Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit

Abstract Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusio...

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Autores principales:	Dennis Shung, Jessie Huang, Egbert Castro, J. Kenneth Tay, Michael Simonov, Loren Laine, Ramesh Batra, Smita Krishnaswamy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/70ecc46b9e1640a0ba5d4390d8202c6e
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spelling	oai:doaj.org-article:70ecc46b9e1640a0ba5d4390d8202c6e2021-12-02T15:27:06ZNeural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit10.1038/s41598-021-88226-32045-2322https://doaj.org/article/70ecc46b9e1640a0ba5d4390d8202c6e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88226-3https://doaj.org/toc/2045-2322Abstract Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database and separated into training (N = 2,032) and internal validation (N = 492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N = 1,526). 62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to a regression-based models on time-updated data. The LSTM model performed better than discrete time regression-based models for both internal validation (AUROC 0.81 vs 0.75 vs 0.75; P < 0.001) and external validation (AUROC 0.65 vs 0.56 vs 0.56; P < 0.001). A LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.Dennis ShungJessie HuangEgbert CastroJ. Kenneth TayMichael SimonovLoren LaineRamesh BatraSmita KrishnaswamyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dennis Shung Jessie Huang Egbert Castro J. Kenneth Tay Michael Simonov Loren Laine Ramesh Batra Smita Krishnaswamy Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
description	Abstract Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database and separated into training (N = 2,032) and internal validation (N = 492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N = 1,526). 62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to a regression-based models on time-updated data. The LSTM model performed better than discrete time regression-based models for both internal validation (AUROC 0.81 vs 0.75 vs 0.75; P < 0.001) and external validation (AUROC 0.65 vs 0.56 vs 0.56; P < 0.001). A LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.
format	article
author	Dennis Shung Jessie Huang Egbert Castro J. Kenneth Tay Michael Simonov Loren Laine Ramesh Batra Smita Krishnaswamy
author_facet	Dennis Shung Jessie Huang Egbert Castro J. Kenneth Tay Michael Simonov Loren Laine Ramesh Batra Smita Krishnaswamy
author_sort	Dennis Shung
title	Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
title_short	Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
title_full	Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
title_fullStr	Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
title_full_unstemmed	Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
title_sort	neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/70ecc46b9e1640a0ba5d4390d8202c6e
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Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit

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