tetano
Editor, Senior Moderator
Antimicrob Steward Healthc Epidemiol
. 2024 May 17;4(1):e88.
doi: 10.1017/ash.2024.82. eCollection 2024. Prediction model to identify infectious COVID-19 patients in the emergency department
Myat Oo Aung[SUP] 1 [/SUP], Indumathi Venkatachalam[SUP] 1 2 [/SUP], Jean X Y Sim[SUP] 1 2 [/SUP], Liang En Wee[SUP] 2 [/SUP], May K Aung[SUP] 1 [/SUP], Yong Yang[SUP] 1 [/SUP], Edwin P Conceicao[SUP] 1 [/SUP], Shalvi Arora[SUP] 1 [/SUP], Marcus A B Lee[SUP] 3 [/SUP], Chang H Sia[SUP] 3 [/SUP], Kenneth B K Tan[SUP] 3 [/SUP], Moi Lin Ling[SUP] 1 [/SUP]
Affiliations
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.
. 2024 May 17;4(1):e88.
doi: 10.1017/ash.2024.82. eCollection 2024. Prediction model to identify infectious COVID-19 patients in the emergency department
Myat Oo Aung[SUP] 1 [/SUP], Indumathi Venkatachalam[SUP] 1 2 [/SUP], Jean X Y Sim[SUP] 1 2 [/SUP], Liang En Wee[SUP] 2 [/SUP], May K Aung[SUP] 1 [/SUP], Yong Yang[SUP] 1 [/SUP], Edwin P Conceicao[SUP] 1 [/SUP], Shalvi Arora[SUP] 1 [/SUP], Marcus A B Lee[SUP] 3 [/SUP], Chang H Sia[SUP] 3 [/SUP], Kenneth B K Tan[SUP] 3 [/SUP], Moi Lin Ling[SUP] 1 [/SUP]
Affiliations
- PMID: 38774116
- PMCID: PMC11106730
- DOI: 10.1017/ash.2024.82
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.