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Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19

BACKGROUND: The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, while the capacity of intensive care units (ICUs) is a limiting factor during the peak of the pandemic and generally dependent on a country’s clinical resources. PURPOSE: To de...

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Autores principales: Schalekamp, S., Huisman, M., van Dijk, R. A., Boomsma, M.F., Freire Jorge, P.J., de Boer, W.S, Herder, G.J.M., Bonarius, M., Groot, O.A., Jong, E., Schreuder, A., Schaefer-Prokop, C.M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Radiological Society of North America 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427120/
https://www.ncbi.nlm.nih.gov/pubmed/32787701
http://dx.doi.org/10.1148/radiol.2020202723
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author Schalekamp, S.
Huisman, M.
van Dijk, R. A.
Boomsma, M.F.
Freire Jorge, P.J.
de Boer, W.S
Herder, G.J.M.
Bonarius, M.
Groot, O.A.
Jong, E.
Schreuder, A.
Schaefer-Prokop, C.M.
author_facet Schalekamp, S.
Huisman, M.
van Dijk, R. A.
Boomsma, M.F.
Freire Jorge, P.J.
de Boer, W.S
Herder, G.J.M.
Bonarius, M.
Groot, O.A.
Jong, E.
Schreuder, A.
Schaefer-Prokop, C.M.
author_sort Schalekamp, S.
collection PubMed
description BACKGROUND: The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, while the capacity of intensive care units (ICUs) is a limiting factor during the peak of the pandemic and generally dependent on a country’s clinical resources. PURPOSE: To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. MATERIAL AND METHODS: In this retrospective study including patients from 7th March 2020 to 24(th) April 2020, a consecutive cohort of hospitalized patients with RT-PCR-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (i.e. death and/or ICU admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, CXR and laboratory findings. Distribution and severity of lung involvement was visually assessed using an 8-point scale (chest radiography score). Internal validation was performed using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed, and a risk calculator was derived. RESULTS: The cohort included 356 hospitalized patients (69 ±12 years, 237 male) of whom 168 (47%) developed critical illness. The final risk model’s variables included gender, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease and chest radiography score at hospital presentation. The AUC of the model was 0.77 (95% CI: 0.72-0.81, P < .001). A risk calculator was derived for individual risk assessment; Dutch COVID-19 risk model (see Appendix E2). At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness of which 59 (83%) would be true-positives. CONCLUSION: A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of ICU beds/facilities.
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spelling pubmed-74271202020-08-17 Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19 Schalekamp, S. Huisman, M. van Dijk, R. A. Boomsma, M.F. Freire Jorge, P.J. de Boer, W.S Herder, G.J.M. Bonarius, M. Groot, O.A. Jong, E. Schreuder, A. Schaefer-Prokop, C.M. Radiology Original Research BACKGROUND: The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, while the capacity of intensive care units (ICUs) is a limiting factor during the peak of the pandemic and generally dependent on a country’s clinical resources. PURPOSE: To determine the value of chest radiographic findings together with patient history and laboratory markers at admission to predict critical illness in hospitalized patients with COVID-19. MATERIAL AND METHODS: In this retrospective study including patients from 7th March 2020 to 24(th) April 2020, a consecutive cohort of hospitalized patients with RT-PCR-confirmed COVID-19 from two large Dutch community hospitals was identified. After univariable analysis, a risk model to predict critical illness (i.e. death and/or ICU admission with invasive ventilation) was developed, using multivariable logistic regression including clinical, CXR and laboratory findings. Distribution and severity of lung involvement was visually assessed using an 8-point scale (chest radiography score). Internal validation was performed using bootstrapping. Performance is presented as an area under the receiver operating characteristic curve (AUC). Decision curve analysis was performed, and a risk calculator was derived. RESULTS: The cohort included 356 hospitalized patients (69 ±12 years, 237 male) of whom 168 (47%) developed critical illness. The final risk model’s variables included gender, chronic obstructive lung disease, symptom duration, neutrophil count, C-reactive protein level, lactate dehydrogenase level, distribution of lung disease and chest radiography score at hospital presentation. The AUC of the model was 0.77 (95% CI: 0.72-0.81, P < .001). A risk calculator was derived for individual risk assessment; Dutch COVID-19 risk model (see Appendix E2). At an example threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness of which 59 (83%) would be true-positives. CONCLUSION: A risk model based on chest radiographic and laboratory findings obtained at admission was predictive of critical illness in hospitalized patients with coronavirus disease 2019. This risk calculator might be useful for triage of patients to the limited number of ICU beds/facilities. Radiological Society of North America 2020-08-13 /pmc/articles/PMC7427120/ /pubmed/32787701 http://dx.doi.org/10.1148/radiol.2020202723 Text en 2020 by the Radiological Society of North America, Inc. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
spellingShingle Original Research
Schalekamp, S.
Huisman, M.
van Dijk, R. A.
Boomsma, M.F.
Freire Jorge, P.J.
de Boer, W.S
Herder, G.J.M.
Bonarius, M.
Groot, O.A.
Jong, E.
Schreuder, A.
Schaefer-Prokop, C.M.
Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
title Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
title_full Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
title_fullStr Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
title_full_unstemmed Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
title_short Model-based Prediction of Critical Illness in Hospitalized Patients with COVID-19
title_sort model-based prediction of critical illness in hospitalized patients with covid-19
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427120/
https://www.ncbi.nlm.nih.gov/pubmed/32787701
http://dx.doi.org/10.1148/radiol.2020202723
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