• Thomas Tschoellitsch, Carl Böck, Tina Tomić Mahečić, Axel Hofmann, and Jens Meier.
• From the Clinic of Anaesthesiology and Critical Care Medicine, Kepler University Hospital GmbH and Johannes Kepler University (TT, CB, JM), Institute of Signal Processing, Johannes Kepler University Linz, Austria (CB), Clinic of Anaesthesiology and Intensive Care Medicine, University Hospital Centre Zagreb - Rebro, Croatia (TTM) and Clinic of Anaesthesiology, University Hospital, Zurich, Switzerland (AH).
• Eur J Anaesthesiol. 2022 Sep 1; 39 (9): 766773766-773.

BackgroundMassive perioperative allogeneic blood transfusion, that is, perioperative transfusion of more than 10 units of packed red blood cells (pRBC), is one of the main contributors to perioperative morbidity and mortality in cardiac surgery. Prediction of perioperative blood transfusion might enable preemptive treatment strategies to reduce risk and improve patient outcomes while reducing resource utilisation. We, therefore, investigated the precision of five different machine learning algorithms to predict the occurrence of massive perioperative allogeneic blood transfusion in cardiac surgery at our centre.ObjectiveIs it possible to predict massive perioperative allogeneic blood transfusion using machine learning?DesignRetrospective, observational study.SettingSingle adult cardiac surgery centre in Austria between 01 January 2010 and 31 December 2019.PatientsPatients undergoing cardiac surgery.Main Outcome MeasuresPrimary outcome measures were the number of patients receiving at least 10 units pRBC, the area under the curve for the receiver operating characteristics curve, the F1 score, and the negative-predictive (NPV) and positive-predictive values (PPV) of the five machine learning algorithms used to predict massive perioperative allogeneic blood transfusion.ResultsA total of 3782 (1124 female:) patients were enrolled and 139 received at least 10 pRBC units. Using all features available at hospital admission, massive perioperative allogeneic blood transfusion could be excluded rather accurately. The best area under the curve was achieved by Random Forests: 0.810 (0.76 to 0.86) with high NPV of 0.99). This was still true using only the eight most important features [area under the curve 0.800 (0.75 to 0.85)].ConclusionMachine learning models may provide clinical decision support as to which patients to focus on for perioperative preventive treatment in order to preemptively reduce massive perioperative allogeneic blood transfusion by predicting, which patients are not at risk.Trial RegistrationJohannes Kepler University Ethics Committee Study Number 1091/2021, Clinicaltrials.gov identifier NCT04856618.Copyright © 2022 European Society of Anaesthesiology and Intensive Care. Unauthorized reproduction of this article is prohibited.

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