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Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran
BACKGROUND: Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). AIMS AND OBJECTIVES: To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. MATERIA...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Jaypee Brothers Medical Publishers
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237161/ https://www.ncbi.nlm.nih.gov/pubmed/35836646 http://dx.doi.org/10.5005/jp-journals-10071-24226 |
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author | Sabetian, Golnar Azimi, Aram Kazemi, Azar Hoseini, Benyamin Asmarian, Naeimehossadat Khaloo, Vahid Zand, Farid Masjedi, Mansoor Shahriarirad, Reza Shahriarirad, Sepehr |
author_facet | Sabetian, Golnar Azimi, Aram Kazemi, Azar Hoseini, Benyamin Asmarian, Naeimehossadat Khaloo, Vahid Zand, Farid Masjedi, Mansoor Shahriarirad, Reza Shahriarirad, Sepehr |
author_sort | Sabetian, Golnar |
collection | PubMed |
description | BACKGROUND: Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). AIMS AND OBJECTIVES: To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. MATERIALS AND METHODS: In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. RESULTS: A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O(2) saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. CONCLUSION: In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran. HOW TO CITE THIS ARTICLE: Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. ETHICS APPROVAL: This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018). |
format | Online Article Text |
id | pubmed-9237161 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Jaypee Brothers Medical Publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-92371612022-07-13 Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran Sabetian, Golnar Azimi, Aram Kazemi, Azar Hoseini, Benyamin Asmarian, Naeimehossadat Khaloo, Vahid Zand, Farid Masjedi, Mansoor Shahriarirad, Reza Shahriarirad, Sepehr Indian J Crit Care Med Original Article BACKGROUND: Prioritizing the patients requiring intensive care may decrease the fatality of coronavirus disease-2019 (COVID-19). AIMS AND OBJECTIVES: To develop, validate, and compare two models based on machine-learning methods for predicting patients with COVID-19 requiring intensive care. MATERIALS AND METHODS: In 2021, 506 suspected COVID-19 patients, with clinical presentations along with radiographic findings, were laboratory confirmed and included in the study. The primary end-point was patients with COVID-19 requiring intensive care, defined as actual admission to the intensive care unit (ICU). The data were randomly partitioned into training and testing sets (70% and 30%, respectively) without overlapping. A decision-tree algorithm and multivariate logistic regression were performed to develop the models for predicting the cases based on their first 24 hours data. The predictive performance of the models was compared based on the area under the receiver operating characteristic curve (AUC), sensitivity, and accuracy of the models. RESULTS: A 10-fold cross-validation decision-tree model predicted cases requiring intensive care with the AUC, accuracy, and sensitivity of 97%, 98%, and 94.74%, respectively. The same values in the machine-learning logistic regression model were 75%, 85.62%, and 55.26%, respectively. Creatinine, smoking, neutrophil/lymphocyte ratio, temperature, respiratory rate, partial thromboplastin time, white blood cell, Glasgow Coma Scale (GCS), dizziness, international normalized ratio, O(2) saturation, C-reactive protein, diastolic blood pressure (DBP), and dry cough were the most important predictors. CONCLUSION: In an Iranian population, our decision-based machine-learning method offered an advantage over logistic regression for predicting patients requiring intensive care. This method can support clinicians in decision-making, using patients’ early data, particularly in low- and middle-income countries where their resources are as limited as Iran. HOW TO CITE THIS ARTICLE: Sabetian G, Azimi A, Kazemi A, Hoseini B, Asmarian N, Khaloo V, et al. Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study based on Machine-learning Approach from Iran. Indian J Crit Care Med 2022;26(6):688–695. ETHICS APPROVAL: This study was approved by the Ethical Committee of Shiraz University of Medical Sciences (IR.SUMS.REC.1399.018). Jaypee Brothers Medical Publishers 2022-06 /pmc/articles/PMC9237161/ /pubmed/35836646 http://dx.doi.org/10.5005/jp-journals-10071-24226 Text en Copyright © 2022; The Author(s). https://creativecommons.org/licenses/by-nc/4.0/© The Author(s). 2022 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and non-commercial reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Original Article Sabetian, Golnar Azimi, Aram Kazemi, Azar Hoseini, Benyamin Asmarian, Naeimehossadat Khaloo, Vahid Zand, Farid Masjedi, Mansoor Shahriarirad, Reza Shahriarirad, Sepehr Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran |
title | Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran |
title_full | Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran |
title_fullStr | Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran |
title_full_unstemmed | Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran |
title_short | Prediction of Patients with COVID-19 Requiring Intensive Care: A Cross-sectional Study Based on Machine-learning Approach from Iran |
title_sort | prediction of patients with covid-19 requiring intensive care: a cross-sectional study based on machine-learning approach from iran |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9237161/ https://www.ncbi.nlm.nih.gov/pubmed/35836646 http://dx.doi.org/10.5005/jp-journals-10071-24226 |
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