Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding
Name
e100245.full.pdf
Description
Published version
Size
786.58 KB
Format
Adobe PDF
Checksum (MD5)
21adcd1765aaafc71ecff0157ca234ab
Author(s)
Levi, Riccardo
Carli, Francesco
Arévalo, Aldo Robles
Altinel, Yuksel
Stein, Daniel J
Naldini, Matteo Maria
Grassi, Federica
Zanoni, Andrea
Finkelstein, Stan Neil
Vieira, Susana M
Date Issued
January 2021
Journal
BMJ Health and Care Informatics
Publisher
BMJ Pubilshing Group
Citation
Levi, Riccardo et al. “Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.” BMJ Health and Care Informatics 28, 1 (January 2021): e100245 © 2021 The Author(s)
Version
Final published version
Abstract
Objective Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU. Methods A machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates. Results The optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all. Conclusions The potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.
MIT Department
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
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DOI of Published Version
https://doi.org/10.1136/bmjhci-2020-100245
