Development of Multivariable Models to Predict and Benchmark Transfusion in Elective Surgery Supporting Patient Blood Management

Abstract

Background: Blood transfusion is a highly prevalent procedure in hospitalized patients and in some clinical scenarios it has lifesaving potential. However, in most cases transfusion is administered to hemodynamically stable patients with no benefit, but increased odds of adverse patient outcomes and substantial direct and indirect cost. Therefore, the concept of Patient Blood Management has increasingly gained importance to pre-empt and reduce transfusion and to identify the optimal transfusion volume for an individual patient when transfusion is indicated.

Objectives: It was our aim to describe, how predictive modeling and machine learning tools applied on pre-operative data can be used to predict the amount of red blood cells to be transfused during surgery and to prospectively optimize blood ordering schedules. In addition, the data derived from the predictive models should be used to benchmark different hospitals concerning their blood transfusion patterns.

Methods: 6,530 case records obtained for elective surgeries from 16 centers taking part in two studies conducted in 2004-2005 and 2009-2010 were analyzed. Transfused red blood cell volume was predicted using random forests. Separate models were trained for overall data, for each center and for each of the two studies. Important characteristics of different models were compared with one another.

Results: Our results indicate that predictive modeling applied prior surgery can predict the transfused volume of red blood cells more accurately (correlation coefficient cc = 0.61) than state of the art algorithms (cc = 0.39). We found significantly different patterns of feature importance a) in different hospitals and b) between study 1 and study 2.

Conclusion: We conclude that predictive modeling can be used to benchmark the importance of different features on the models derived with data from different hospitals. This might help to optimize crucial processes in a specific hospital, even in other scenarios beyond Patient Blood Management.

Keywords: Predictive modelling; benchmarking; blood transfusion; machine learning; patient blood management; random forests.

Fig. 1

Leave 10% out approach used for training, prediction and statistical evaluation of each model, including calculation of model accuracy and feature importance.

Fig. 2

Centre specific model design for benchmarking feature importance across centers.

Fig. 3

Feature importance (left) of the 22 features used during modelling (see table 1 for a description of the features) and correlation coefficient between actual and predicted transfused red blood cell (RBC) volume (right). 10 models were built applying a leave-10%-out approach. Boxplots summarize the results achieved for these 10 models.

Fig. 4

Example of a visualization of benchmarking data for the feature importance as achieved for a single center (center 8 of the Austrian Benchmarking Study 1, horizontal lines) with the respective values of all other centers represented by boxplots (left). Correlation coefficients between predicted an actual transfused (RBC) volume and actual transfused RBC volume are shown on the right. 32 models were built (one per center). Boxplots summarize the results achieved for each single center.

Fig. 5

Comparison of feature importance (left), correlation coefficient in between predicted and actual value of transfused RBC volume (middle) and actual transfused red blood cell (RBC) volume (right) as achieved for the first (top) and the second (bottom) Austrian Benchmarking Study. Results of ten submodels – each developed and validated with a leave 10 % out approach – are shown as boxplots.