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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.


Orthopaedic Proceedings
Vol. 100-B, Issue SUPP_13 | Pages 71 - 71
1 Oct 2018
Bostrom MPG Jones CW Choi D Sun P Chui Y Lipman JD Lyman S Chiu Y
Full Access

Introduction

Custom flanged acetabular components (CFAC) have been shown to be effective in treating complex acetabular reconstructions in revision total hip arthroplasty (THA). However, the specific patient factors and CFAC design characteristics that affect the overall survivorship remain unclear. Once the surgeon opts to follow this treatment pathway, numerous decisions need to be made during the pre-operative design phase and during implantation, which may influence the ultimate success of CFAC. The goal of this study was to retrospectively review the entire cohort of CFAC cases performed at a large volume institution and to identify any patient, surgeon, or design factors that may be related to the long-term survival of these prostheses.

Methods

We reviewed 96 CFAC cases performed in 91 patients between 2004 and 2017, from which 36 variables were collected spanning patient demographics, pre-operative clinical and radiographic features, intraoperative information, and implant design characteristics. Patient demographics and relevant clinical features were collected from individual medical records. Radiographic review included analysis of pre-operative radiographs, computer tomographic (CT) scans, and serial post-operative radiographs. Radiographic failure was defined as loosening or gross migration as determined by a board-certified orthopedic surgeon. CFAC implant design characteristics and intra-operative features were collected from the design record, surgical record and post-operative radiograph for each case respectively.

Two sets of statistical analyses were performed with this dataset. First, univariate analyses were performed for each variable, comprising of a Pearson chi-square test for categorical variables and an independent t-test for continuous variables. Second, a random forest supervised machine learning method was applied to identify the most influential variables within the dataset, which were then used to perform a bivariable logistic regression to generate odds ratios. Statistical significance for this study was set at p < 0.05.