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

• Published in: Scientific reports Vol. 11; no. 1; p. 8827
• Main Authors: Shung, Dennis, Huang, Jessie, Castro, Egbert, Tay, J. Kenneth, Simonov, Michael, Laine, Loren, Batra, Ramesh, Krishnaswamy, Smita
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23-04-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308/409; 692/4020/1503/197; 692/499; Adult; Aged; Aged, 80 and over; Bleeding; Blood; Blood transfusion; Cohort Studies; Erythrocyte Transfusion; Erythrocytes; Female; Gastrointestinal Hemorrhage - therapy; Humanities and Social Sciences; Humans; Intensive care; Intensive Care Units; Laboratory tests; Long short-term memory; Male; Middle Aged; multidisciplinary; Neural networks; Neural Networks, Computer; Patient Admission; Patients; Regression analysis; Reproducibility of Results; Risk Assessment; Risk groups; Science; Science (multidisciplinary); Transfusion
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-88226-3

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.