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Bone & Joint Open
Vol. 3, Issue 4 | Pages 291 - 301
4 Apr 2022
Holleyman RJ Lyman S Bankes MJK Board TN Conroy JL McBryde CW Andrade AJ Malviya A Khanduja V

Aims

This study uses prospective registry data to compare early patient outcomes following arthroscopic repair or debridement of the acetabular labrum.

Methods

Data on adult patients who underwent arthroscopic labral debridement or repair between 1 January 2012 and 31 July 2019 were extracted from the UK Non-Arthroplasty Hip Registry. Patients who underwent microfracture, osteophyte excision, or a concurrent extra-articular procedure were excluded. The EuroQol five-dimension (EQ-5D) and International Hip Outcome Tool 12 (iHOT-12) questionnaires were collected preoperatively and at six and 12 months post-operatively. Due to concerns over differential questionnaire non-response between the two groups, a combination of random sampling, propensity score matching, and pooled multivariable linear regression models were employed to compare iHOT-12 improvement.


The Bone & Joint Journal
Vol. 101-B, Issue 6_Supple_B | Pages 68 - 76
1 Jun 2019
Jones CW Choi DS Sun P Chiu Y Lipman JD Lyman S Bostrom MPG Sculco PK

Aims

Custom flange acetabular components (CFACs) are a patient-specific option for addressing large acetabular defects at revision total hip arthroplasty (THA), but patient and implant characteristics that affect survivorship remain unknown. This study aimed to identify patient and design factors related to survivorship.

Patients and Methods

A retrospective review of 91 patients who underwent revision THA using 96 CFACs was undertaken, comparing features between radiologically failed and successful cases. Patient characteristics (demographic, clinical, and radiological) and implant features (design characteristics and intraoperative features) were collected. There were 74 women and 22 men; their mean age was 62 years (31 to 85). The mean follow-up was 24.9 months (sd 27.6; 0 to 116). Two sets of statistical analyses were performed: 1) univariate analyses (Pearson’s chi-squared and independent-samples Student’s t-tests) for each feature; and 2) bivariable logistic regressions using features identified from a random forest analysis.