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

Dennis Shung, Jessie Huang, egbert castro, J. Kenneth Tay, Michael Simonov, Loren Laine, Smita Krishnaswamy

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 frst
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 identifed 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 identifed 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 frst 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 frst 24 h from
admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.