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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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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. |
format | Online Article Text |
id | pubmed-7595731 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
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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