Journal article
Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021
PloS one, Vol.16(3), pp.e0248438-e0248438
2021
DOI: 10.1371/journal.pone.0248438
PMCID: PMC7946184
PMID: 33690722
Abstract
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.
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.
Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, 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).
Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.
Details
- Title: Subtitle
- Clinical prediction rule for SARS-CoV-2 infection from 116 U.S. emergency departments 2-22-2021
- Creators
- Jeffrey A Kline - Indiana UniversityCarlos A Camargo Jr - Harvard Medical SchoolD Mark Courtney - The University of Texas Southwestern Medical CenterChristopher Kabrhel - Massachusetts General HospitalKristen E Nordenholz - University of Colorado DenverThomas Aufderheide - Medical College of WisconsinJoshua J Baugh - Massachusetts General HospitalDavid G Beiser - University of ChicagoChristopher L Bennett - Stanford UniversityJoseph Bledsoe - Intermountain HealthcareEdward Castillo - University of California San DiegoMakini Chisolm-Straker - Icahn School of Medicine at Mount SinaiElizabeth M Goldberg - Brown UniversityHans House - University of IowaStacey House - Washington University in St. LouisTimothy Jang - University of California, Los AngelesStephen C Lim - University Medical Center New OrleansTroy E Madsen - University of UtahDanielle M McCarthy - Northwestern UniversityAndrew Meltzer - George Washington UniversityStephen Moore - Penn State Milton S. Hershey Medical CenterCraig Newgard - Oregon Health & Science UniversityJustine Pagenhardt - West Virginia UniversityKatherine L Pettit - Indiana UniversityMichael S Pulia - University of Wisconsin–MadisonMichael A Puskarich - Hennepin County Medical CenterLauren T Southerland - The Ohio State University Wexner Medical CenterScott Sparks - Riverside Regional Medical CenterDanielle Turner-Lawrence - Beaumont HealthMarie Vrablik - University of WashingtonAlfred Wang - Indiana UniversityAnthony J Weekes - Carolinas Medical CenterLauren Westafer - Baystate HealthJohn Wilburn - Wayne State University
- Resource Type
- Journal article
- Publication Details
- PloS one, Vol.16(3), pp.e0248438-e0248438
- DOI
- 10.1371/journal.pone.0248438
- PMID
- 33690722
- PMCID
- PMC7946184
- NLM abbreviation
- PLoS One
- ISSN
- 1932-6203
- eISSN
- 1932-6203
- Grant note
- K76 AG059983 / NIA NIH HHS
- Language
- English
- Date published
- 2021
- Academic Unit
- Emergency Medicine
- Record Identifier
- 9984296986002771
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