Cargando…

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients

OBJECTIVE(S): To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). DESIGN: A machine learning study within a national ICU COVID-19 registry in Australia. PARTICIPANTS: Adult patients who...

Descripción completa

Detalles Bibliográficos
Autores principales: Karri, Roshan, Chen, Yi-Ping Phoebe, Burrell, Aidan J. C., Penny-Dimri, Jahan C., Broadley, Tessa, Trapani, Tony, Deane, Adam M., Udy, Andrew A., Plummer, Mark P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604987/
https://www.ncbi.nlm.nih.gov/pubmed/36288359
http://dx.doi.org/10.1371/journal.pone.0276509
_version_ 1784817953473036288
author Karri, Roshan
Chen, Yi-Ping Phoebe
Burrell, Aidan J. C.
Penny-Dimri, Jahan C.
Broadley, Tessa
Trapani, Tony
Deane, Adam M.
Udy, Andrew A.
Plummer, Mark P.
author_facet Karri, Roshan
Chen, Yi-Ping Phoebe
Burrell, Aidan J. C.
Penny-Dimri, Jahan C.
Broadley, Tessa
Trapani, Tony
Deane, Adam M.
Udy, Andrew A.
Plummer, Mark P.
author_sort Karri, Roshan
collection PubMed
description OBJECTIVE(S): To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). DESIGN: A machine learning study within a national ICU COVID-19 registry in Australia. PARTICIPANTS: Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded. MAIN OUTCOME MEASURES: Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models. RESULTS: 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50–69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively. CONCLUSION: Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.
format Online
Article
Text
id pubmed-9604987
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-96049872022-10-27 Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients Karri, Roshan Chen, Yi-Ping Phoebe Burrell, Aidan J. C. Penny-Dimri, Jahan C. Broadley, Tessa Trapani, Tony Deane, Adam M. Udy, Andrew A. Plummer, Mark P. PLoS One Research Article OBJECTIVE(S): To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). DESIGN: A machine learning study within a national ICU COVID-19 registry in Australia. PARTICIPANTS: Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded. MAIN OUTCOME MEASURES: Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models. RESULTS: 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50–69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively. CONCLUSION: Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs. Public Library of Science 2022-10-26 /pmc/articles/PMC9604987/ /pubmed/36288359 http://dx.doi.org/10.1371/journal.pone.0276509 Text en © 2022 Karri 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
Karri, Roshan
Chen, Yi-Ping Phoebe
Burrell, Aidan J. C.
Penny-Dimri, Jahan C.
Broadley, Tessa
Trapani, Tony
Deane, Adam M.
Udy, Andrew A.
Plummer, Mark P.
Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
title Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
title_full Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
title_fullStr Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
title_full_unstemmed Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
title_short Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients
title_sort machine learning predicts the short-term requirement for invasive ventilation among australian critically ill covid-19 patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9604987/
https://www.ncbi.nlm.nih.gov/pubmed/36288359
http://dx.doi.org/10.1371/journal.pone.0276509
work_keys_str_mv AT karriroshan machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT chenyipingphoebe machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT burrellaidanjc machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT pennydimrijahanc machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT broadleytessa machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT trapanitony machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT deaneadamm machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT udyandrewa machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT plummermarkp machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients
AT machinelearningpredictstheshorttermrequirementforinvasiveventilationamongaustraliancriticallyillcovid19patients