How do I/we forecast tomorrow's transfusion? A focus on recipients' profiles

Abstract

Red blood cell (RBC) transfusion is a life-saving medical intervention and has an essential role in the management of surgical patients. However, blood donations and supply levels are decreasing, therefore there is an unmet need for the accurate prediction of the transfusion probability for surgical patients. Multiple methods have been established to predict the need for RBC transfusion. Maximum surgical blood order schedules are widely used in the clinical setting. However, these lists are not designed to accurately predict RBC utilization for an individual case as factors such as preoperative haemoglobin level, total body blood volume, comedications are not considered. Artificial intelligence and related technologies based on machine learning modelling are valuable alternatives to predict transfusion probability taking into account patient individual risk factors including among others comorbidities, laboratory parameters, use of oral anticoagulation, ASA score, surgeon's ID or applied blood saving measures. Overall, forecasting the need for a RBC transfusion can facilitate personalized medicine, quality assurance, decrease blood wastage, decrease costs, and increase patient safety. Furthermore, transfusion prediction models could facilitate blood management strategies before surgery.

Keywords: Artificial intelligence; Blood transfusion; Economics; Hospital transfusion services; Patient Blood Management; Patient safety.