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Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning

With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for cr...

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Autores principales: Islam, Khandaker Reajul, Kumar, Jaya, Tan, Toh Leong, Reaz, Mamun Bin Ibne, Rahman, Tawsifur, Khandakar, Amith, Abbas, Tariq, Hossain, Md. Sakib Abrar, Zughaier, Susu M., Chowdhury, Muhammad E. H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498213/
https://www.ncbi.nlm.nih.gov/pubmed/36140545
http://dx.doi.org/10.3390/diagnostics12092144
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author Islam, Khandaker Reajul
Kumar, Jaya
Tan, Toh Leong
Reaz, Mamun Bin Ibne
Rahman, Tawsifur
Khandakar, Amith
Abbas, Tariq
Hossain, Md. Sakib Abrar
Zughaier, Susu M.
Chowdhury, Muhammad E. H.
author_facet Islam, Khandaker Reajul
Kumar, Jaya
Tan, Toh Leong
Reaz, Mamun Bin Ibne
Rahman, Tawsifur
Khandakar, Amith
Abbas, Tariq
Hossain, Md. Sakib Abrar
Zughaier, Susu M.
Chowdhury, Muhammad E. H.
author_sort Islam, Khandaker Reajul
collection PubMed
description With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.
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spelling pubmed-94982132022-09-23 Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning Islam, Khandaker Reajul Kumar, Jaya Tan, Toh Leong Reaz, Mamun Bin Ibne Rahman, Tawsifur Khandakar, Amith Abbas, Tariq Hossain, Md. Sakib Abrar Zughaier, Susu M. Chowdhury, Muhammad E. H. Diagnostics (Basel) Article With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients. MDPI 2022-09-03 /pmc/articles/PMC9498213/ /pubmed/36140545 http://dx.doi.org/10.3390/diagnostics12092144 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Islam, Khandaker Reajul
Kumar, Jaya
Tan, Toh Leong
Reaz, Mamun Bin Ibne
Rahman, Tawsifur
Khandakar, Amith
Abbas, Tariq
Hossain, Md. Sakib Abrar
Zughaier, Susu M.
Chowdhury, Muhammad E. H.
Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
title Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
title_full Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
title_fullStr Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
title_full_unstemmed Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
title_short Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning
title_sort prognostic model of icu admission risk in patients with covid-19 infection using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498213/
https://www.ncbi.nlm.nih.gov/pubmed/36140545
http://dx.doi.org/10.3390/diagnostics12092144
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