Please use this identifier to cite or link to this item: https://covid-19.conacyt.mx/jspui/handle/1000/747
Epidemiological and Clinical Predictors of COVID-19
Sun, Y
Koh, V
Marimuthu, K
Ng, O
Young, B
Vasoo, S
Chan, M
Lee, V
De, P
Barkham, T
Lin, R
Cook, A
Leo, Y
Acceso Abierto
Atribución-NoComercial-SinDerivadas
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 to 98 years) presenting at the designated national outbreak screening centre and tertiary care hospital in Singapore for SARS-CoV-2 testing from January 26 to February 16, 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 characteristics curves, adjusting for over-confidence using leave-out-one 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-out-one cross validation, all the models incorporating clinical tests (Models 1, 2 and 3) performed well with areas under the receiver operating characteristics curve (AUC) 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.
Clinical Infectious Diseases
2020
Preimpreso
https://coronavirus.1science.com/item/7a34fb7d1eead59a73ffdd5441ac94d4803d3e34
Inglés
VIRUS RESPIRATORIOS
Appears in Collections:Artículos científicos

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