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Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19

BACKGROUND: Timely recognition of respiratory failure and the need for mechanical ventilation is crucial in managing patients with coronavirus disease 2019 (COVID-19) and reducing hospital mortality rate. A risk stratification tool could assist to avoid clinical deterioration of patients with COVID-...

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Autores principales: Zhang, Yi, Zhu, Yang-Jie, Zhu, Dao-Jun, Yu, Bo-Yang, Liu, Tong-Tong, Wang, Lu-Yao, Zhang, Lu-Lu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375294/
https://www.ncbi.nlm.nih.gov/pubmed/37522004
http://dx.doi.org/10.3389/fpubh.2023.1227935
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author Zhang, Yi
Zhu, Yang-Jie
Zhu, Dao-Jun
Yu, Bo-Yang
Liu, Tong-Tong
Wang, Lu-Yao
Zhang, Lu-Lu
author_facet Zhang, Yi
Zhu, Yang-Jie
Zhu, Dao-Jun
Yu, Bo-Yang
Liu, Tong-Tong
Wang, Lu-Yao
Zhang, Lu-Lu
author_sort Zhang, Yi
collection PubMed
description BACKGROUND: Timely recognition of respiratory failure and the need for mechanical ventilation is crucial in managing patients with coronavirus disease 2019 (COVID-19) and reducing hospital mortality rate. A risk stratification tool could assist to avoid clinical deterioration of patients with COVID-19 and optimize allocation of scarce resources. Therefore, we aimed to develop a prediction model for early identification of patients with COVID-19 who may require mechanical ventilation. METHODS: We included patients with COVID-19 hospitalized in United States. Demographic and clinical data were extracted from the records of the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Model construction involved the use of the least absolute shrinkage and selection operator and multivariable logistic regression. The model’s performance was evaluated based on discrimination, calibration, and clinical utility. RESULTS: The training set comprised 73,957 patients (5,971 requiring mechanical ventilation), whereas the validation set included 10,428 (887 requiring mechanical ventilation). The prediction model incorporating age, sex, and 11 other comorbidities (deficiency anemias, congestive heart failure, coagulopathy, dementia, diabetes with chronic complications, complicated hypertension, neurological disorders unaffecting movement, obesity, pulmonary circulation disease, severe renal failure, and weight loss) demonstrated moderate discrimination (area under the curve, 0.715; 95% confidence interval, 0.709–0.722), good calibration (Brier score = 0.070, slope = 1, intercept = 0) and a clinical net benefit with a threshold probability ranged from 2 to 34% in the training set. Similar model’s performances were observed in the validation set. CONCLUSION: A robust prognostic model utilizing readily available predictors at hospital admission was developed for the early identification of patients with COVID-19 who may require mechanical ventilation. Application of this model could support clinical decision-making to optimize patient management and resource allocation.
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spelling pubmed-103752942023-07-29 Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19 Zhang, Yi Zhu, Yang-Jie Zhu, Dao-Jun Yu, Bo-Yang Liu, Tong-Tong Wang, Lu-Yao Zhang, Lu-Lu Front Public Health Public Health BACKGROUND: Timely recognition of respiratory failure and the need for mechanical ventilation is crucial in managing patients with coronavirus disease 2019 (COVID-19) and reducing hospital mortality rate. A risk stratification tool could assist to avoid clinical deterioration of patients with COVID-19 and optimize allocation of scarce resources. Therefore, we aimed to develop a prediction model for early identification of patients with COVID-19 who may require mechanical ventilation. METHODS: We included patients with COVID-19 hospitalized in United States. Demographic and clinical data were extracted from the records of the Healthcare Cost and Utilization Project State Inpatient Database in 2020. Model construction involved the use of the least absolute shrinkage and selection operator and multivariable logistic regression. The model’s performance was evaluated based on discrimination, calibration, and clinical utility. RESULTS: The training set comprised 73,957 patients (5,971 requiring mechanical ventilation), whereas the validation set included 10,428 (887 requiring mechanical ventilation). The prediction model incorporating age, sex, and 11 other comorbidities (deficiency anemias, congestive heart failure, coagulopathy, dementia, diabetes with chronic complications, complicated hypertension, neurological disorders unaffecting movement, obesity, pulmonary circulation disease, severe renal failure, and weight loss) demonstrated moderate discrimination (area under the curve, 0.715; 95% confidence interval, 0.709–0.722), good calibration (Brier score = 0.070, slope = 1, intercept = 0) and a clinical net benefit with a threshold probability ranged from 2 to 34% in the training set. Similar model’s performances were observed in the validation set. CONCLUSION: A robust prognostic model utilizing readily available predictors at hospital admission was developed for the early identification of patients with COVID-19 who may require mechanical ventilation. Application of this model could support clinical decision-making to optimize patient management and resource allocation. Frontiers Media S.A. 2023-07-14 /pmc/articles/PMC10375294/ /pubmed/37522004 http://dx.doi.org/10.3389/fpubh.2023.1227935 Text en Copyright © 2023 Zhang, Zhu, Zhu, Yu, Liu, Wang and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Zhang, Yi
Zhu, Yang-Jie
Zhu, Dao-Jun
Yu, Bo-Yang
Liu, Tong-Tong
Wang, Lu-Yao
Zhang, Lu-Lu
Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19
title Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19
title_full Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19
title_fullStr Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19
title_full_unstemmed Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19
title_short Development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with COVID-19
title_sort development and validation of a prediction model for mechanical ventilation based on comorbidities in hospitalized patients with covid-19
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10375294/
https://www.ncbi.nlm.nih.gov/pubmed/37522004
http://dx.doi.org/10.3389/fpubh.2023.1227935
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