Jeffrey A. Kline, Indiana University School of Medicine
Carlos A. Camargo, Harvard Medical School
D. Mark Courtney, UT Southwestern Medical Center
Christopher Kabrhel, Harvard Medical School
Kristen E. Nordenholz, University of Colorado School of Medicine
Thomas Aufderheide, Medical College of Wisconsin
Joshua J. Baugh, Harvard Medical School
David G. Beiser, The University of Chicago
Christopher L. Bennett, Stanford University School of Medicine
Joseph Bledsoe, Intermountain Healthcare
Edward Castillo, University of California, San Diego
Makini Chisolm Straker, Icahn School of Medicine at Mount Sinai
Elizabeth M. Goldberg, The Warren Alpert Medical School
Hans House, University of Iowa Carver College of Medicine
Stacey House, Washington University School of Medicine in St. Louis
Timothy Jang, David Geffen School of Medicine at UCLA
Stephen C. Lim, LSU Health Sciences Center- New OrleansFollow
Troy E. Madsen, University of Utah School of Medicine
Danielle M. McCarthy, Northwestern University Feinberg School of Medicine
Andrew Meltzer, The George Washington University School of Medicine and Health Sciences
Stephen Moore, Peen State Milton S. Hershey Medical Center
Craig Newgard, Oregon Health and Science University
Justine Pagenhardt, West Virginia University School of Medicine
Katherine L. Pettit, Indiana University School of Medicine
Michael S. Pulia, University of Wisconsin School of Medicine and Public Health
Michael A. Puskarish, University of Minnesota
Lauren T. Southerland, Ohio State University Medical Center
Scott Sparks, Riverside Regional Medical Center
Danielle Turner-Lawrence, Beaumont Health
Marie Vrablik

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Objectives Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. Methods Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. Results Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age50 years, measured temperature37.5C, oxygen saturation 95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and-1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79 0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8 96.3%), specificity of 20.0% (19.0 21.0%), negative likelihood ratio of 0.22 (0.19 0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., 75% probability with +5 or more points). Conclusion Criteria that are available at the point of care can accurately predict the probability of SARSCoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.

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3 March


Public Library of Science

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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