Literature DB >> 18019459

Comparative repeatability of guide-pin axis positioning in computer-assisted and manual femoral head resurfacing arthroplasty.

A Hodgson1, N Helmy, B A Masri, N V Greidanus, K B Inkpen, C P Duncan, D S Garbuz, C Anglin.   

Abstract

The orientation of the femoral component in hip resurfacing arthroplasty affects the likelihood of loosening and fracture. Computer-assisted surgery has been shown to improve significantly the surgeon's ability to achieve a desired position and orientation; nevertheless, both bias and variability in positioning remain and can potentially be improved. The authors recently developed a computer-assisted surgical (CAS) technique to guide the placement of the pin used in femoral head resurfacing arthroplasty and showed that it produced significantly less variation than a typical manual technique in varus/valgus placement relative to a preoperatively determined surgical plan while taking a comparable amount of time. In the present study, the repeatability of both the CAS and manual techniques is evaluated in order to estimate the relative contributions to overall variability of surgical technique (CAS versus manual), surgeon experience (novice versus experienced), and other sources of variability (e.g. across specimens and across surgeons). This will enable further improvements in the accuracy of CAS techniques. Three residents/fellows new to femoral head resurfacing and three experienced hip arthroplasty surgeons performed 20-30 repetitions of each of the CAS and manual techniques on at least one of four cadaveric femur specimens. The CAS system had markedly better repeatability (1.2 degrees) in varus/valgus placement relative to the manual technique (2.8 degrees), slightly worse repeatability in version (4.4 degrees versus 3.2 degrees), markedly better repeatability in mid-neck placement (0.7 mm versus 2.5 mm), no significant dependence on surgeon skill level (in contrast to the manual technique), and took significantly less time (50 s versus 123 s). Proposed improvements to the version measurement process showed potential for reducing the standard deviation by almost two thirds. This study supports the use of CAS for femoral head resurfacing as it is quicker than the manual technique, independent of surgeon experience, and demonstrates improved repeatability.

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Year:  2007        PMID: 18019459     DOI: 10.1243/09544119JEIM284

Source DB:  PubMed          Journal:  Proc Inst Mech Eng H        ISSN: 0954-4119            Impact factor:   1.617


  7 in total

1.  [Imageless computer navigation of hip resurfacing arthroplasty].

Authors:  Christoph Schnurr; Jochen Nessler; Jürgen Koebke; Joern William Michael; Peer Eysel; Dietmar Pierre König
Journal:  Oper Orthop Traumatol       Date:  2010-07       Impact factor: 1.154

2.  Component alignment in hip resurfacing using computer navigation.

Authors:  Chris Bailey; Rehan Gul; Mark Falworth; Steven Zadow; Roger Oakeshott
Journal:  Clin Orthop Relat Res       Date:  2008-10-30       Impact factor: 4.176

3.  A custom-made guide-wire positioning device for hip surface replacement arthroplasty: description and first results.

Authors:  Martijn Raaijmaakers; Frederik Gelaude; Karla De Smedt; Tim Clijmans; Jeroen Dille; Michiel Mulier
Journal:  BMC Musculoskelet Disord       Date:  2010-07-14       Impact factor: 2.362

4.  Use of patient-specific templates in hip resurfacing arthroplasty: experience from sixteen cases.

Authors:  Hao Du; Xiao-xiao Tian; Tong-sen Li; Jin-sheng Yang; Ke-han Li; Guo-xian Pei; Le Xie
Journal:  Int Orthop       Date:  2013-03-02       Impact factor: 3.075

5.  Accuracy of computer-assisted navigation for femoral head resurfacing decreases in hips with abnormal anatomy.

Authors:  Rocco P Pitto; Sharif Malak; Iain A Anderson
Journal:  Clin Orthop Relat Res       Date:  2009-05-07       Impact factor: 4.176

6.  Accuracy of a computer-assisted navigation system in resurfacing hip arthroplasty.

Authors:  R P Pitto; S Malak; I A Anderson
Journal:  Int Orthop       Date:  2008-08-29       Impact factor: 3.075

7.  Machine learning prediction models in orthopedic surgery: A systematic review in transparent reporting.

Authors:  Olivier Q Groot; Paul T Ogink; Amanda Lans; Peter K Twining; Neal D Kapoor; William DiGiovanni; Bas J J Bindels; Michiel E R Bongers; Jacobien H F Oosterhoff; Aditya V Karhade; F C Oner; Jorrit-Jan Verlaan; Joseph H Schwab
Journal:  J Orthop Res       Date:  2021-03-29       Impact factor: 3.102

  7 in total

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