Epidemiological and Clinical Predictors of COVID-19.
Adolescent
Adult
Aged
Aged, 80 and over
Betacoronavirus
/ genetics
COVID-19
COVID-19 Testing
Case-Control Studies
Child
Clinical Laboratory Techniques
Coronavirus Infections
/ diagnosis
Diagnostic Tests, Routine
/ methods
Female
Humans
Male
Mass Screening
/ methods
Middle Aged
Pandemics
Pneumonia, Viral
/ diagnosis
Polymerase Chain Reaction
/ methods
Retrospective Studies
SARS-CoV-2
Singapore
/ epidemiology
Sputum
/ virology
Young Adult
COVID-19
SARS-CoV-2
prediction model
risk factors
Journal
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
ISSN: 1537-6591
Titre abrégé: Clin Infect Dis
Pays: United States
ID NLM: 9203213
Informations de publication
Date de publication:
28 07 2020
28 07 2020
Historique:
received:
16
03
2020
accepted:
21
03
2020
pubmed:
27
3
2020
medline:
11
8
2020
entrez:
27
3
2020
Statut:
ppublish
Résumé
Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid-based reverse transcription polymerase chain reaction (PCR) testing. This retrospective case-control study involves subjects (7-98 years) presenting at the designated national outbreak screening center and tertiary care hospital in Singapore for SARS-CoV-2 testing from 26 January to 16 February 2020. COVID-19 status was confirmed by PCR testing of sputum, nasopharyngeal swabs, or throat swabs. Demographic, clinical, laboratory, and exposure-risk variables ascertainable at presentation were analyzed to develop an algorithm for estimating the risk of COVID-19. Model development used Akaike's information criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction scores. Performance was measured using receiver operating characteristic curves, adjusting for overconfidence using leave-one-out cross-validation. The study population included 788 subjects, of whom 54 (6.9%) were SARS-CoV-2 positive and 734 (93.1%) were SARS-CoV-2 negative. The median age was 34 years, and 407 (51.7%) were female. Using leave-one-out cross-validation, all the models incorporating clinical tests (models 1, 2, and 3) performed well with areas under the receiver operating characteristic curve (AUCs) of 0.91, 0.88, and 0.88, respectively. In comparison, model 4 had an AUC of 0.65. Rapidly ascertainable clinical and laboratory data could identify individuals at high risk of COVID-19 and enable prioritization of PCR testing and containment efforts. Basic laboratory test results were crucial to prediction models.
Sections du résumé
BACKGROUND
Rapid identification of COVID-19 cases, which is crucial to outbreak containment efforts, is challenging due to the lack of pathognomonic symptoms and in settings with limited capacity for specialized nucleic acid-based reverse transcription polymerase chain reaction (PCR) testing.
METHODS
This retrospective case-control study involves subjects (7-98 years) presenting at the designated national outbreak screening center and tertiary care hospital in Singapore for SARS-CoV-2 testing from 26 January to 16 February 2020. COVID-19 status was confirmed by PCR testing of sputum, nasopharyngeal swabs, or throat swabs. Demographic, clinical, laboratory, and exposure-risk variables ascertainable at presentation were analyzed to develop an algorithm for estimating the risk of COVID-19. Model development used Akaike's information criterion in a stepwise fashion to build logistic regression models, which were then translated into prediction scores. Performance was measured using receiver operating characteristic curves, adjusting for overconfidence using leave-one-out cross-validation.
RESULTS
The study population included 788 subjects, of whom 54 (6.9%) were SARS-CoV-2 positive and 734 (93.1%) were SARS-CoV-2 negative. The median age was 34 years, and 407 (51.7%) were female. Using leave-one-out cross-validation, all the models incorporating clinical tests (models 1, 2, and 3) performed well with areas under the receiver operating characteristic curve (AUCs) of 0.91, 0.88, and 0.88, respectively. In comparison, model 4 had an AUC of 0.65.
CONCLUSIONS
Rapidly ascertainable clinical and laboratory data could identify individuals at high risk of COVID-19 and enable prioritization of PCR testing and containment efforts. Basic laboratory test results were crucial to prediction models.
Identifiants
pubmed: 32211755
pii: 5811426
doi: 10.1093/cid/ciaa322
pmc: PMC7542554
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
786-792Investigateurs
Poh Lian Lim
(P)
Brenda Ang
(B)
Cheng Chuan Lee
(C)
David Chien Boon Lye
(DCB)
Li Min Ling
(LM)
Lawrence Soon-U Lee
(LS)
Sapna Sadarangani
(S)
Chen Seong Wong
(C)
Tau Hong Lee
(TH)
Ray Junhao Lin
(R)
Po Ying Chia
(PY)
Mucheli Sharavan Sadasiv
(MS)
Deborah Hee Ling Ng
(DHL)
Chiaw Yee Choy
(CY)
Tsin Wen Yeo
(TW)
Glorijoy Shi En Tan
(GSE)
Yu Kit Chan
(YK)
Jun Yang Tay
(JY)
Pei Hua Lee
(PH)
Sean Wei Xiang Ong
(SWX)
Stephanie Sutjipto
(S)
Ian Liang En Wee
(ILE)
Dimatatac Frederico
(D)
Chi Jong Go
(CJ)
Florante Santo Isais
(FS)
Commentaires et corrections
Type : CommentIn
Informations de copyright
© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.
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