Incidence Of New Hip Fractures Research Articles

Incidence of and risk factors for hip fracture in Nagasaki, Japan from 2005 to 2014

SummaryThe annual incidence of new hip fractures increased from 2005 to 2014 in Nagasaki and females were much more affected. High-risk factors were identified as age ≥ 80 years, winter, indoors, living room, Monday, and early morning. Seven days after admission, most patients remained hospitalized and had been treated surgically.IntroductionHip fractures are major osteoporotic fractures that reduce quality of life. In Japan, the incidence of hip fractures increased steadily from 1986 to 2014 and the number of hip fractures could be 7.3–21.3 million by 2050. This study aimed to determine the incidence of hip fractures from 2005 to 2014 in Nagasaki Prefecture and to analyze the characteristics of and risk factors for hip fracture.MethodsHip fractures that occurred in Nagasaki Prefecture between 2005 and 2014 were analyzed using emergency transportation records. Fracture type, age, sex, location in which fracture occurred, and risk factors for hip fracture were clarified.ResultsThe total number of new hip fractures among individuals ≥ 35 years old was 17,395 (mean age, 82.6 years old) and the annual incidence per 100,000 population increased from 147.9 in 2005 to 235.0 in 2014. Females (79.6%) were much more commonly affected than males (20.4%) and cervical fractures were more common than trochanteric fractures in all age groups. Hip fracture tended to be associated with age ≥ 80 years, winter rather than summer, indoors rather than outdoors, and living room rather than the bathroom or toilet. Other high-risk factors were Monday as day of the week, and early morning as the time of day. Seven days after admission, 97.3% of patients were hospitalized and 78.1% of hip fractures had been treated surgically.ConclusionInformation on actual situations and valid preventive measures relevant to hip fracture are urgently needed.

Bisphosphonates for Secondary Prevention of Osteoporotic Fractures: A Bayesian Network Meta-Analysis of Randomized Controlled Trials.

Purpose To investigate the comparative efficacies of the five most commonly used bisphosphonates for the secondary prevention of osteoporotic fractures in a Bayesian network meta-analysis. Methods Five databases and the reference lists of all acquired articles from inception to July 2017 were searched. A Bayesian random-effects model was employed, and vertebral, hip and nonvertebral nonhip fractures were assessed by odds ratios (ORs) and 95%credible intervals. Furthermore, with respect to each endpoint, rank probabilities for each bisphosphonate were evaluated using the surface under the cumulative ranking curve (SUCRA) value. Results Thirteen eligible studies were identified involving 11,822 patients with osteoporotic fractures. Overall in the pairwise meta-analyses, bisphosphonate use significantly reduced the risk of new vertebral, hip, and nonvertebral nonhip fractures, with ORs and 95% confidence intervals of 0.56 (0.49–0.64), 0.69 (0.48–0.98), and 0.82 (0.70–0.97), respectively. In network meta-analyses, significant differences were found between placebo and any one of the five bisphosphonates for new vertebral fractures. The rank probability plot and the SUCRA calculation results suggested that alendronate was the best intervention (14.6%) for secondary prevention of vertebral fractures, followed by zoledronate (15.3%) and etidronate (22.1%). In terms of the incidence of new hip fractures, alendronate was associated with the lowest incidence (18.5%), followed by zoledronate (43.1%) and risedronate (52.5%). However, zoledronate ranked lowest (16.6%) regarding the incidence of new nonvertebral nonhip fractures, followed by risedronate (23.8%) and alendronate (44.1%). Conclusions Bisphosphonates show significant efficacy for secondary prevention of new vertebral fractures, and alendronate is most likely to be successful at secondary prevention of vertebral and hip fractures compared with the other four bisphosphonates.

Prediction of hip fracture in post-menopausal women using artificial neural network approach.

Hip fracture is one of the most serious health problems among post-menopausal women with osteoporosis. It is very difficult to predict hip fracture, because it is affected by multiple risk factors. Existing statistical models for predicting hip fracture risk yield area under the receiver operating characteristic curve (AUC) ~0.7-0.85. In this study, we trained an artificial neural network (ANN) to predict hip fracture in one cohort, and validated its predictive performance in another cohort. The data for training and validation included age, bone mineral density (BMD), clinical factors, and lifestyle factors which had been obtained from a longitudinal study that involved 1167 women aged 60 years and above. The women had been followed up for up to 10 years, and during the period, the incidence of new hip fractures was ascertained. We applied feed-forward neural networks to learn from the data, and then used the learning for predicting hip fracture. Results of prediction showed that the accuracy of model I (which included only lumbar spine and femoral neck BMD) and model II (which included non-BMD factors) was 82% and 84%, respectively. When both BMD and non-BMD factors were combined (Model III), the accuracy increased to 87%. The AUC for model III was 0.94. These findings indicate that ANNs are able to predict hip fracture more accurately than any existing statistical models, and that ANNs can help stratify individuals for clinical management.