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An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study

INTRODUCTION: Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 ba...

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Autores principales: Laino, Maria Elena, Generali, Elena, Tommasini, Tobia, Angelotti, Giovanni, Aghemo, Alessio, Desai, Antonio, Morandini, Pierandrea, Stefanini, Giulio G., Lleo, Ana, Voza, Antonio, Savevski, Victor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Termedia Publishing House 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103632/
https://www.ncbi.nlm.nih.gov/pubmed/35591841
http://dx.doi.org/10.5114/aoms/144980
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author Laino, Maria Elena
Generali, Elena
Tommasini, Tobia
Angelotti, Giovanni
Aghemo, Alessio
Desai, Antonio
Morandini, Pierandrea
Stefanini, Giulio G.
Lleo, Ana
Voza, Antonio
Savevski, Victor
author_facet Laino, Maria Elena
Generali, Elena
Tommasini, Tobia
Angelotti, Giovanni
Aghemo, Alessio
Desai, Antonio
Morandini, Pierandrea
Stefanini, Giulio G.
Lleo, Ana
Voza, Antonio
Savevski, Victor
author_sort Laino, Maria Elena
collection PubMed
description INTRODUCTION: Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning. MATERIAL AND METHODS: We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation. RESULTS: 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality. CONCLUSIONS: Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients.
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spelling pubmed-91036322022-05-18 An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study Laino, Maria Elena Generali, Elena Tommasini, Tobia Angelotti, Giovanni Aghemo, Alessio Desai, Antonio Morandini, Pierandrea Stefanini, Giulio G. Lleo, Ana Voza, Antonio Savevski, Victor Arch Med Sci COVID-19/SARS-CoV-2 INTRODUCTION: Identifying SARS-CoV-2 patients at higher risk of mortality is crucial in the management of a pandemic. Artificial intelligence techniques allow one to analyze large amounts of data to find hidden patterns. We aimed to develop and validate a mortality score at admission for COVID-19 based on high-level machine learning. MATERIAL AND METHODS: We conducted a retrospective cohort study on hospitalized adult COVID-19 patients between March and December 2020. The primary outcome was in-hospital mortality. A machine learning approach based on vital parameters, laboratory values and demographic features was applied to develop different models. Then, a feature importance analysis was performed to reduce the number of variables included in the model, to develop a risk score with good overall performance, that was finally evaluated in terms of discrimination and calibration capabilities. All results underwent cross-validation. RESULTS: 1,135 consecutive patients (median age 70 years, 64% male) were enrolled, 48 patients were excluded, and the cohort was randomly divided into training (760) and test (327) groups. During hospitalization, 251 (22%) patients died. After feature selection, the best performing classifier was random forest (AUC 0.88 ±0.03). Based on the relative importance of each variable, a pragmatic score was developed, showing good performances (AUC 0.85 ±0.025), and three levels were defined that correlated well with in-hospital mortality. CONCLUSIONS: Machine learning techniques were applied in order to develop an accurate in-hospital mortality risk score for COVID-19 based on ten variables. The application of the proposed score has utility in clinical settings to guide the management and prognostication of COVID-19 patients. Termedia Publishing House 2022-01-14 /pmc/articles/PMC9103632/ /pubmed/35591841 http://dx.doi.org/10.5114/aoms/144980 Text en Copyright: © 2022 Termedia & Banach https://creativecommons.org/licenses/by-nc-sa/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
spellingShingle COVID-19/SARS-CoV-2
Laino, Maria Elena
Generali, Elena
Tommasini, Tobia
Angelotti, Giovanni
Aghemo, Alessio
Desai, Antonio
Morandini, Pierandrea
Stefanini, Giulio G.
Lleo, Ana
Voza, Antonio
Savevski, Victor
An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study
title An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study
title_full An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study
title_fullStr An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study
title_full_unstemmed An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study
title_short An individualized algorithm to predict mortality in COVID-19 pneumonia: a machine learning based study
title_sort individualized algorithm to predict mortality in covid-19 pneumonia: a machine learning based study
topic COVID-19/SARS-CoV-2
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103632/
https://www.ncbi.nlm.nih.gov/pubmed/35591841
http://dx.doi.org/10.5114/aoms/144980
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