Identification of Preoperative Clinical Factors Associated With Perioperative Blood Transfusions: An Artificial Neural Network Approach

Identification of Preoperative Clinical Factors Associated With Perioperative Blood Transfusions: An Artificial Neural Network Approach

Steven Walczak, Vic Velanovich
Copyright: © 2021 |Pages: 14
DOI: 10.4018/IJHSTM.2021010103
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Abstract

Predicting patients' surgical transfusion needs preoperatively enables more efficient blood resource management. Identifying the significance of variables to use for transfusion predictions may be accomplished more reliably using machine learning, specifically artificial neural networks (ANN). A logistic regression model and two ANN programs are used to identify the contribution of nine variables selected following a literature review. The first ANN uses a sum of the weights method to identify variable contribution and the second ANN uses a leave one out strategy to identify variable contribution. All models indicated that hematocrit was the most significant variable for predicting perioperative blood transfusions. The weighted averages method indicated wRVU's and ASA score were the next most significant contributors. The leave one out method identified sex and INR as contributing to transfusion prediction. The importance of the variables other than hematocrit varied between techniques and may be dependent on the modeling method used.
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Methods

A literature review is performed to identify variables commonly reported related to transfusions and intraoperative blood loss. Next a logistic regression model and two artificial neural network models are developed to determine variable impact on prediction sensitivity, when a predefined specificity level of 75% is used. Results from the three models will be used to evaluate each variable’s importance in transfusion prediction models.

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