TY - JOUR
T1 - Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021
AU - Kline, Jeffrey A.
AU - Camargo, Carlos A.
AU - Courtney, D. Mark
AU - Kabrhel, Christopher
AU - Nordenholz, Kristen E.
AU - Aufderheide, Thomas
AU - Baugh, Joshua J.
AU - Beiser, David G.
AU - Bennett, Christopher L.
AU - Bledsoe, Joseph
AU - Castillo, Edward
AU - Straker, Makini Chisolm
AU - Goldberg, Elizabeth M.
AU - House, Hans
AU - House, Stacey
AU - Jang, Timothy
AU - Lim, Stephen C.
AU - Madsen, Troy E.
AU - McCarthy, Danielle M.
AU - Meltzer, Andrew
AU - Moore, Stephen
AU - Newgard, Craig
AU - Pagenhardt, Justine
AU - Pettit, Katherine L.
AU - Pulia, Michael S.
AU - Puskarich, Michael A.
AU - Southerland, Lauren T.
AU - Sparks, Scott
AU - Lawrence, Danielle Turner
AU - Vrablik, Marie
AU - Wang, Alfred
AU - Weekes, Anthony J.
AU - Westafer, Lauren
AU - Wilburn, John
N1 - Publisher Copyright:
© 2021 Public Library of Science. All rights reserved.
PY - 2021/3
Y1 - 2021/3
N2 - 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.
AB - 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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U2 - 10.1371/journal.pone.0248438
DO - 10.1371/journal.pone.0248438
M3 - Article
C2 - 33690722
AN - SCOPUS:85102621835
SN - 1932-6203
VL - 16
JO - PloS one
JF - PloS one
IS - 3 March
M1 - e0248438
ER -