Red blood cell (RBC) transfusion is a crucial medical intervention that plays a vital role in the care of surgical patients. However, due to the decreasing availability of blood donations and supplies, there is a pressing need for accurately predicting the likelihood of transfusion for surgical patients. While maximum surgical blood order schedules are commonly used in clinical practice, they are not specifically designed to accurately predict RBC utilization on an individual basis.
Factors such as preoperative haemoglobin level, total body blood volume, comedications, and other patient-specific risk factors are not taken into consideration. Artificial intelligence and machine learning-based technologies offer valuable alternatives for predicting transfusion probability, incorporating individual patient risk factors like comorbidities, laboratory parameters, use of oral anticoagulation, ASA score, surgeon’s ID, and implemented blood-saving measures.
The ability to forecast the need for RBC transfusion can greatly contribute to personalized medicine, quality assurance, reduction of blood loss, cost reduction, and enhancement of patient safety. Moreover, transfusion prediction models can support blood management strategies prior to surgery.
Reference link: https://doi.org/10.1016/j.tracli.2022.09.063
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