title: Data Driven Methods for Predicting Blood Transfusion Needs in Elective Surgery.
abstract: Research in blood transfusions mainly focuses on Donor Blood Management, including donation, screening, storage and transport. However, the last years saw an increasing interest in recipient related optimizations, i.e. Patient Blood Management (PBM). Although PBM already aims at reducing transfusion rates by pre- and intra-surgical optimization, there is still a high potential of improvement on an individual level. The present paper investigates the feasibility of predicting blood transfusions needs based on datasets from various treatment phases, using data which have been collected in two previous studies. Results indicate that prediction of blood transfusions can be further improved by predictive modelling including individual pre-surgical parameters. This also allows to identify the main predictors influencing transfusion practice. If confirmed in a prospective dataset, these or similar predictive methods could be a valuable tool to support PBM with the ultimate goal to reduce costs and improve patient outcomes.
authors.affiliation: AIT Austrian Institute of Technology GmbH, Austria.
authors.forename: Dieter
authors.initials: D
authors.lastname: Hayn
authors.affiliation: AIT Austrian Institute of Technology GmbH, Austria.
authors.forename: Karl
authors.initials: K
authors.lastname: Kreiner
authors.affiliation: AIT Austrian Institute of Technology GmbH, Austria.
authors.forename: Peter
authors.initials: P
authors.lastname: Kastner
authors.affiliation: AIT Austrian Institute of Technology GmbH, Austria.
authors.forename: Nada
authors.initials: N
authors.lastname: Breznik
authors.affiliation: Department of Anesthesiology, University Hospital Zurich, Switzerland.
authors.forename: Axel
authors.initials: A
authors.lastname: Hofmann
authors.affiliation: Department of Anesthesiology and Intensive Care, General Hospital Linz, Austria.
authors.forename: Hans
authors.initials: H
authors.lastname: Gombotz
authors.affiliation: AIT Austrian Institute of Technology GmbH, Austria.
authors.forename: Günter
authors.initials: G
authors.lastname: Schreier
mesh_terms: D016063:Blood Loss, Surgical / Q000706:statistics & numerical data; D001803:Blood Transfusion / Q000706:statistics & numerical data*; D017558:Elective Surgical Procedures / Q000379:methods / Q000706:statistics & numerical data*; D005260:Female; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D015233:Models, Statistical