Preoperative identification of cardiac surgery patients at risk of receiving a platelet transfusion: The Australian Cardiac Surgery Platelet Transfusion (ACSePT) risk prediction tool

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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 patientʼs 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.

Original languageEnglish
Pages (from-to)2272-2283
Number of pages12
Issue number10
Publication statusPublished - Oct 2020

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