Machine Learning-Based Prediction of Major Perioperative Allogeneic Blood Requirements in Cardiac Surgery

Cardiac surgery is one of the clinical surgical specialties that carries a particularly high risk for patients to suffer from severe bleeding perioperatively and consequent anemia, which subsequently requires transfusion of allogeneic blood. Although a surprisingly high number of patients in cardiac surgery do not require perioperative transfusions, it is primarily those patients who do require transfusion who are subsequently at risk for a worse outcome. In recent years many studies have been published discussing measures that can assist physicians in avoiding the triad of anemia, bleeding, and transfusion in cardiac surgery. Within these publications, the implementation of Patient Blood Management (PBM) is advised. PBM is a set of measures aimed at improving patient outcome by reducing perioperative bleeding and thus preventing both anemia and bleeding. The three pillars of this bundle are the preoperative preparation of anemic patients with iron, erythropoietin, folic acid and vitamin B12, the prevention of intraoperative blood loss and the reasonable indication for allogeneic transfusions. Nevertheless, it must be mentioned that the implementation of at least part of these measures is laborious, and full implementation of the recommended bundle is therefore rarely achieved. As a consequence, the full potential of Patient Blood Management is not always realized. Unfortunately this means that transfusion of allogeneic blood cannot be prevented in many patients. A small proportion of patients undergoing cardiac surgery requires a very large amount of allogeneic blood perioperatively. These patients are typically those with a particularly poor outcome. Massive transfusion of allogeneic blood in this situation is an indicator of complications and a cause of increased mortality. Although cardiac surgeons and anesthesiologists believe they can assess which patients are at high risk for hemorrhage, recent publications indicate that there is an urgent need for adeq