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 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.

Machine learning in transfusion medicine: A scoping review

“…Demand for blood components and associated morbidity and mortality are significant in obstetrics, gastrointestinal bleeding, and hemato/oncology 1,5,66 ; however, ML for prediction of transfusion in these settings is underrepresented comprising a total of 5 of 54 studies, none of which have undergone prospective validation or implementation at the time of writing. The studies exploring gastrointestinal bleeding demonstrate benefits of using large, publicly available data sets, able to externally validate models 67,68 . Given the availability of the data, these tasks could be developed into benchmarks, enabling different research teams to compare the performance of new approaches.…”

“…This problem may be reflected in poor external validation of pretrained models if the sites use different guidance or practice. 68 Although considered beyond the scope of this review, it may be of interest to review studies where the contribution of physicians, for example, surgeons and anesthesiologists, features as a variable of the model, or where variables behind physicians' decision-making are explored in more detail, 44 to address reasons for variation of practice. Such an approach could prompt action to address discrepancies, particularly as the large, multicenter datasets used for ML are also well suited to address physician effects while preserving anonymity.…”

“…The studies exploring gastrointestinal bleeding demonstrate benefits of using large, publicly available data sets, able to externally validate models. 67,68 Given the availability of the data, these tasks could be developed into benchmarks, enabling different research teams to compare the performance of new approaches. Interestingly, Levi et al 67 apply their model to support triage: predicting which patients do not require transfusion (suggesting no ongoing bleeding) and, therefore, may avoid admission to intensive care unit (ICU).…”

Application of Artificial Neural Network in Daily Prediction of Bleeding in ICU Patients Treated With Anti-Thrombotic Therapy

“…The most used type of RNNs is LSTM, which can be useful for clinical measurements because they carefully tune the information passed between subsequent time iterations of the model. Advantages of LSTM over regression models include the ability to generate multiple predictions with the rst data input and the ability to combine features in more complex ways to model changes over time [23]. Another type of recurrent unit, which we refer to as a gated recurrent unit (GRU), was proposed to make each recurrent unit adaptively capture dependencies of different time scales.…”

Predicting the need for blood transfusion in intensive care units with reinforcement learning

“…Vital signs such as heart rate and temperature are taken several times within an hour, while laboratory tests such as arterial pH and creatinine are administered every few hours as needed. Following [30], this wide discrepancy in measurement frequency for time-varying continuous features is consolidated into means at 4-hour intervals. A list of clinically reasonable measurement ranges provided by [8] is used to remove outlier values for each feature.…”