Prediction of perioperative transfusions using an artificial neural network

Steven Walczak, Vic Velanovich, Steven Walczak, Vic Velanovich

Abstract

Background: Accurate prediction of operative transfusions is essential for resource allocation and identifying patients at risk of postoperative adverse events. This research examines the efficacy of using artificial neural networks (ANNs) to predict transfusions for all inpatient operations.

Methods: Over 1.6 million surgical cases over a two year period from the NSQIP-PUF database are used. Data from 2014 (750937 records) are used for model development and data from 2015 (885502 records) are used for model validation. ANN and regression models are developed to predict perioperative transfusions for surgical patients.

Results: Various ANN models and logistic regression, using four variable sets, are compared. The best performing ANN models with respect to both sensitivity and area under the receiver operator characteristic curve outperformed all of the regression models (p < .001) and achieved a performance of 70-80% specificity with a corresponding 75-62% sensitivity.

Conclusion: ANNs can predict >75% of the patients who will require transfusion and 70% of those who will not. Increasing specificity to 80% still enables a sensitivity of almost 67%. The unique contribution of this research is the utilization of a single ANN model to predict transfusions across a broad range of surgical procedures.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Cohort selection flow diagram.
Fig 1. Cohort selection flow diagram.
Fig 2. Sample ANN architectures for variable…
Fig 2. Sample ANN architectures for variable sets Set 2 and Set 3.
Size of the boxes representing the processing elements in the ANN indicates the size of numeric difference from zero. Hollow boxes indicate negative values and filled in boxes indicate zero or positive values. Dashed lines represent the weighted connections between processing elements, with the length of the dashes indicating positive or negative values and the color indicating relative value.

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