Details

Autor(en) / Beteiligte

• Sun, Yinxiaohe
• Koh, Vanessa
• Marimuthu, Kalisvar
• Ng, Oon Tek
• Young, Barnaby
• Vasoo, Shawn
• Chan, Monica
• Lee, Vernon J M
• De, Partha P
• Barkham, Timothy
• Lin, Raymond T P
• Cook, Alex R
• Leo, Yee Sin

Titel

Epidemiological and Clinical Predictors of COVID-19

Ist Teil von

• Clinical infectious diseases, 2020-07, Vol.71 (15), p.786-792

Ort / Verlag

US: Oxford University Press

Erscheinungsjahr

2020

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• 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. A risk score incorporating easily ascertainable demographic, clinical evaluation, and clinical testing covariates to identify patients at high risk of COVID-19 can help prioritize subjects for testing and public health measures to prevent onward transmission, especially in resource-limited settings.