Personalized Surgical Transfusion Risk Prediction: Comment

Abstract

The authors include 4 million surgical cases during a 3-yr period from the American College of Surgeons National Surgical Quality Improvement Program database.The authors used the American College of Surgeons National Surgical Quality Improvement Program database to develop a machine learning model that incorporates patient-and surgery-specific variables to predict transfusion risk and the associated need for preoperative type and screen.The authors hypothesize that their machine learning algorithm would outperform the traditional approach of relying primarily on historical surgery-specific transfusion rates and thus optimize resource allocation by decreasing blood bank waste.The machine learning algorithm recommends fewer preoperative type and screen orders.The study presents in exceptional detail the methodologic approach to developing highly accurate algorithms to predict transfusion risk.Several authors have shown that race is an independent predictor of postoperative transfusion across surgical disciplines, associated with 2.

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