Chondrosarcoma is the second most common surgically treated primary bone sarcoma. Despite a large number of scientific papers in the literature, there is still significant controversy about diagnostics, treatment of the primary tumour, subtypes, and complications. Therefore, consensus on its day-to-day treatment decisions is needed. In January 2024, the Birmingham Orthopaedic
Prediction tools are instruments which are commonly used to estimate the prognosis in
Aims. With recent progress in cancer treatment, the number of advanced-age patients with spinal metastases has been increasing. It is important to clarify the influence of advanced age on outcomes following surgery for spinal metastases, especially with a focus on subjective health state values. Methods. We prospectively analyzed 101 patients with spinal metastases who underwent palliative surgery from 2013 to 2016. These patients were divided into two groups based on age (< 70 years and ≥ 70 years). The Eastern Cooperative
Frailty has been gathering attention as a factor to predict surgical outcomes. However, the association of frailty with postoperative complications remains controversial in spinal metastases surgery. We therefore designed a prospective study to elucidate risk factors for postoperative complications with a focus on frailty. We prospectively analyzed 241 patients with spinal metastasis who underwent palliative surgery from June 2015 to December 2021. Postoperative complications were assessed by the Clavien-Dindo classification; scores of ≥ Grade II were defined as complications. Data were collected regarding demographics (age, sex, BMI, and primary cancer) and preoperative clinical factors (new Katagiri score, Frankel grade, performance status, radiotherapy, chemotherapy, spinal instability neoplastic score, modified Frailty Index-11 (mFI), diabetes, and serum albumin levels). Univariate and multivariate analyses were developed to identify risk factors for postoperative complications (p < 0.05).Aims
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Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.Aims
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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. 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.Aims
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