Abstract

Background - Predicting red cell transfusion may assist in identifying those most likely to benefit from patient blood management strategies. Our objective was to identify a simple statistical model to predict transfusion in elective surgery from routinely available data.
Materials and methods - Our final multicentre cohort consisted of 42,546 patients and contained the following potential predictors of red cell transfusion known prior to admission: patient age, sex, pre-admission haemoglobin, surgical procedure, and comorbidities. Missing data were handled by multiple imputation methods. The outcome measure of interest was administration of a red cell transfusion. We used multivariable logistic regression models to predict transfusion, and evaluated the performance by applying a 10-fold cross-validation. Model accuracy was assessed by comparing the area under the receiver operating characteristics curve. After applying an optimal probability cut-off we measured model accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
Results - 7.0% (n=2,993) of the study population received a red cell transfusion. Our most simple model predicted red cell transfusion based on admission haemoglobin and surgical procedure with a multiply imputed estimated area under the curve of 0.862 (0.856, 0.864). The estimated accuracy, sensitivity, specificity, positive predictive, and negative predictive values at the probability cut-off of 0.4 were 0.934, 0.257, 0.986, 0.573, and 0.946 respectively.
Discussion - A small number of variables available prior to admission can predict red cell transfusion with very good accuracy. Our model can be used to flag high-risk patients most likely to benefit from pre-operative patient blood management measures.

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Authors

Kevin M. Trentino - School of Population and Global Health, The University of Western Australia, Perth, Australia; Data and Digital Innovation, East Metropolitan Health Service, Perth, Australia

Frank M. Sanfilippo - School of Population and Global Health, The University of Western Australia, Perth, Australia

Michael F. Leahy - Department of Haematology, PathWest Laboratory Medicine, Royal Perth Hospital, Perth, Australia; School of Medicine and Pharmacology, The University of Western Australia, Perth, Australia

Shannon L. Farmer - Department of Haematology, Royal Perth Hospital, Perth, Australia; Discipline of Surgery, Medical School, The University of Western Australia, Perth, Australia

Hamish Mace - Department of Anaesthesia, Pain and Perioperative Medicine, Fiona Stanley Hospital, Murdoch, Australia; Division of Emergency Medicine, The University of Western Australia, Perth, Australia

Adam Lloyd - Data and Digital Innovation, East Metropolitan Health Service, Perth, Australia

Kevin Murray - School of Population and Global Health, The University of Western Australia, Perth, Australia

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