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Proximal femur anatomy and bone mineral density vary widely among individuals, precluding the use of one predefined finite element (FE) model to determine the stress field for all proximal femurs. This variability poses a challenge in current prosthetic hip design approach. Given the numerous options for generating computed tomography (CT)‐based FE models, selecting the best methods for defining the mechanical behavior of the proximal femur is difficult. In this study, a combination of computational and experimental approaches was used to explore the susceptibility of the predicted stress field of the proximal femur to different combinations of density–elasticity relationships, element type, element size, and calibration error. Our results suggest that FE models with first‐order voxelized elements generated by the Keyak and Falkinstein density–elasticity relationship or quadratic tetrahedral elements generated by the Morgan density–elasticity relationship lead to accurate estimations of the mechanical behavior of human femurs. Other combinations of element size, element type, and mathematical relationships produce less accurate results, especially in the cortical bone of the femoral neck and calcar region. The voxelized model was more susceptible to variation of element size and density–elasticity relationships than FE models with quadratic tetrahedral elements. Regardless of element type, the stress fields predicted by the Keyak and Falkinstein and the Morgan relationships were the most robust to calibration error when deriving material density from CT‐generated Hounsfield data. These results provide insight into the implementation of a robust platform for designing patient‐specific implants capable of maintaining or modifying the stress in bones. [ABSTRACT FROM AUTHOR]

Background: Stratification of the fracture risk is an important treatment component for patients with multiple myeloma, which is associated with up to an 80% risk of pathologic fracture. The Mirels score, which is commonly used to estimate the fracture risk for patients with osseous lesions, was evaluated in a cohort in which fewer than 15% of lesions were caused by multiple myeloma. The behavior of multiple myeloma lesions often differs from that of lesions caused by metastatic disease, and accurate risk stratification is critical for effective care. To our knowledge, the Mirels score has not been validated specifically for multiple myeloma.Questions/purposes: Our purpose was: (1) To develop a novel scoring system for the prediction of pathologic fracture in patients with long-bone lesions from multiple myeloma; and (2) to compare the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic (ROC) area under curve (AUC) between the novel scoring system and the Mirels system.Methods: Between 2003 and 2017, 763 patients at one center with the diagnosis of multiple myeloma were reviewed, of whom 174 presented with long-bone disease involvement. Of those, 5% (nine of 174) were missing data or radiographs at a minimum of 1 year and had not reached an endpoint (fracture or surgery) before that time and were therefore excluded. Many patients have more than one lesion; consequently, we used the largest lesion in each patient, resulting in 163 lesions in as many patients. Ten percent (16 of 163) of these patients eventually developed a fracture and 4% (six of 163) underwent prophylactic stabilization (excluded from analysis because of outcome uncertainty). During the study period, prophylactic stabilization was performed at the discretion of the orthopaedic oncologist. Fifty-one percent (83 of 163) of patients were female, and the mean (± SD) age was 60 ± 10 years at radiographic lesion identification. All lesions were characterized before determining whether the patient underwent pathologic fracture. We identified variables associated with pathologic fracture on univariate analysis. Variables independently significant on logistic regression analysis were used to generate scoring algorithms at varying weights and scoring cutoffs for comparison via ROC curves. We then selected a novel score based on ROC performance, and compared the sensitivity, specificity, PPV, and NPV of that scoring system to that of Mirels score. ROC AUCs were compared after bootstrapping 100,000 iterations. Alpha was set at 0.05.Results: After controlling for potential confounders, such as age, sex, and duration of myeloma diagnosis, we found the following factors were independently associated with the occurrence of pathologic fracture: larger lesion size (area, cm2) (log odds 0.17; p = 0.03), longer lesion latency (years from diagnosis to lesion identification) (log odds 0.25; p = 0.03), presence of pain (relative risk [RR] 2.9; p = 0.04), and metaphyseal location (RR 3.2, compared with epiphyseal or diaphyseal; p = 0.003). These variables were used to formulate a novel scoring system. Compared with the Mirels system, the novel system was more sensitive (69% [95% CI 61 to 76] versus 38% [95% CI 30 to 46]; p < 0.05) but not different in terms of specificity (87% [95% CI 80 to 91] versus 87% [95% CI 81 to 92]; p > 0.05), PPV (37% [95% CI 29 to 45] versus 25% [95% CI 19 to 33]; p > 0.05), NPV (96% [95% CI 91 to 99] versus 92% [95% CI 87 to 96]; p > 0.05), or AUC (0.85 [95% CI 0.74 to 0.92] versus 0.67 [95% CI 0.51 to 0.81]; p > 0.05).Conclusion: The novel scoring system was found to be more sensitive than the Mirels system for predicting pathologic fracture in our retrospective cohort of patients with multiple myeloma-related bone disease. Specificity, PPV, NPV, and ROC AUC were not different with the numbers available. Thus, the novel scoring system may serve as a more effective screening tool to determine which patients with multiple myeloma would benefit from further radiologic or orthopaedic evaluation based on a skeletal survey.Level Of Evidence: Level III, diagnostic study. [ABSTRACT FROM AUTHOR]

Background: Surgical fixation of hip fractures within 24-48 hours of hospital presentation is associated with decreased rates of postoperative morbidity and death, and recently, hospitals nationwide have implemented strategies to expedite surgery. Our aim was to describe how time-to-surgery and short-term complication rates have changed using the National Surgical Quality Improvement Program database from 2011 to 2017., Methods: We identified more than 73,000 patients aged ≥65 years who underwent surgical fixation. Poisson regression adjusting for comorbidities, surgery type, type of anesthesia, patient sex, and patient age was performed to quantify annual changes in time-to-surgery. Annual changes in 30-day postoperative complications were analyzed using a generalized linear model with binomial distribution., Results: A significant decrease in time-to-surgery was observed during the study period (mean 30 hours in 2011 versus 26 hours in 2017; P<0.001 ). Time-to-surgery decreased by 2% annually during the 7-year period (0.5 hour/year, 95% CI: -35, -23; P<0.001 ). The all-cause 30-day complication rate also decreased annually (annual risk difference: -0.35%, 95% CI: -0.50%, -0.20%; P<0.001 ). For individual complications, we found significant decreases in deep infection (-0.2%, P=0.002 ), reintubation (-0.3%, P=0.001 ), urinary tract infection (-2.5%, P<0.001 ), and death (-1.3%, P=0.03 ). We found significant but small increases of pulmonary embolism (0.3%, P=0.03 ) and myocardial infarction (0.1%, P=0.02 ). Higher rates of complications were associated with increased time-to-surgery ( P<0.001 )., Conclusion: From 2011 to 2017, time-to-surgery for hip fracture decreased significantly, as did short-term postoperative rates of all-cause complications and death. Longer time-to-surgery was associated with increased number of complications.