ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2016
Patient-specific and intra-operatively modifiable factors assessed by computer navigation predict maximal knee flexion one year after TKA.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of Metabolic Factors and Inflammatory Markers Predictive of Outcome after Total Knee Arthroplasty in Patients with Knee Osteoarthritis: A Systematic Review.International journal of environmental research and public health · 2023Pooled it
- Initiating range of motion exercises within 24 hours following total knee arthroplasty affects the reduction of postoperative pain: A randomized controlled trial.Asia-Pacific journal of sports medicine, arthroscopy, rehabilitation and technology · 2020Article
- Sensor Positioning Influences the Accuracy of Knee Rom Data of an E-Rehabilitation System: A Preliminary Study with Healthy Subjects.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThere 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.
methodsData 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.
resultsWhen 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.
conclusionsComputer 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.
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