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Bone & Joint Open
Vol. 6, Issue 3 | Pages 328 - 335
12 Mar 2025
Logishetty K Verhaegen JCF Tse S Maheswaran T Fornasiero M Subbiah Ponniah H Hutt JB Witt JD

Aims

The effectiveness of total hip arthroplasty (THA) for patients with no or minimal radiological signs of osteoarthritis (OA) is unclear. In this study, we aimed to: 1) assess the outcome of such patients; 2) identify patient comorbidities and CT or MRI findings which predicted outcome; and 3) compare their outcome to the expected outcome of THA for hip OA.

Methods

Adult patients undergoing THA for hip pain, with no or minimal radiological features of OA (Tönnis grading scale ≤ 1), were identified from a consecutive series of 1,925 THAs. Exclusion criteria were: inflammatory arthritis; osteonecrosis of the femoral head; prior trauma or infection; and patients without minimum one-year follow-up and patient-reported outcome measures (PROMs). The primary outcome measure was the Oxford Hip Score (OHS). Secondary outcome measures were EuroQol-visual analogue scale (EQ-VAS), University of California and Los Angeles (UCLA) scale, and patient satisfaction on a validated three-point ‘better’, ‘same’, or ‘worse’ scale.


The Bone & Joint Journal
Vol. 104-B, Issue 4 | Pages 486 - 494
4 Apr 2022
Liu W Sun Z Xiong H Liu J Lu J Cai B Wang W Fan C

Aims

The aim of this study was to develop and internally validate a prognostic nomogram to predict the probability of gaining a functional range of motion (ROM ≥ 120°) after open arthrolysis of the elbow in patients with post-traumatic stiffness of the elbow.

Methods

We developed the Shanghai Prediction Model for Elbow Stiffness Surgical Outcome (SPESSO) based on a dataset of 551 patients who underwent open arthrolysis of the elbow in four institutions. Demographic and clinical characteristics were collected from medical records. The least absolute shrinkage and selection operator regression model was used to optimize the selection of relevant features. Multivariable logistic regression analysis was used to build the SPESSO. Its prediction performance was evaluated using the concordance index (C-index) and a calibration graph. Internal validation was conducted using bootstrapping validation.