Kompendium 2020 Forschung & Klinik

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37
A Prediction Model for
Total Knee Arthroplasty
A collaborative group of researchers consisting of clinicians,
biostatisticians and anatomists initiated this study in order to
determine patient specific factors that could predict whether a
patient will have to undergo Total Knee Arthroplasty (TKA) within
two years. This work resulted in the establishment of an easily
applicable prediction model based on Artificial Neural Networks
(ANN), as well as in a publication entitled „Predicting Total Knee
Replacement from Symptomology and Radiographic Structural
Change Using Artificial Neural Networks—Data from the Osteoarthritis
Initiative (OAI)“ in the Journal of Clinical Medicine 1 .
As our clinic is a well renowned excellence center for total joint arthroplasty
(EndoCert), we are constantly concerned with improving patient care
by ongoing research. Considering the crucial role of timing of TKA, as well
as the progression of osteoarthritis as the underlying disease, we aimed to
determine factors that could predict TKA two years in advance. In this effort
clinicians from our department teamed up with a biostatistics professor
from Paracelsus Medical University and an anatomy professor from Mayo
Clinic College of Medicine. By using artificial neural networks we succeeded
to establish a prediction model that was able to correctly predict 80%
of the classified individuals to undergo TKA surgery, with a positive predictive
value of 84% and a negative predictive value of 73% 1 .
As commonly known, osteoarthritis of the knee contributes significantly to
the patient’s individual disability and impaired health-related quality of life,
and its treatment imposes a great socioeconomic burden, which is likely to
increase further, as we have shown in a previous study 2 .
Study:
Heisinger S, Hitzl W, Hobusch
GM, Windhager R, Cotofana S.
Predicting Total Knee Replacement
from Symptomology
and Radiographic Structural
Change Using Artificial
Neural Networks-Data from
the Osteoarthritis Initiative
(OAI). J Clin Med. 2020 May
1;9(5):1298.
Patients and Methods
In this study we included the radiographic and clinical data of 165 patients that
were enrolled in the Osteoarthritis Initiative study, representing a well-established
database for osteoarthritis research, which is accessible at nda.nih.gov 3 .
Patient data were analyzed longitudinally and changes were identified as
shown in Figure 1 (WOMAC total: 9.7 95% CI (7–12.5), p = < 0.0001; WOMAC
pain subscore: 0.5 (1.5–3), p = < 0.0001; quality of life 9.4 (6.3–12.6), p = <
0.0001; and pain intensity 1.5 (1–2), p = < 0.0001 ). While the radiographic
status constantly worsened between the timepoints prior to TKA, the symptomology
started to significantly worsen 1 year before surgery. In order to
develop a prediction model we used Artificial neural networks (ANNs) 1 .