Kompendium 2020 Forschung & Klinik

TOP-Studien
38
Figure 1: Longitudinal change of WOMAC totalscore, WOMAC pain subscore,
quality of life and pain intensity.
„Our model is based on easily accessible
patient data, making it easily applicable
in a primary care setting to evaluate the
need for TKA surgery within the next
two years and to give the patient an idea
about their status quo.“
Stephan Heisinger
In total 14 variables were included, and the most predictive group consisting
of pain intensity, Kellgren and Lawrence grades, WOMAC total score, use of
pain medication, and body mass index was identified by the integrated neural
network variable selection algorithm to have the best outcome performance
for predicting TKA surgery within 2 years. Furthermore, more than 250 neural
networks models with different network architecture were created, tested,
and protected against overlearning by splitting the patient sample into a
training, a verification, and a test sample, and monitored accordingly (ratio
2:1:1). We used two prediction thresholds for acceptance and for rejection,
i.e., if data of a subject fall into this area, no prediction is made. All analyses
were done using PASW 22 (IBM SPSS Statistics for Windows, Version 19.0.,
Armonk, NY, USA), StatXact 10 (Cytel Software 2013, Cambridge MA, USA),
Mathematica 7 (Wolfram Research, Inc., Mathematica, Version 7.0, Champaign,
IL, USA), STATISTICA for neural networks 1.2 and STATISTICA 13 (Hill, T. &
Lewicki, P. Statistics: Methods and Applications. StatSoft, Tulsa, OK, USA) 1 .
After the application of the variable selection algorithm and testing more
than 250 models, KL grades, WOMAC total score, body mass index measures,
pain intensity, and pain medication revealed the following performance: a
negative predictive value of 73% (52–88%), a positive predictive value of
84% (71–94%), and total percentage of correctly predicted knees of 80%
(69–89%) (1). To graphically visualize the neural network output and thereby
our prediction model we created a simplified and easily applicable chart, as
depicted in Figure 2.