Antimicrobial Stewardship & Healthcare Epidemiology (Jan 2024)

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

  • Myat Oo Aung,
  • Indumathi Venkatachalam,
  • Jean X.Y. Sim,
  • Liang En Wee,
  • May K. Aung,
  • Yong Yang,
  • Edwin P. Conceicao,
  • Shalvi Arora,
  • Marcus A.B. Lee,
  • Chang H. Sia,
  • Kenneth B.K. Tan,
  • Moi Lin Ling

DOI
https://doi.org/10.1017/ash.2024.82
Journal volume & issue
Vol. 4

Abstract

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Abstract 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.