Robust Meta-Model for Predicting the Need for Blood Transfusion in
  Non-traumatic ICU Patients
Alireza Rafiei Ronald Moore Tilendra Choudhary Curtis Marshall Geoffrey Smith John D. Roback Ravi M. Patel Cassandra D. Josephson Rishikesan Kamaleswaran
Objective: Blood transfusions, crucial in managing anemia and coagulopathy in
ICU settings, require accurate prediction for effective resource allocation and
patient risk assessment. However, existing clinical decision support systems
have primarily targeted a particular patient demographic with unique medical
conditions and focused on a single type of blood transfusion. This study aims
to develop an advanced machine learning-based model to predict the probability
of transfusion necessity over the next 24 hours for a diverse range of
non-traumatic ICU patients.
  Methods: We conducted a retrospective cohort study on 72,072 adult
non-traumatic ICU patients admitted to a high-volume US metropolitan academic
hospital between 2016 and 2020. We developed a meta-learner and various machine
learning models to serve as predictors, training them annually with four-year
data and evaluating on the fifth, unseen year, iteratively over five years.
  Results: The experimental results revealed that the meta-model surpasses the
other models in different development scenarios. It achieved notable
performance metrics, including an Area Under the Receiver Operating
Characteristic (AUROC) curve of 0.97, an accuracy rate of 0.93, and an F1-score
of 0.89 in the best scenario.
  Conclusion: This study pioneers the use of machine learning models for
predicting blood transfusion needs in a diverse cohort of critically ill
patients. The findings of this evaluation confirm that our model not only
predicts transfusion requirements effectively but also identifies key
biomarkers for making transfusion decisions.