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
{"title":"识别急诊科感染性 COVID-19 患者的预测模型。","authors":"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":"10.1017/ash.2024.82","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>To develop a prototypic prediction model for infectious COVID-19 at the time of presentation to the ED.</p><p><strong>Material and methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":72246,"journal":{"name":"Antimicrobial stewardship & healthcare epidemiology : ASHE","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11106730/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prediction model to identify infectious COVID-19 patients in the emergency department.\",\"authors\":\"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\":\"10.1017/ash.2024.82\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Objective: </strong>To develop a prototypic prediction model for infectious COVID-19 at the time of presentation to the ED.</p><p><strong>Material and methods: </strong>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).</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":72246,\"journal\":{\"name\":\"Antimicrobial stewardship & healthcare epidemiology : ASHE\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11106730/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Antimicrobial stewardship & healthcare epidemiology : ASHE\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/ash.2024.82\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Antimicrobial stewardship & healthcare epidemiology : ASHE","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/ash.2024.82","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Prediction model to identify infectious COVID-19 patients in the emergency department.
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.