A risk prediction score to identify patients at low risk for COVID-19 infection.

Autor:	Chew WM; Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore., Loh CH; Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore., Jalali A; Department of General Medicine, Changi General Hospital, Singapore., En Fong GS; Department of General Medicine, Changi General Hospital, Singapore., Kumar LS; Department of General Medicine, Changi General Hospital, Singapore., Zhen Sim RH; Department of General Medicine, Changi General Hospital, Singapore., Tan RP; Department of General Medicine, Changi General Hospital, Singapore., Gill SR; Department of General Medicine, Changi General Hospital, Singapore., Liang TR; Department of General Medicine, Changi General Hospital, Singapore., Kwang Koh JM; Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore., Tay TR; Department of Respiratory and Critical Care Medicine, Changi General Hospital, Singapore.
Jazyk:	angličtina
Zdroj:	Singapore medical journal [Singapore Med J] 2022 Aug; Vol. 63 (8), pp. 426-432. Date of Electronic Publication: 2021 Mar 12.
DOI:	10.11622/smedj.2021019
Abstrakt:	Introduction: Singapore's enhanced surveillance programme for COVID-19 identifies and isolates hospitalised patients with acute respiratory symptoms to prevent nosocomial spread. We developed risk prediction models to identify patients with low risk for COVID-19 from this cohort of hospitalised patients with acute respiratory symptoms. Methods: This was a single-centre retrospective observational study. Patients admitted to our institution's respiratory surveillance wards from 10 February to 30 April 2020 contributed data for analysis. Prediction models for COVID-19 were derived from a training cohort using variables based on demographics, clinical symptoms, exposure risks and blood investigations fitted into logistic regression models. The derived prediction models were subsequently validated on a test cohort. Results: Of the 1,228 patients analysed, 52 (4.2%) were diagnosed with COVID-19. Two prediction models were derived, the first based on age, presence of sore throat, dormitory residence, blood haemoglobin level (Hb), and total white blood cell counts (TW), and the second based on presence of headache, contact with infective patients, Hb and TW. Both models had good diagnostic performance with areas under the receiver operating characteristic curve of 0.934 and 0.866, respectively. Risk score cut-offs of 0.6 for Model 1 and 0.2 for Model 2 had 100% sensitivity, allowing identification of patients with low risk for COVID-19. Limiting COVID-19 screening to only elevated-risk patients reduced the number of isolation days for surveillance patients by up to 41.7% and COVID-19 swab testing by up to 41.0%. Conclusion: Prediction models derived from our study were able to identify patients at low risk for COVID-19 and rationalise resource utilisation. Competing Interests: None
Databáze:	MEDLINE
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