A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients
Objective: To predict the in-hospital incidence of acute respiratory distress syndrome (ARDS) in COVID-19 patients by developing a predictive nomogram. Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between 30 January 2020, and 22 February 2020 were enrolled in this study....
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ftdoajarticles:oai:doaj.org/article:783428dc1134416f9ad5fd1ee2efcc26 2023-05-15T15:09:16+02:00 A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients Ning Ding Yang Zhou Guifang Yang Xiangping Chai 2021-01-01T00:00:00Z https://doi.org/10.4103/1995-7645.318303 https://doaj.org/article/783428dc1134416f9ad5fd1ee2efcc26 EN eng Wolters Kluwer Medknow Publications http://www.apjtm.org/article.asp?issn=1995-7645;year=2021;volume=14;issue=6;spage=274;epage=280;aulast=Ding https://doaj.org/toc/2352-4146 2352-4146 doi:10.4103/1995-7645.318303 https://doaj.org/article/783428dc1134416f9ad5fd1ee2efcc26 Asian Pacific Journal of Tropical Medicine, Vol 14, Iss 6, Pp 274-280 (2021) nomogram acute respiratory distress syndrome covid-19 Arctic medicine. Tropical medicine RC955-962 article 2021 ftdoajarticles https://doi.org/10.4103/1995-7645.318303 2022-12-30T19:54:40Z Objective: To predict the in-hospital incidence of acute respiratory distress syndrome (ARDS) in COVID-19 patients by developing a predictive nomogram. Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between 30 January 2020, and 22 February 2020 were enrolled in this study. Clinical characteristics and laboratory variables were analyzed and compared between patients with or without ARDS. Clinical characteristics and laboratory variables that were risk factors of ARDS were screened by the least absolute shrinkage and selection operator binary logistic regression. Based on risk factors, a prediction model was established by logistic regression and the final nomogram prognostic model was performed. The calibration curve was applied to evaluate the consistency between the nomogram and the ideal observation. Results: A total of 113 patients, including 99 non-ARDS patients and 14 ARDS patients were included in this study. Eight variables including hypertension, chronic obstructive pulmonary disease, cough, lactate dehydrogenase, creatine kinase, white blood count, body temperature, and heart rate were included in the model. The area under receiver operating characteristic curve, specificity, sensitivity, and accuracy of the full model were 0.969, 1.000, 0.857, and 0.875, respectively. The calibration curve also showed good agreement between the predicted and observed values in the model. Conclusions: The nomogram can be used to predict the in-hospital incidence of ARDS in COVID-19 patients. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Asian Pacific Journal of Tropical Medicine 14 6 274 |
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Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
nomogram acute respiratory distress syndrome covid-19 Arctic medicine. Tropical medicine RC955-962 |
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nomogram acute respiratory distress syndrome covid-19 Arctic medicine. Tropical medicine RC955-962 Ning Ding Yang Zhou Guifang Yang Xiangping Chai A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients |
topic_facet |
nomogram acute respiratory distress syndrome covid-19 Arctic medicine. Tropical medicine RC955-962 |
description |
Objective: To predict the in-hospital incidence of acute respiratory distress syndrome (ARDS) in COVID-19 patients by developing a predictive nomogram. Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between 30 January 2020, and 22 February 2020 were enrolled in this study. Clinical characteristics and laboratory variables were analyzed and compared between patients with or without ARDS. Clinical characteristics and laboratory variables that were risk factors of ARDS were screened by the least absolute shrinkage and selection operator binary logistic regression. Based on risk factors, a prediction model was established by logistic regression and the final nomogram prognostic model was performed. The calibration curve was applied to evaluate the consistency between the nomogram and the ideal observation. Results: A total of 113 patients, including 99 non-ARDS patients and 14 ARDS patients were included in this study. Eight variables including hypertension, chronic obstructive pulmonary disease, cough, lactate dehydrogenase, creatine kinase, white blood count, body temperature, and heart rate were included in the model. The area under receiver operating characteristic curve, specificity, sensitivity, and accuracy of the full model were 0.969, 1.000, 0.857, and 0.875, respectively. The calibration curve also showed good agreement between the predicted and observed values in the model. Conclusions: The nomogram can be used to predict the in-hospital incidence of ARDS in COVID-19 patients. |
format |
Article in Journal/Newspaper |
author |
Ning Ding Yang Zhou Guifang Yang Xiangping Chai |
author_facet |
Ning Ding Yang Zhou Guifang Yang Xiangping Chai |
author_sort |
Ning Ding |
title |
A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients |
title_short |
A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients |
title_full |
A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients |
title_fullStr |
A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients |
title_full_unstemmed |
A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients |
title_sort |
nomogram for predicting acute respiratory distress syndrome in covid-19 patients |
publisher |
Wolters Kluwer Medknow Publications |
publishDate |
2021 |
url |
https://doi.org/10.4103/1995-7645.318303 https://doaj.org/article/783428dc1134416f9ad5fd1ee2efcc26 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Asian Pacific Journal of Tropical Medicine, Vol 14, Iss 6, Pp 274-280 (2021) |
op_relation |
http://www.apjtm.org/article.asp?issn=1995-7645;year=2021;volume=14;issue=6;spage=274;epage=280;aulast=Ding https://doaj.org/toc/2352-4146 2352-4146 doi:10.4103/1995-7645.318303 https://doaj.org/article/783428dc1134416f9ad5fd1ee2efcc26 |
op_doi |
https://doi.org/10.4103/1995-7645.318303 |
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Asian Pacific Journal of Tropical Medicine |
container_volume |
14 |
container_issue |
6 |
container_start_page |
274 |
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