Literature DB >> 33221693

Does component axial rotational alignment affect clinical outcomes in Oxford unicompartmental knee arthroplasty?

Jonathan Patrick Ng1, Jason Chi Ho Fan2, Wang Wai Chau3, Chun Man Lau2, Yik Cheung Wan2, Tycus Tao Sun Tse2, Yuk Wah Hung2.   

Abstract

BACKGROUND: Limited studies have examined the relationship between axial rotational alignment and functional outcome in mobile-bearing UKA. The aims of this study was to determine the correlation between component axial rotational alignment and functional outcomes, and to recommend a safety range for component rotation for Oxford UKA.
METHODS: A retrospective study of 83 Oxford UKA was performed in 67 patients. Postoperative CT scans and clinical assessments were performed at a mean follow up of 21 months. Functional outcomes were measured by the OKS, modified KSS and KFS scores. A moving threshold analysis was performed to evaluate the relationship between different rotational alignment cut-off values and functional outcome scores.
RESULTS: The mean femoral and tibial components were positioned with a mean of 4.8° and 7.5° external rotation (ER), respectively. Increasing tibial external rotation was negatively correlated with clinical outcome scores while increasing femoral component rotation did not correlate with clinical outcomes. Better functional scores were observed at mean femoral and tibial rotation angles between 2-6° ER (1.2-6.6°) and 1-8° ER (0.5-8.8°), respectively; with the highest OKS, KSS and FKS observed at 3-4° ER for femoral component, and 4-5° ER for tibial component.
CONCLUSION: Femoral component axial rotation between 2°- 6° ER, and tibial component axial rotation between 1° and 8° ER correlated with significantly better functional scores. Surgeons should be especially aware of the relatively high variability in tibial component rotation and its implications of functional outcomes.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Component axial rotational alignment; Functional outcome; Mobile-bearing; Unicompartmental arthroplasty

Mesh:

Year:  2020        PMID: 33221693     DOI: 10.1016/j.knee.2020.10.016

Source DB:  PubMed          Journal:  Knee        ISSN: 0968-0160            Impact factor:   2.199


  1 in total

1.  A novel image-based machine learning model with superior accuracy and predictability for knee arthroplasty loosening detection and clinical decision making.

Authors:  Lawrence Chun Man Lau; Elvis Chun Sing Chui; Gene Chi Wai Man; Ye Xin; Kevin Ki Wai Ho; Kyle Ka Kwan Mak; Michael Tim Yun Ong; Sheung Wai Law; Wing Hoi Cheung; Patrick Shu Hang Yung
Journal:  J Orthop Translat       Date:  2022-10-06       Impact factor: 4.889

  1 in total

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