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Early prediction of level-of-care requirements in patients with COVID-19

This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for...

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Autores principales: Hao, Boran, Sotudian, Shahabeddin, Wang, Taiyao, Xu, Tingting, Hu, Yang, Gaitanidis, Apostolos, Breen, Kerry, Velmahos, George C, Paschalidis, Ioannis Ch
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595731/
https://www.ncbi.nlm.nih.gov/pubmed/33044170
http://dx.doi.org/10.7554/eLife.60519
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author Hao, Boran
Sotudian, Shahabeddin
Wang, Taiyao
Xu, Tingting
Hu, Yang
Gaitanidis, Apostolos
Breen, Kerry
Velmahos, George C
Paschalidis, Ioannis Ch
author_facet Hao, Boran
Sotudian, Shahabeddin
Wang, Taiyao
Xu, Tingting
Hu, Yang
Gaitanidis, Apostolos
Breen, Kerry
Velmahos, George C
Paschalidis, Ioannis Ch
author_sort Hao, Boran
collection PubMed
description This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease.
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spelling pubmed-75957312020-11-02 Early prediction of level-of-care requirements in patients with COVID-19 Hao, Boran Sotudian, Shahabeddin Wang, Taiyao Xu, Tingting Hu, Yang Gaitanidis, Apostolos Breen, Kerry Velmahos, George C Paschalidis, Ioannis Ch eLife Medicine This study examined records of 2566 consecutive COVID-19 patients at five Massachusetts hospitals and sought to predict level-of-care requirements based on clinical and laboratory data. Several classification methods were applied and compared against standard pneumonia severity scores. The need for hospitalization, ICU care, and mechanical ventilation were predicted with a validation accuracy of 88%, 87%, and 86%, respectively. Pneumonia severity scores achieve respective accuracies of 73% and 74% for ICU care and ventilation. When predictions are limited to patients with more complex disease, the accuracy of the ICU and ventilation prediction models achieved accuracy of 83% and 82%, respectively. Vital signs, age, BMI, dyspnea, and comorbidities were the most important predictors of hospitalization. Opacities on chest imaging, age, admission vital signs and symptoms, male gender, admission laboratory results, and diabetes were the most important risk factors for ICU admission and mechanical ventilation. The factors identified collectively form a signature of the novel COVID-19 disease. eLife Sciences Publications, Ltd 2020-10-12 /pmc/articles/PMC7595731/ /pubmed/33044170 http://dx.doi.org/10.7554/eLife.60519 Text en © 2020, Hao et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Medicine
Hao, Boran
Sotudian, Shahabeddin
Wang, Taiyao
Xu, Tingting
Hu, Yang
Gaitanidis, Apostolos
Breen, Kerry
Velmahos, George C
Paschalidis, Ioannis Ch
Early prediction of level-of-care requirements in patients with COVID-19
title Early prediction of level-of-care requirements in patients with COVID-19
title_full Early prediction of level-of-care requirements in patients with COVID-19
title_fullStr Early prediction of level-of-care requirements in patients with COVID-19
title_full_unstemmed Early prediction of level-of-care requirements in patients with COVID-19
title_short Early prediction of level-of-care requirements in patients with COVID-19
title_sort early prediction of level-of-care requirements in patients with covid-19
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7595731/
https://www.ncbi.nlm.nih.gov/pubmed/33044170
http://dx.doi.org/10.7554/eLife.60519
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