Frank Lampe1,2, Carlos J Marques3, Franziska Fiedler4, Anusch Sufi-Siavach4, Ana I Carita5, Georg Matziolis6. 1. Research Center of the Orthopedic and Joint Replacement Department, Schoen Klinik Hamburg Eilbek, Dehnhaide 120, 22081, Hamburg, Germany. 2. Faculty of Life Sciences, Hamburg University of Applied Sciences, Lohbrügger Kirchstraße 65, 21033, Hamburg, Germany. 3. Research Center of the Orthopedic and Joint Replacement Department, Schoen Klinik Hamburg Eilbek, Dehnhaide 120, 22081, Hamburg, Germany. cmarques@schoen-kliniken.de. 4. Department of Orthopedics and Joint Replacement, Schoen Klinik Hamburg Eilbek, Dehnhaide 120, 22081, Hamburg, Germany. 5. Mathematic Methods Department, Faculty of Human Kinetics, Lisbon University, Estrada da Costa, 1499-002, Cruz Quebrada, Portugal. 6. Orthopaedic Department, Jena University Hospital, Campus Eisenberg, Klosterlausnitzer Straße 81, 07607, Eisenberg, Germany.
Abstract
PURPOSE: There are multiple factors affecting maximal knee flexion (MKF) after total knee arthroplasty (TKA). The aim of the study was to investigate whether patient-specific factors (PSF) and surgically modifiable factors (SMF), measured by means of a computer-assisted navigation system, can predict the MKF after TKA. METHODS: Data from 99 patients collected during a randomized clinical trial were used for this secondary data analysis. The MKF of the patients was measured preoperatively and 1-year post-surgery. Multiple regression analyses were performed to investigate which combination of variables would be the best to predict the 1-year MKF. RESULTS: When considering SMF alone, the combination of three factors significantly predicted the 1-year MKF (p = 0.001), explaining 22 % of its variation. When considering only PSF, the combination of pre-op MKF and BMI significantly predicted the 1-year MKF (p < 0.001), explaining 23 % of its variation. When considering both groups of potential predictors simultaneously, the combination of five SMF with two PSF significantly predicted the 1-year MKF (p = 0.001), explaining 32 % of its variation. CONCLUSIONS: Computer navigation variables alone could explain 22 % of the variance in the 1-year MKF. The larger proportion (32 %) of the 1-year MKF variation could be explained with a combination of SMF and PSF. The results of studies in this area could be used to identify patients at risk of poor outcomes. LEVEL OF EVIDENCE: Level II, Prognostic study.
RCT Entities:
PURPOSE: There are multiple factors affecting maximal knee flexion (MKF) after total knee arthroplasty (TKA). The aim of the study was to investigate whether patient-specific factors (PSF) and surgically modifiable factors (SMF), measured by means of a computer-assisted navigation system, can predict the MKF after TKA. METHODS: Data from 99 patients collected during a randomized clinical trial were used for this secondary data analysis. The MKF of the patients was measured preoperatively and 1-year post-surgery. Multiple regression analyses were performed to investigate which combination of variables would be the best to predict the 1-year MKF. RESULTS: When considering SMF alone, the combination of three factors significantly predicted the 1-year MKF (p = 0.001), explaining 22 % of its variation. When considering only PSF, the combination of pre-op MKF and BMI significantly predicted the 1-year MKF (p < 0.001), explaining 23 % of its variation. When considering both groups of potential predictors simultaneously, the combination of five SMF with two PSF significantly predicted the 1-year MKF (p = 0.001), explaining 32 % of its variation. CONCLUSIONS: Computer navigation variables alone could explain 22 % of the variance in the 1-year MKF. The larger proportion (32 %) of the 1-year MKF variation could be explained with a combination of SMF and PSF. The results of studies in this area could be used to identify patients at risk of poor outcomes. LEVEL OF EVIDENCE: Level II, Prognostic study.
Entities:
Keywords:
Computer-assisted surgery; Joint range of motion; Prognosis; Total knee replacement
Authors: Carlos J Marques; Christian Bauer; Dafne Grimaldo; Steffen Tabeling; Timo Weber; Alexander Ehlert; Alexandre H Mendes; Juergen Lorenz; Frank Lampe Journal: Sensors (Basel) Date: 2020-04-15 Impact factor: 3.576