Prediction model to identify infectious COVID-19 patients in the emergency department.

Autor:	Aung MO; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore., Venkatachalam I; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore.; Department of Infectious Disease, Singapore General Hospital, Singapore, Singapore., Sim JXY; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore.; Department of Infectious Disease, Singapore General Hospital, Singapore, Singapore., Wee LE; Department of Infectious Disease, Singapore General Hospital, Singapore, Singapore., Aung MK; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore., Yang Y; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore., Conceicao EP; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore., Arora S; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore., Lee MAB; Emergency Department, Singapore General Hospital, Singapore, Singapore., Sia CH; Emergency Department, Singapore General Hospital, Singapore, Singapore., Tan KBK; Emergency Department, Singapore General Hospital, Singapore, Singapore., Ling ML; Infection Prevention and Epidemiology Department, Singapore General Hospital, Singapore, Singapore.
Jazyk:	angličtina
Zdroj:	Antimicrobial stewardship & healthcare epidemiology : ASHE [Antimicrob Steward Healthc Epidemiol] 2024 May 17; Vol. 4 (1), pp. e88. Date of Electronic Publication: 2024 May 17 (Print Publication: 2024).
DOI:	10.1017/ash.2024.82
Abstrakt:	Background: Real-time reverse-transcriptase polymerase chain reaction (RT-PCR) has been the gold standard for diagnosing coronavirus disease 2019 (COVID-19) but has a lag time for the results. An effective prediction algorithm for infectious COVID-19, utilized at the emergency department (ED), may reduce the risk of healthcare-associated COVID-19. Objective: To develop a prototypic prediction model for infectious COVID-19 at the time of presentation to the ED. Material and Methods: Retrospective cohort study of all adult patients admitted to Singapore General Hospital (SGH) through ED between March 15, 2020, and December 31, 2022, with admission of COVID-19 RT-PCR results. Two prediction models were developed and evaluated using area under the curve (AUC) of receiver operating characteristics (ROC) to identify infectious COVID-19 patients (cycle threshold (Ct) of <25). Results: Total of 78,687 patients were admitted to SGH through ED during study period. 6,132 of them tested severe acute respiratory coronavirus 2 positive on RT-PCR. Nearly 70% (4,226 of 6,132) of the patients had infectious COVID-19 (Ct<25). Model that included demographics, clinical history, symptom and laboratory variables had AUROC of 0.85 with sensitivity and specificity of 80.0% & 72.1% respectively. When antigen rapid test results at ED were available and added to the model for a subset of the study population, AUROC reached 0.97 with sensitivity and specificity of 95.0% and 92.8% respectively. Both models maintained respective sensitivity and specificity results when applied to validation data. Conclusion: Clinical predictive models based on available information at ED can be utilized for identification of infectious COVID-19 patients and may enhance infection prevention efforts. Competing Interests: All authors report no conflicts of interest relevant to this article.None. (© The Author(s) 2024.)
Databáze:	MEDLINE
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