Cargando…

Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis

Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintained database from a University Hospital in Paris ar...

Descripción completa

Detalles Bibliográficos
Autores principales: Jung, Camille, Excoffier, Jean-Baptiste, Raphaël-Rousseau, Mathilde, Salaün-Penquer, Noémie, Ortala, Matthieu, Chouaid, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863256/
https://www.ncbi.nlm.nih.gov/pubmed/35192649
http://dx.doi.org/10.1371/journal.pone.0263266
_version_ 1784655199865929728
author Jung, Camille
Excoffier, Jean-Baptiste
Raphaël-Rousseau, Mathilde
Salaün-Penquer, Noémie
Ortala, Matthieu
Chouaid, Christos
author_facet Jung, Camille
Excoffier, Jean-Baptiste
Raphaël-Rousseau, Mathilde
Salaün-Penquer, Noémie
Ortala, Matthieu
Chouaid, Christos
author_sort Jung, Camille
collection PubMed
description Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintained database from a University Hospital in Paris area, over a year corresponding to the first three waves of COVID-19 in France. Evolution of patient characteristics between non-severe and severe cases through the waves was analyzed with a classical multivariate logistic regression along with a complementary Machine-Learning-based analysis using explainability methods. On 1076 hospitalized patients, severe forms concerned 29% (123/429), 31% (66/214) and 18% (79/433) of each wave. Risk factors of the first wave included old age (≥ 70 years), male gender, diabetes and obesity while cardiovascular issues appeared to be a protective factor. Influence of age, gender and comorbidities on the occurrence of severe COVID-19 was less marked in the 3rd wave compared to the first 2, and the interactions between age and comorbidities less important. Typology of hospitalized patients with severe forms evolved rapidly through the waves. This evolution may be due to the changes of hospital practices and the early vaccination campaign targeting the people at high risk such as elderly and patients with comorbidities.
format Online
Article
Text
id pubmed-8863256
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-88632562022-02-23 Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis Jung, Camille Excoffier, Jean-Baptiste Raphaël-Rousseau, Mathilde Salaün-Penquer, Noémie Ortala, Matthieu Chouaid, Christos PLoS One Research Article Characteristics of patients at risk of developing severe forms of COVID-19 disease have been widely described, but very few studies describe their evolution through the following waves. Data was collected retrospectively from a prospectively maintained database from a University Hospital in Paris area, over a year corresponding to the first three waves of COVID-19 in France. Evolution of patient characteristics between non-severe and severe cases through the waves was analyzed with a classical multivariate logistic regression along with a complementary Machine-Learning-based analysis using explainability methods. On 1076 hospitalized patients, severe forms concerned 29% (123/429), 31% (66/214) and 18% (79/433) of each wave. Risk factors of the first wave included old age (≥ 70 years), male gender, diabetes and obesity while cardiovascular issues appeared to be a protective factor. Influence of age, gender and comorbidities on the occurrence of severe COVID-19 was less marked in the 3rd wave compared to the first 2, and the interactions between age and comorbidities less important. Typology of hospitalized patients with severe forms evolved rapidly through the waves. This evolution may be due to the changes of hospital practices and the early vaccination campaign targeting the people at high risk such as elderly and patients with comorbidities. Public Library of Science 2022-02-22 /pmc/articles/PMC8863256/ /pubmed/35192649 http://dx.doi.org/10.1371/journal.pone.0263266 Text en © 2022 Jung et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jung, Camille
Excoffier, Jean-Baptiste
Raphaël-Rousseau, Mathilde
Salaün-Penquer, Noémie
Ortala, Matthieu
Chouaid, Christos
Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis
title Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis
title_full Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis
title_fullStr Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis
title_full_unstemmed Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis
title_short Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis
title_sort evolution of hospitalized patient characteristics through the first three covid-19 waves in paris area using machine learning analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863256/
https://www.ncbi.nlm.nih.gov/pubmed/35192649
http://dx.doi.org/10.1371/journal.pone.0263266
work_keys_str_mv AT jungcamille evolutionofhospitalizedpatientcharacteristicsthroughthefirstthreecovid19wavesinparisareausingmachinelearninganalysis
AT excoffierjeanbaptiste evolutionofhospitalizedpatientcharacteristicsthroughthefirstthreecovid19wavesinparisareausingmachinelearninganalysis
AT raphaelrousseaumathilde evolutionofhospitalizedpatientcharacteristicsthroughthefirstthreecovid19wavesinparisareausingmachinelearninganalysis
AT salaunpenquernoemie evolutionofhospitalizedpatientcharacteristicsthroughthefirstthreecovid19wavesinparisareausingmachinelearninganalysis
AT ortalamatthieu evolutionofhospitalizedpatientcharacteristicsthroughthefirstthreecovid19wavesinparisareausingmachinelearninganalysis
AT chouaidchristos evolutionofhospitalizedpatientcharacteristicsthroughthefirstthreecovid19wavesinparisareausingmachinelearninganalysis