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Title: Preoperative identification of cardiac surgery patients at risk of receiving a platelet transfusion: The Australian Cardiac Surgery Platelet Transfusion (ACSePT) risk prediction tool.
Authors: Flint, Andrew W J
Bailey, Michael
Reid, Christopher M
Smith, Julian A
Tran, Lavinia
Wood, Erica M
McQuilten, Zoe K
Reade, Michael C
Citation: © 2020 AABB.
Transfusion. 2020 Aug 5. doi: 10.1111/trf.15990.
Abstract: Platelet (PLT) transfusions are limited and costly resources. Accurately predicting clinical demand while limiting product wastage remains difficult. A PLT transfusion prediction score was developed for use in cardiac surgery patients who commonly require PLT transfusions. STUDY DESIGN AND METHODS: Using the Australian and New Zealand Society of Cardiac and Thoracic Surgeons National Cardiac Surgery Database, significant predictors for PLT transfusion were identified by multivariate logistic regression. Using a development data set containing 2005 to 2016 data, the Australian Cardiac Surgery Platelet Transfusion (ACSePT) risk prediction tool was developed by assigning weights to each significant predictor that corresponded to a probability of PLT transfusion. The predicted probability for each score was compared to actual PLT transfusion occurrence in a validation (2017) data set. RESULTS: The development data set contained 38 independent variables and 91 521 observations. The validation data set contained 12 529 observations. The optimal model contained 23 variables significant at P < .001 and an area under the receiver operating characteristic (ROC) curve of 0.69 (95% confidence interval [CI], 0.68-0.69). ACSePT contained nine variables and had an area under the ROC curve of 0.66 (95% CI, 0.65-0.66) and overall predicted probability of PLT transfusion of 19.8% for the validation data set compared to an observed risk of 20.3%. CONCLUSION: The ACSePT risk prediction tool is the first scoring system to predict a cardiac surgery patient's risk of receiving a PLT transfusion. It can be used to identify patients at higher risk of receiving PLT transfusions for inclusion in clinical trials and by PLT inventory managers to predict PLT demand.
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Journal title: Transfusion
Publication Date: 2020-08-05
Type: Journal Article
DOI: 10.1111/trf.15990
Orcid: 0000-0001-9183-7882
Appears in Collections:(a) NT Health Research Collection

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