Aims. This study aimed to compare the performance of survival prediction models for bone metastases of the extremities (BM-E) with pathological fractures in an Asian cohort, and investigate patient characteristics associated with survival. Methods. This retrospective cohort study included 469 patients, who underwent surgery for BM-E between January 2009 and March 2022 at a tertiary hospital in South Korea. Postoperative survival was calculated using the PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models. Model performance was assessed with area under the curve (AUC), calibration curve, Brier score, and decision curve analysis. Cox regression analyses were performed to evaluate the factors contributing to survival. Results. The SORG model demonstrated the highest discriminatory accuracy with AUC (0.80 (95% confidence interval (CI) 0.76 to 0.85)) at 12 months. In calibration analysis, the PATHfx3.0 and OPTIModel models underestimated survival, while the SPRING13 and IOR models overestimated survival. The SORG model exhibited excellent calibration with intercepts of 0.10 (95% CI -0.13 to 0.33) at 12 months. The SORG model also had lower Brier scores than the null score at three and 12 months, indicating good overall performance. Decision curve analysis showed that all five survival prediction models provided greater net benefit than the default strategy of operating on either all or no patients. Rapid growth cancer and low serum albumin levels were associated with three-, six-, and 12-month survival. Conclusion. State-of-art survival prediction models for BM-E (PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models) are useful clinical tools for orthopaedic surgeons in the decision-making process for the treatment in Asian patients, with SORG models offering the best predictive performance. Rapid growth cancer and serum albumin level are independent, statistically significant factors contributing to survival following surgery of BM-E. Further refinement of survival prediction models will bring about informed and patient-specific treatment of BM-E. Cite this article: Bone Joint J 2024;106-B(2):203–211

Aims

The purpose of this study was to develop a prognostic model for predicting survival of patients undergoing surgery owing to metastatic bone disease (MBD) in the appendicular skeleton.

Aims. This study aims to assess first, whether mutations in the epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma (kRAS) genes are associated with overall survival (OS) in patients who present with symptomatic bone metastases from non-small cell lung cancer (NSCLC) and secondly, whether mutation status should be incorporated into prognostic models that are used when deciding on the appropriate palliative treatment for symptomatic bone metastases. Patients and Methods. We studied 139 patients with NSCLC treated between 2007 and 2014 for symptomatic bone metastases and whose mutation status was known. The association between mutation status and overall survival was analysed and the results applied to a recently published prognostic model to determine whether including the mutation status would improve its discriminatory power. Results. The median OS was 3.9 months (95% confidence interval (CI) 2.1 to 5.7). Patients with EGFR (15%) or kRAS mutations (34%) had a median OS of 17.3 months (95% CI 12.7 to 22.0) and 1.8 months (95% CI 1.0 to 2.7), respectively. Compared with EGFR-positive patients, EGFR-negative patients had a 2.5 times higher risk of death (95% CI 1.5 to 4.2). Incorporating EGFR mutation status in the prognostic model improved its discriminatory power. Conclusion. Survival prediction models for patients with symptomatic bone metastases are used to determine the most appropriate (surgical) treatment for painful or fractured lesions. This study shows that NSCLC should not be regarded as a single entity in such models. Cite this article: Bone Joint J 2017;99-B:516–21

Aims

The aim of this prospective cohort study was to evaluate the early migration of the TriFit cementless proximally coated tapered femoral stem using radiostereometric analysis (RSA).