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522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients

BACKGROUND: The clinical spectrum of the novel corona virus disease 2019 (COVID-19) ranges from mild to severe disease and death. We aim to construct a simple and novel scoring model that will predict mortality events in hospitalized COVID-19 patients. METHODS: We established a retrospective cohort...

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Autores principales: Acosta, Tommy J Parraga, Vahia, Amit T, Chaudhry, Zohra S, Gudipati, Smitha, Arshad, Samia, Ramesh, Mayur, Zervos, Marcus, Alangaden, George J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777640/
http://dx.doi.org/10.1093/ofid/ofaa439.716
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author Acosta, Tommy J Parraga
Vahia, Amit T
Chaudhry, Zohra S
Gudipati, Smitha
Arshad, Samia
Ramesh, Mayur
Zervos, Marcus
Alangaden, George J
author_facet Acosta, Tommy J Parraga
Vahia, Amit T
Chaudhry, Zohra S
Gudipati, Smitha
Arshad, Samia
Ramesh, Mayur
Zervos, Marcus
Alangaden, George J
author_sort Acosta, Tommy J Parraga
collection PubMed
description BACKGROUND: The clinical spectrum of the novel corona virus disease 2019 (COVID-19) ranges from mild to severe disease and death. We aim to construct a simple and novel scoring model that will predict mortality events in hospitalized COVID-19 patients. METHODS: We established a retrospective cohort of 2541 patients admitted with COVID-19 from February 19, 2020 to April 28, 2020 to Henry Ford Health System, MI. Sociodemographic data, comorbidities, and clinical data were collected. Our novel SAS score was constructed using 3 easily available parameters, namely Sex, Age, and Oxygen Saturation at presentation (Table 1 and 2). Primary endpoint was mortality. Multivariate analysis with logistic regression was done and the model was assessed using receiver operating characteristic (ROC) with area under ROC (AUROC) to determine the optimal cutoff for sensitivity, specificity, and positive and negative predictive values. [Image: see text] [Image: see text] RESULTS: The mean age of survivors was 61 compared to 75 years for non-survivors (standard deviation 16 vs 13.8, p< 0.0001), and 1298 (51.1%) were men. Multivariate analysis of the SAS score adjusted for modified SOFA [Sequential organ failure assessment] score (mSOFA) showed that age (odds ratio [OR] 2.4, 95% confidence interval {CI} 2.04–2.72, p< 0.0001) and oxygen saturation (OR 1.6, 95% CI 1.27–1.98) were the most significant predictors of mortality in the model. The SAS score had an AUROC of 0.78 (95% CI 0.77–0.81) (Figure 1). A cutoff score of 3 offered the most sensitivity for predicting mortality while maintaining a negative predictive value of 95% (Table 3). Comparison of AUROC shows that SAS score adjusted to mSOFA has better diagnostic information compared to either SAS score or mSOFA alone (Figure 2). [Image: see text] [Image: see text] [Image: see text] CONCLUSION: The easy to use SAS score at time of presentation identified hospitalized COVID-19 patients at high risk for mortality. Application of the SAS score in the emergency department may help triage patients to inpatient versus outpatient care. DISCLOSURES: Marcus Zervos, MD, Melinta Therapeutics (Grant/Research Support)
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spelling pubmed-77776402021-01-07 522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients Acosta, Tommy J Parraga Vahia, Amit T Chaudhry, Zohra S Gudipati, Smitha Arshad, Samia Ramesh, Mayur Zervos, Marcus Alangaden, George J Open Forum Infect Dis Poster Abstracts BACKGROUND: The clinical spectrum of the novel corona virus disease 2019 (COVID-19) ranges from mild to severe disease and death. We aim to construct a simple and novel scoring model that will predict mortality events in hospitalized COVID-19 patients. METHODS: We established a retrospective cohort of 2541 patients admitted with COVID-19 from February 19, 2020 to April 28, 2020 to Henry Ford Health System, MI. Sociodemographic data, comorbidities, and clinical data were collected. Our novel SAS score was constructed using 3 easily available parameters, namely Sex, Age, and Oxygen Saturation at presentation (Table 1 and 2). Primary endpoint was mortality. Multivariate analysis with logistic regression was done and the model was assessed using receiver operating characteristic (ROC) with area under ROC (AUROC) to determine the optimal cutoff for sensitivity, specificity, and positive and negative predictive values. [Image: see text] [Image: see text] RESULTS: The mean age of survivors was 61 compared to 75 years for non-survivors (standard deviation 16 vs 13.8, p< 0.0001), and 1298 (51.1%) were men. Multivariate analysis of the SAS score adjusted for modified SOFA [Sequential organ failure assessment] score (mSOFA) showed that age (odds ratio [OR] 2.4, 95% confidence interval {CI} 2.04–2.72, p< 0.0001) and oxygen saturation (OR 1.6, 95% CI 1.27–1.98) were the most significant predictors of mortality in the model. The SAS score had an AUROC of 0.78 (95% CI 0.77–0.81) (Figure 1). A cutoff score of 3 offered the most sensitivity for predicting mortality while maintaining a negative predictive value of 95% (Table 3). Comparison of AUROC shows that SAS score adjusted to mSOFA has better diagnostic information compared to either SAS score or mSOFA alone (Figure 2). [Image: see text] [Image: see text] [Image: see text] CONCLUSION: The easy to use SAS score at time of presentation identified hospitalized COVID-19 patients at high risk for mortality. Application of the SAS score in the emergency department may help triage patients to inpatient versus outpatient care. DISCLOSURES: Marcus Zervos, MD, Melinta Therapeutics (Grant/Research Support) Oxford University Press 2020-12-31 /pmc/articles/PMC7777640/ http://dx.doi.org/10.1093/ofid/ofaa439.716 Text en © The Author 2020. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Poster Abstracts
Acosta, Tommy J Parraga
Vahia, Amit T
Chaudhry, Zohra S
Gudipati, Smitha
Arshad, Samia
Ramesh, Mayur
Zervos, Marcus
Alangaden, George J
522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients
title 522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients
title_full 522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients
title_fullStr 522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients
title_full_unstemmed 522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients
title_short 522. The Simple and Novel SAS Score to Predict Mortality at Presentation in 2541 Hospitalized COVID-19 Patients
title_sort 522. the simple and novel sas score to predict mortality at presentation in 2541 hospitalized covid-19 patients
topic Poster Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7777640/
http://dx.doi.org/10.1093/ofid/ofaa439.716
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