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Improvement of femoral component size prediction using a C-arm intensifier guide and our established algorithm in unicompartmental knee arthroplasty: A report from a Chinese population.
Improvement of femoral component size prediction using a C-arm intensifier guide and our established algorithm in unicompartmental knee arthroplasty: A report from a Chinese population.
Knee. 2013 Jul 23;
Authors: Tu Y, Xue H, Cai M, Ma T, Liu X, Xia Z
Abstract
BACKGROUND: Unicompartmental knee arthroplasty (UKA) is becoming more widely used with the recent increase in popularity of the use of minimally invasive techniques. However, it is difficult to judge about the femoral component size in UKA using preoperative templating digitally or otherwise. Even when using navigation it is impossible to control the femoral component size. The aim of this study is to develop a better pre- or intra-operative measure that will predict femoral component size.
METHODS: Ninety-two UKA cases were studied from June 2007 to December 2011 with a mean 26-month follow-up. We developed an intra-operative C-arm intensifier guide (CAIG) method for determining femoral size instead of pre-operative templating. The accuracy of prediction of both methods was compared from a review of post-operative radiographs. In addition, we summarized all cases and developed a Chinese algorithm to determine the femoral component size pre-operatively.
RESULTS: There was a significant difference between templating (59%) and CAIG (92%) method (P=0.0001). In the Chinese algorithm, height based on gender and tibial size both have greater accuracy of prediction (88% and 70.7%) than the Oxford algorithm (51.1% and 59.8%). Component size distribution and optimal tibial/femoral pairing differed from those in the Oxford report.
CONCLUSIONS: We conclude that the Chinese algorithm can greatly improve the accuracy of prediction of femoral component size. In addition, CAIG-assisted implantation of a UKR is a reliable intra-operative tool and can aid size selection of the femoral component.
LEVEL OF EVIDENCE: Level III.
PMID: 23890472 [PubMed - as supplied by publisher]
