Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis - Université Paris-Est-Créteil-Val-de-Marne Access content directly
Journal Articles PLoS ONE Year : 2022

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

Abstract

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.
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Dates and versions

hal-04142030 , version 1 (26-06-2023)

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Camille Jung, Jean-Baptiste Excoffier, Mathilde Raphaël-Rousseau, Noémie Salaün-Penquer, Matthieu Ortala, et al.. Evolution of hospitalized patient characteristics through the first three COVID-19 waves in Paris area using machine learning analysis. PLoS ONE, 2022, 17 (2), pp.e0263266. ⟨10.1371/journal.pone.0263266⟩. ⟨hal-04142030⟩

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