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. 2020 Jul 28;71(15):786-792.
doi: 10.1093/cid/ciaa322.

Epidemiological and Clinical Predictors of COVID-19

Yinxiaohe Sun  1 , Vanessa Koh  2   3 , Kalisvar Marimuthu  2   3   4 , Oon Tek Ng  2   3   5 , Barnaby Young  2   3   5 , Shawn Vasoo  2   3 , Monica Chan  2   3 , Vernon J M Lee  1   6 , Partha P De  7 , Timothy Barkham  4   7 , Raymond T P Lin  4   8 , Alex R Cook  1 , Yee Sin Leo  1   2   3   4   5 ; National Centre for Infectious Diseases COVID-19 Outbreak Research Team
Collaborators, Affiliations

Epidemiological and Clinical Predictors of COVID-19

Yinxiaohe Sun et al. Clin Infect Dis. .

Abstract

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.

Keywords: COVID-19; SARS-CoV-2; prediction model; risk factors.

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Figures

Figure 1.
Figure 1.
Study subject disposition. Abbreviations: NCID, National Centre for Infectious Diseases, Singapore; PCR, polymerase chain reaction; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
Figure 2.
Figure 2.
Performance of models 1, 2, 3, and 4 measured using receiver operating characteristic curves, adjusting for overconfidence using leave-one-out cross-validation. Abbreviation: AUC, area under the receiver operating characteristic curve.
See this image and copyright information in PMC

Comment in

  • Good IgA Bad IgG in SARS-CoV-2 Infection?
    Béné MC, de Carvalho Bittencourt M, Eveillard M, Le Bris Y. Béné MC, et al. Clin Infect Dis. 2020 Jul 28;71(15):897-898. doi: 10.1093/cid/ciaa426. Clin Infect Dis. 2020. PMID: 32280952 Free PMC article. No abstract available.

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