Cargando…

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission...

Descripción completa

Detalles Bibliográficos
Autores principales: Cavallaro, Massimo, Moiz, Haseeb, Keeling, Matt J., McCarthy, Noel D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259985/
https://www.ncbi.nlm.nih.gov/pubmed/34161326
http://dx.doi.org/10.1371/journal.pcbi.1009121
_version_ 1783718748707880960
author Cavallaro, Massimo
Moiz, Haseeb
Keeling, Matt J.
McCarthy, Noel D.
author_facet Cavallaro, Massimo
Moiz, Haseeb
Keeling, Matt J.
McCarthy, Noel D.
author_sort Cavallaro, Massimo
collection PubMed
description Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases.
format Online
Article
Text
id pubmed-8259985
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-82599852021-07-19 Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values Cavallaro, Massimo Moiz, Haseeb Keeling, Matt J. McCarthy, Noel D. PLoS Comput Biol Research Article Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that leverage large datasets and complex machine-learning models, provide data-driven intelligence, and guide decisions such as intensive-care unit admission (ICUA). The objective of this study is two-fold, one substantive and one methodological: substantively to evaluate the association of demographic and health records with two related, yet different, outcomes of severe COVID-19 (viz., death and ICUA); methodologically to compare interpretations based on logistic regression and on gradient-boosted decision tree (GBDT) predictions interpreted by means of the Shapley impacts of covariates. Very different association of some factors, e.g., obesity and chronic respiratory diseases, with death and ICUA may guide review of practice. Shapley explanation of GBDTs identified varying effects of some factors among patients, thus emphasising the importance of individual patient assessment. The results of this study are also relevant for the evaluation of complex automated clinical decision systems, which should optimise prediction scores whilst remaining interpretable to clinicians and mitigating potential biases. Public Library of Science 2021-06-23 /pmc/articles/PMC8259985/ /pubmed/34161326 http://dx.doi.org/10.1371/journal.pcbi.1009121 Text en © 2021 Cavallaro 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
Cavallaro, Massimo
Moiz, Haseeb
Keeling, Matt J.
McCarthy, Noel D.
Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values
title Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values
title_full Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values
title_fullStr Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values
title_full_unstemmed Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values
title_short Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values
title_sort contrasting factors associated with covid-19-related icu admission and death outcomes in hospitalised patients by means of shapley values
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259985/
https://www.ncbi.nlm.nih.gov/pubmed/34161326
http://dx.doi.org/10.1371/journal.pcbi.1009121
work_keys_str_mv AT cavallaromassimo contrastingfactorsassociatedwithcovid19relatedicuadmissionanddeathoutcomesinhospitalisedpatientsbymeansofshapleyvalues
AT moizhaseeb contrastingfactorsassociatedwithcovid19relatedicuadmissionanddeathoutcomesinhospitalisedpatientsbymeansofshapleyvalues
AT keelingmattj contrastingfactorsassociatedwithcovid19relatedicuadmissionanddeathoutcomesinhospitalisedpatientsbymeansofshapleyvalues
AT mccarthynoeld contrastingfactorsassociatedwithcovid19relatedicuadmissionanddeathoutcomesinhospitalisedpatientsbymeansofshapleyvalues