Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1784707601417633792 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9103632 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Termedia Publishing House |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lainomariaelena anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT generalielena anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT tommasinitobia anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT angelottigiovanni anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT aghemoalessio anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT desaiantonio anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT morandinipierandrea anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT stefaninigiuliog anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT lleoana anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT vozaantonio anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT savevskivictor anindividualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT lainomariaelena individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT generalielena individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT tommasinitobia individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT angelottigiovanni individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT aghemoalessio individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT desaiantonio individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT morandinipierandrea individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT stefaninigiuliog individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT lleoana individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT vozaantonio individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy AT savevskivictor individualizedalgorithmtopredictmortalityincovid19pneumoniaamachinelearningbasedstudy |