 33996839 | |
local.name.researcher | Bolton, Damien M | |
item.openairetype | Journal Article | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Urology | - |
crisitem.author.dept | Urology | - |
Appears in Collections: | Journal articles |
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