Multi-center validation of personalized surgical transfusion risk prediction

Presentation Time: 11:15 AM - 11:30 AM

Abstract Keywords: Machine Learning, Surgery, Clinical Decision Support, Healthcare Economics/Cost of Care, Precision Medicine, Patient Safety
Primary Track: Applications
Programmatic Theme: Clinical Informatics

Accurate estimation of surgical transfusion risk guides perioperative planning and effective resource allocation. Few validated methods incorporating patient factors are available to estimate such risk. Here we externally validate a personalized surgical transfusion risk prediction model versus the standard of care using a national sample of 45 hospitals. We find that the model has generalizable performance and can consistently reduce the number of presurgical blood orders needed by one-third compared to the standard of care.

Speaker(s):
Sunny Lou, MD, PhD
Washington University, St. Louis

Author(s):
Sayantan Kumar, PhD Student in Computer Science - Institute for Informatics at Washington University in St. Louis, School of Medicine; Charles Goss, PhD - Washington University School of Medicine; Michael Avidan, MBBCh - Washington University School of Medicine; Sachin Kheterpal, MD, MBA - University of Michigan; Thomas Kannampallil, PhD - Washington University School of Medicine;