Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

<h4>Background</h4>Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often...

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Autores principales:	Naomi George, Edward Moseley, Rene Eber, Jennifer Siu, Mathew Samuel, Jonathan Yam, Kexin Huang, Leo Anthony Celi, Charlotta Lindvall
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/92393c1977564ebd8bf4c41d658874cc
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spelling	oai:doaj.org-article:92393c1977564ebd8bf4c41d658874cc2021-12-02T20:09:52ZDeep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.1932-620310.1371/journal.pone.0253443https://doaj.org/article/92393c1977564ebd8bf4c41d658874cc2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253443https://doaj.org/toc/1932-6203<h4>Background</h4>Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.<h4>Methods</h4>Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.<h4>Results</h4>There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.<h4>Discussion</h4>We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.Naomi GeorgeEdward MoseleyRene EberJennifer SiuMathew SamuelJonathan YamKexin HuangLeo Anthony CeliCharlotta LindvallPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253443 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naomi George Edward Moseley Rene Eber Jennifer Siu Mathew Samuel Jonathan Yam Kexin Huang Leo Anthony Celi Charlotta Lindvall Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
description	<h4>Background</h4>Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approximately 7 to 10 days. Yet half of patients admitted to the intensive care unit receiving tracheostomy will die within a year, often within three months. Existing mortality prediction models for prolonged mechanical ventilation, such as the ProVent Score, have poor sensitivity and are not applied until after 14 days of mechanical ventilation. We developed a model to predict 3-month mortality in patients requiring more than 7 days of mechanical ventilation using deep learning techniques and compared this to existing mortality models.<h4>Methods</h4>Retrospective cohort study. Setting: The Medical Information Mart for Intensive Care III Database. Patients: All adults requiring ≥ 7 days of mechanical ventilation. Measurements: A neural network model for 3-month mortality was created using process-of-care variables, including demographic, physiologic and clinical data. The area under the receiver operator curve (AUROC) was compared to the ProVent model at predicting 3 and 12-month mortality. Shapley values were used to identify the variables with the greatest contributions to the model.<h4>Results</h4>There were 4,334 encounters divided into a development cohort (n = 3467) and a testing cohort (n = 867). The final deep learning model included 250 variables and had an AUROC of 0.74 for predicting 3-month mortality at day 7 of mechanical ventilation versus 0.59 for the ProVent model. Older age and elevated Simplified Acute Physiology Score II (SAPS II) Score on intensive care unit admission had the largest contribution to predicting mortality.<h4>Discussion</h4>We developed a deep learning prediction model for 3-month mortality among patients requiring ≥ 7 days of mechanical ventilation using a neural network approach utilizing readily available clinical variables. The model outperforms the ProVent model for predicting mortality among patients requiring ≥ 7 days of mechanical ventilation. This model requires external validation.
format	article
author	Naomi George Edward Moseley Rene Eber Jennifer Siu Mathew Samuel Jonathan Yam Kexin Huang Leo Anthony Celi Charlotta Lindvall
author_facet	Naomi George Edward Moseley Rene Eber Jennifer Siu Mathew Samuel Jonathan Yam Kexin Huang Leo Anthony Celi Charlotta Lindvall
author_sort	Naomi George
title	Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
title_short	Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
title_full	Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
title_fullStr	Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
title_full_unstemmed	Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
title_sort	deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/92393c1977564ebd8bf4c41d658874cc
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Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

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