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Machine learning prediction for COVID-19 disease severity at hospital admission
IMPORTANCE: Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. OBJECTIVE: To develop machine learning models to...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9990559/ https://www.ncbi.nlm.nih.gov/pubmed/36882829 http://dx.doi.org/10.1186/s12911-023-02132-4 |
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author | Raman, Ganesh Ashraf, Bilal Demir, Yusuf Kemal Kershaw, Corey D. Cheruku, Sreekanth Atis, Murat Atis, Ahsen Atar, Mustafa Chen, Weina Ibrahim, Ibrahim Bat, Taha Mete, Mutlu |
author_facet | Raman, Ganesh Ashraf, Bilal Demir, Yusuf Kemal Kershaw, Corey D. Cheruku, Sreekanth Atis, Murat Atis, Ahsen Atar, Mustafa Chen, Weina Ibrahim, Ibrahim Bat, Taha Mete, Mutlu |
author_sort | Raman, Ganesh |
collection | PubMed |
description | IMPORTANCE: Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. OBJECTIVE: To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data. DESIGN, SETTING, AND PARTICIPANTS: We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest’s feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods. MAIN OUTCOMES AND MEASURES: Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation. RESULTS: This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html. CONCLUSIONS AND RELEVANCE: In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19. |
format | Online Article Text |
id | pubmed-9990559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99905592023-03-08 Machine learning prediction for COVID-19 disease severity at hospital admission Raman, Ganesh Ashraf, Bilal Demir, Yusuf Kemal Kershaw, Corey D. Cheruku, Sreekanth Atis, Murat Atis, Ahsen Atar, Mustafa Chen, Weina Ibrahim, Ibrahim Bat, Taha Mete, Mutlu BMC Med Inform Decis Mak Research IMPORTANCE: Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. OBJECTIVE: To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data. DESIGN, SETTING, AND PARTICIPANTS: We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest’s feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods. MAIN OUTCOMES AND MEASURES: Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation. RESULTS: This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html. CONCLUSIONS AND RELEVANCE: In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19. BioMed Central 2023-03-07 /pmc/articles/PMC9990559/ /pubmed/36882829 http://dx.doi.org/10.1186/s12911-023-02132-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . 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 in a credit line to the data. |
spellingShingle | Research Raman, Ganesh Ashraf, Bilal Demir, Yusuf Kemal Kershaw, Corey D. Cheruku, Sreekanth Atis, Murat Atis, Ahsen Atar, Mustafa Chen, Weina Ibrahim, Ibrahim Bat, Taha Mete, Mutlu Machine learning prediction for COVID-19 disease severity at hospital admission |
title | Machine learning prediction for COVID-19 disease severity at hospital admission |
title_full | Machine learning prediction for COVID-19 disease severity at hospital admission |
title_fullStr | Machine learning prediction for COVID-19 disease severity at hospital admission |
title_full_unstemmed | Machine learning prediction for COVID-19 disease severity at hospital admission |
title_short | Machine learning prediction for COVID-19 disease severity at hospital admission |
title_sort | machine learning prediction for covid-19 disease severity at hospital admission |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9990559/ https://www.ncbi.nlm.nih.gov/pubmed/36882829 http://dx.doi.org/10.1186/s12911-023-02132-4 |
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