Moving from “Surgeries” to Patients: Progress and Pitfalls While Using Machine Learning to Personalize Transfusion Prediction

Abstract

Of the many roles an anesthesiologist fills each day, managing intraoperative transfusion remains one of the most important and complex.The evidence supporting different transfusion thresholds and consensus guidelines continues to evolve. [1][2]2][3] Ensuring availability of crossmatched blood is counterbalanced by the increasing need to avoid unnecessary testing, wasting of scarce blood products, and healthcare costs, particularly during a pandemic.4 In this issue of Anesthesiology, Lou et al. 5 present an important advancement in transfusion risk prediction by incorporating patient-specific factors and offering a publicly available open-source machine learning algorithm for implementation.Using more than 3 million records from the American College of Surgeons National Surgical Quality Improvement Program registry between 2016 and 2018, they developed an algorithm that combined historical procedure-specific transfusion rates and patient-specific demographics, comorbidities, and laboratory values to predict erythrocyte transfusion on the day of surgery.The algorithm was then validated against 2019 registry data for 1 million patients and 2020 local academic medical center data.More importantly, they compared their algorithm's recommendations to the current standard of care, the Maximum Surgical Blood Ordering System, which only uses historical procedure-specific transfusion data. 6The algorithm reduced the number of predicted transfusions by a third, while maintaining 96% sensitivity, the current standard.These predictions can be used to guide preoperative type and screen orders.

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