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The Bone & Joint Journal
Vol. 104-B, Issue 9 | Pages 1011 - 1016
1 Sep 2022
Acem I van de Sande MAJ

Prediction tools are instruments which are commonly used to estimate the prognosis in oncology and facilitate clinical decision-making in a more personalized manner. Their popularity is shown by the increasing numbers of prediction tools, which have been described in the medical literature. Many of these tools have been shown to be useful in the field of soft-tissue sarcoma of the extremities (eSTS). In this annotation, we aim to provide an overview of the available prediction tools for eSTS, provide an approach for clinicians to evaluate the performance and usefulness of the available tools for their own patients, and discuss their possible applications in the management of patients with an eSTS. Cite this article: Bone Joint J 2022;104-B(9):1011–1016


The Bone & Joint Journal
Vol. 107-B, Issue 2 | Pages 239 - 245
1 Feb 2025
Evenhuis RE Bus MPA van Nes J Walter SG Cabrolier J Fiocco M van der Wal RJP Broekhuis D Sellevold S van de Sande MAJ

Aims

Over time, the locking mechanism of Modular Universal Tumour and Revision System (MUTARS) knee arthroplasties changed from polyethylene (PE) to polyether-ether-ketone Optima (PEEK) and metal-on-metal (MoM) in an attempt to reduce the risk of mechanical failure. In this study, we aimed to assess the cumulative incidence of locking mechanism revision for symptomatic instability by type of material, and assess potential associated risk factors.

Methods

The MUTARS Orthopaedic Registry Europe was used for a retrospective review of 316 patients (54% male (n = 170), median age 44 years (IQR 23 to 61)) who underwent a MUTARS knee arthroplasty for oncological indications between December 1995 and January 2023. The minimum follow-up was 12 months, and the median follow-up was 7.9 years (IQR 3.3 to 13.0). A competing risk model was used to estimate the cumulative incidence of first locking mechanism revision with death and revision for any other reason as competing events. Possible risk factors were assessed employing a univariate cause-specific hazards regression model.