Advertisement for orthosearch.org.uk
Results 1 - 3 of 3
Results per page:
Bone & Joint Research
Vol. 7, Issue 6 | Pages 430 - 439
1 Jun 2018
Eggermont F Derikx LC Verdonschot N van der Geest ICM de Jong MAA Snyers A van der Linden YM Tanck E

Objectives. In this prospective cohort study, we investigated whether patient-specific finite element (FE) models can identify patients at risk of a pathological femoral fracture resulting from metastatic bone disease, and compared these FE predictions with clinical assessments by experienced clinicians. Methods. A total of 39 patients with non-fractured femoral metastatic lesions who were irradiated for pain were included from three radiotherapy institutes. During follow-up, nine pathological fractures occurred in seven patients. Quantitative CT-based FE models were generated for all patients. Femoral failure load was calculated and compared between the fractured and non-fractured femurs. Due to inter-scanner differences, patients were analyzed separately for the three institutes. In addition, the FE-based predictions were compared with fracture risk assessments by experienced clinicians. Results. In institute 1, median failure load was significantly lower for patients who sustained a fracture than for patients with no fractures. In institutes 2 and 3, the number of patients with a fracture was too low to make a clear distinction. Fracture locations were well predicted by the FE model when compared with post-fracture radiographs. The FE model was more accurate in identifying patients with a high fracture risk compared with experienced clinicians, with a sensitivity of 89% versus 0% to 33% for clinical assessments. Specificity was 79% for the FE models versus 84% to 95% for clinical assessments. Conclusion. FE models can be a valuable tool to improve clinical fracture risk predictions in metastatic bone disease. Future work in a larger patient population should confirm the higher predictive power of FE models compared with current clinical guidelines. Cite this article: F. Eggermont, L. C. Derikx, N. Verdonschot, I. C. M. van der Geest, M. A. A. de Jong, A. Snyers, Y. M. van der Linden, E. Tanck. Can patient-specific finite element models better predict fractures in metastatic bone disease than experienced clinicians? Towards computational modelling in daily clinical practice. Bone Joint Res 2018;7:430–439. DOI: 10.1302/2046-3758.76.BJR-2017-0325.R2


Bone & Joint Research
Vol. 12, Issue 7 | Pages 412 - 422
4 Jul 2023
Ferguson J Bourget-Murray J Hotchen AJ Stubbs D McNally M

Aims

Dead-space management, following dead bone resection, is an important element of successful chronic osteomyelitis treatment. This study compared two different biodegradable antibiotic carriers used for dead-space management, and reviewed clinical and radiological outcomes. All cases underwent single-stage surgery and had a minimum one-year follow-up.

Methods

A total of 179 patients received preformed calcium sulphate pellets containing 4% tobramycin (Group OT), and 180 patients had an injectable calcium sulphate/nanocrystalline hydroxyapatite ceramic containing gentamicin (Group CG). Outcome measures were infection recurrence, wound leakage, and subsequent fracture involving the treated segment. Bone-void filling was assessed radiologically at a minimum of six months post-surgery.


Bone & Joint Research
Vol. 9, Issue 3 | Pages 139 - 145
1 Mar 2020
Guebeli A Platz EA Paller CJ McGlynn KA Rohrmann S

Aims

To examine the relationship of sex steroid hormones with osteopenia in a nationally representative sample of men in the USA.

Methods

Data on bone mineral density (BMD), serum sex hormones, dairy consumption, smoking status, and body composition were available for 806 adult male participants of the cross-sectional National Health and Nutrition Examination Survey (NHANES, 1999-2004). We estimated associations between quartiles of total and estimated free oestradiol (E2) and testosterone (T) and osteopenia (defined as 1 to 2.5 SD below the mean BMD for healthy 20- to 29-year-old men) by applying sampling weights and using multivariate-adjusted logistic regression. We then estimated the association between serum hormone concentrations and osteopenia by percentage of body fat, frequency of dairy intake, cigarette smoking status, age, and race/ethnicity.