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Introduction: The incidence of acetabular fractures in older patients is increasing. The management of these patients is evolving due to the recognition of risks associated with prolonged immobility with conservative treatment. Materials and methods: Consecutive patients undergoing fixation and total hip replacement (THR) for displaced acetabular fractures undergoing single operation with acetabular fixation and THR were identified. Outcomes were assessed using radiographs, clinical notes, Oxford Hip Score and EuroQol-5L. Results: 77 patients were identified with 51 completing outcome scores. Mean age 68 years at time of injury. Mean follow-up 5 (2–12) years, OHS 40, EQ-5D 0.78. Revision surgery performed in 7 patients (9%). Discussion: Acute fixation combined with THR for acetabular fractures in the elderly patient, offers good functional outcomes and a low complication rate in the mid-term.

AimsThe aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients.MethodsDemographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset.ResultsA total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752.ConclusionThis study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment.Cite this article: Bone Joint J 2023;105-B(6):702–710.