Summary
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.
Citation: Hayn D, Kreiner K, Ebner H, Kastner P, Breznik N, Rzepka A, Hofmann A, Gombotz H,
Schreier G. Development of multivariable models to predict and benchmark transfusion
in elective surgery supporting patient blood management. Appl Clin Inform 2017; 8:
617–631 https://doi.org/10.4338/ACI-2016-11-RA-0195
Keywords
Predictive modelling - random forests - machine learning - benchmarking - blood transfusion
- patient blood management
