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The Bone & Joint Journal
Vol. 106-B, Issue 10 | Pages 1125 - 1132
1 Oct 2024
Luengo-Alonso G Valencia M Martinez-Catalan N Delgado C Calvo E

Aims

The prevalence of osteoarthritis (OA) associated with instability of the shoulder ranges between 4% and 60%. Articular cartilage is, however, routinely assessed in these patients using radiographs or scans (2D or 3D), with little opportunity to record early signs of cartilage damage. The aim of this study was to assess the prevalence and localization of chondral lesions and synovial damage in patients undergoing arthroscopic surgery for instablility of the shoulder, in order to classify them and to identify risk factors for the development of glenohumeral OA.

Methods

A total of 140 shoulders in 140 patients with a mean age of 28.5 years (15 to 55), who underwent arthroscopic treatment for recurrent glenohumeral instability, were included. The prevalence and distribution of chondral lesions and synovial damage were analyzed and graded into stages according to the division of the humeral head and glenoid into quadrants. The following factors that might affect the prevalence and severity of chondral damage were recorded: sex, dominance, age, age at the time of the first dislocation, number of dislocations, time between the first dislocation and surgery, preoperative sporting activity, Beighton score, type of instability, and joint laxity.


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.