Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2016
Patient-specific and intra-operatively modifiable factors assessed by computer navigation predict maximal knee flexion one year after TKA.
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Lampe Frank, Marques Carlos J, Fiedler Franziska, Sufi-Siavach Anusch, Carita Ana I, Matziolis Georg
Аннотация
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.
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.