Kompendium 2020 Forschung & Klinik

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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.
TOP-Studien 39 Figure 2: Graphic visualization of the neural network output illustrating the three-layer perceptron decision areas, including the variables pain intensity, Kellgren and Lawrence grades, WOMAC total score, use of medication (for pain, aching, or stiffness in the knee), and body mass index into the neural network. Areas represent: green area = no TKA within next 2 years, red area = TKA within next 2 years, and grey area = no prediction is made (1). Results and Conclusion Concludingly, our results indicate that significant worsening of pain, function, and quality of life in the year prior to the TKA seems to be the more dominant decision driving factor as compared to the radiographic structural changes of the knee. Furthermore, we were able to establish a model that correctly predicted 80% of the classified individuals to undergo TKA surgery within the next 2 years, with a positive predictive value of 84%, and a negative predictive value of 73% 1 . 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. Dr. Stephan Heisinger Author: Stephan Heisinger completed his residency at the Department of Orthopedics and Trauma Surgery at the Medical University of Vienna and is currently engaged in a broad variety of research fields with a focus on the application of novel research tools, such as Artificial Neural Networks. References: 1 Walzer SM, Toegel S, Chiari C, Farr S, Rinner B, Weinberg AM, Weinmann D, Fischer MB, Windhager R.; A three dimensional model of zonally organized cartilaginous matrix in vitro. (in submission) 2 Kolb A, Robinson S, Stelzeneder D, Schreiner M, Chiari C, Windhager R, Trattnig S, Bohndorf K; Vessel architecture in human knee cartilage in children: an in vivo susceptibility-weighted imaging study at 7 T; Eur Radiol. 2018 Aug;28(8):3384-3392. doi: 10.1007/s00330-017-5290-1. Epub 2018 Feb 26. 3 Kolb A, Benca E, Willegger M, Puchner SE, Windhager R, Chiari C.; Measurement considerations on examiner-dependent factors in the ultrasound assessment of developmental dysplasia of the hip; Int Orthop. 2017 Jun;41(6):1245–1250.(2)
Page 1 and 2: Kompendium 2020 Forschung & Klinik
Page 3 and 4: Inhalt/Impressum 3 Die Klinik 4 Edi
Page 6 and 7: Zahlen und Fakten 6 Klinische Leist
Page 9 and 10: Zahlen und Fakten 9 Hohe Ambulanzfr
Page 11 and 12: Klinik 11 Neben der Polytrauma-Vers
Page 13 and 14: Die Meniskusversorgung Alle Möglic
Page 15 and 16: Das ÄrztInnenteam 15 Universitäts
Page 17 and 18: Spherox ist das einzig EMA-zugelass
Page 19 and 20: Das ÄrztInnenteam 19 Medizinische
Page 21 and 22: Ambulanzen 21 Spezialambulanzen Kli
Page 23 and 24: TOP-Studien 23 defects (≤ Interna
Page 25 and 26: WE LENGTHEN, TRANSPORT AND GROW BON
Page 27 and 28: TOP-Studien 27 5 5 Patients (-) 10
Page 29 and 30: FRESH AIR FRESH PERSPECTIVE Remove
Page 31 and 32: TOP-Studien 31 Aseptic loosening is
Page 33 and 34: Integriertes Patientenversorgungsko
Page 35 and 36: TOP-Studien 35 Figure 1: Bone miner
Page 37: TOP-Studien 37 A Prediction Model f
Page 41 and 42: TOP-Studien 41 Figure 1: Individual
Page 43 and 44: 3D Biochips for Research on Inflamm
Page 45 and 46: TOP-Studien 45 Chondro-Synovial Cro
Page 47 and 48: TOP-Studien 47 Figure 1. (A) Intrao
Page 49 and 50: TOP-Studien 49 Total Hip Arthroplas
Page 51 and 52: TOP-Studien 51 nerve lesions (1 N.
Page 53 and 54: TOP-Studien 53 1 Volume fill of car
Page 55 and 56: TOP-Studien The Genetic Landscape o
Page 57 and 58: TOP-Studien 57 rare heterozygous lo
Page 59 and 60: TOP-Studien 59 Figure 1: Example of
Page 61 and 62: COPAL ® - ERFOLGREICH GEGEN INFEKT
Page 63 and 64: TOP-Studien 63 Figure 1: Biomechani
Page 65 and 66: TOP-Studien 65 Development of a New
Page 67 and 68: TOP-Studien 67 Table 1: Relative ro
Page 69 and 70: Publikationen 69 Bumberger A, Kolle
Page 71 and 72: Publikationen 71 sou NA, McNally MA
Page 73 and 74: Publikationen 73 Propensity-Matched
Page 75 and 76: Publikationen 75 novel hypothetical
Page 77 and 78: Publikationen 77 ritisches Denken.
Page 79 and 80: Publikationen 79 Zuteilungsdatum: 2
Page 81 and 82: Publikationen 81 Payr S: Reviewer J
Page 83 and 84: Publikationen 83 fracture, Wrist Ar
Page 85 and 86: Kundenkompetenzzentrum mit Shop & W
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