Introduction of the Cost-Effectiveness Studies of Fracture Liaison Service in Other Countries

Regional disparities in the risk of secondary fractures in patients with hip fractures

PURPOSE

We aimed to examine the regional disparities in secondary fracture incidence among patients with hip fractures in South Korea.

METHODS

This observational, retrospective, cohort study was conducted using data of 6,213 South Korean nationals from the National Health Insurance Service-National Sample Cohort (2004-2019). Secondary fractures included hip, wrist, humerus, spine, ankle, and pelvis fractures that occurred 6 months after hip fracture. The position value for relative composite index was used to identify medically vulnerable regions. Cox proportional hazards models were used for statistical analysis.

RESULTS

Among the 6,213 (1,949 male, 4,264 female) patients with hip fracture, 981 lived in medically vulnerable areas and 5,232 in non-vulnerable areas. Patients residing in vulnerable areas had a higher risk of secondary fractures than did those residing in non-vulnerable areas (hazard ratio [HR]: 1.24, 95 % confidence interval [CI]: 1.05-1.47); the factors that increased their risk included female sex (HR: 1.30, 95 % CI: 1.08-1.57), age ≥71 years (HR: 1.23, 95 % CI: 1.01-1.44), and not receiving osteoporosis medication (HR: 1.47, 95 % CI: 1.14-1.89). Ten years after hip fracture surgery, the risk of secondary fracture more than tripled in the vulnerable areas than that in the non-vulnerable areas.

CONCLUSION

Patients living in vulnerable regions had a higher risk of secondary fractures than that of those in non-vulnerable regions. Prevention and medication policies should thus be implemented to reduce regional healthcare disparities.

Dementia and Hip Fractures: A Comprehensive Review of Management Approaches

» The elderly population is the fastest growing demographic, and the number of dementia cases in the United States is expected to double to 10 million by 2050.» Patients with dementia are at 3× higher risk of hip fractures and have higher morbidity and mortality after hip fractures.» Hip fracture patients with dementia benefit from early analgesia and timely surgical fixation of fracture.» Early and intensive inpatient rehabilitation is associated with improved postoperative outcomes in patients with dementia.» Coordination of care within a "orthogeriatric" team decreases mortality, and fracture liaison services show potential for improving long-term outcomes in hip fracture patients with dementia.

The role of the fracture liaison service in the prevention of atypical femoral fractures

Osteoporosis and fragility fractures (FFs) are considered critical health problems by the World Health Organization (WHO) because of high morbidity, mortality, and healthcare costs. The occurrence of a FF raises the risk of a subsequent fracture (refracture). The hip is the most common site of fragility refracture, and its onset is associated with a further increase in patient's morbidity, mortality, and socioeconomic burden. Therefore, the prevention of refracture is essential. In this context, fracture liaison service (FLS) demonstrated to be able to reduce FF risk and also improve patients' adherence to anti-osteoporotic treatments, particularly for bisphosphonates (BPs). However, long-term and high adherence to BPs may lead to atypical femoral fractures (AFFs). These latter are tensile side stress fractures of the femur, with high rates of complications, including delayed and non-healing. An effective FLS should be able to prevent both FF and AFF. A comprehensive and interdisciplinary approach, through the involvement and education of a dedicated team of healthcare professionals (i.e. orthopedic, geriatrician, primary care physician, rehabilitation team, and bone nurse) for evaluating both FF and AFF risks might be useful to improve the standard of care.

Osteoporosis Feature Selection and Risk Prediction Model by Machine Learning Using a Cross-Sectional Database

BACKGROUND

The purpose of this study was to verify the accuracy and validity of using machine learning (ML) to select risk factors, to discriminate differences in feature selection by ML between men and women, and to develop predictive models for patients with osteoporosis in a big database.

METHODS

The data on 968 observed features from a total of 3,484 the Korea National Health and Nutrition Examination Survey participants were collected. To find preliminary features that were well-related to osteoporosis, logistic regression, random forest, gradient boosting, adaptive boosting, and support vector machine were used.

RESULTS

In osteoporosis feature selection by 5 ML models in this study, the most selected variables as risk factors in men and women were body mass index, monthly alcohol consumption, and dietary surveys. However, differences between men and women in osteoporosis feature selection by ML models were age, smoking, and blood glucose level. The receiver operating characteristic (ROC) analysis revealed that the area under the ROC curve for each ML model was not significantly different for either gender.

CONCLUSIONS

ML performed a feature selection of osteoporosis, considering hidden differences between men and women. The present study considers the preprocessing of input data and the feature selection process as well as the ML technique to be important factors for the accuracy of the osteoporosis prediction model.

Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review

BACKGROUND

Dual energy X-ray absorptiometry (DXA) is a preferred modality for screening or diagnosis of osteoporosis and can predict the risk of hip fracture. However, the DXA test is difficult to implement easily in some developing countries, and fractures have been observed before patients underwent DXA. The purpose of this systematic review is to search for studies that predict the risk of hip fracture using artificial intelligence (AI) or machine learning, organize the results of each study, and analyze the usefulness of this technology.

METHODS

The PubMed, OVID Medline, Cochrane Collaboration Library, Web of Science, EMBASE, and AHRQ databases were searched including "hip fractures" AND "artificial intelligence".

RESULTS

A total of 7 studies are included in this study. The total number of subjects included in the 7 studies was 330,099. There were 3 studies that included only women, and 4 studies included both men and women. One study conducted AI training after 1:1 matching between fractured and non-fractured patients. The area under the curve of AI prediction model for hip fracture risk was 0.39 to 0.96. The accuracy of AI prediction model for hip fracture risk was 70.26% to 90%.

CONCLUSIONS

We believe that predicting the risk of hip fracture by the AI model will help select patients with high fracture risk among osteoporosis patients. However, to apply the AI model to the prediction of hip fracture risk in clinical situations, it is necessary to identify the characteristics of the dataset and AI model and use it after performing appropriate validation.

Estimated Direct Medical Cost of Osteoporosis in Saudi Arabia: A Single-Center Retrospective Cost Analysis

Osteoporosis and its complications are a major health concern in Saudi Arabia, and the prevalence of osteoporosis is on the rise. The aim of this study was to estimate the direct healthcare cost for patients with osteoporosis. A retrospective study was carried out among adult patients with osteoporosis in a teaching hospital in Saudi Arabia. A bottom-up approach was conducted to estimate the healthcare resources used and the total direct medical cost for the treatment of osteoporosis and related fractures. The study included 511 osteoporosis patients, 93% of whom were female. The average (SD) age was 68.5 years (10.2). The total mean direct medical costs for patients without fractures were USD 975.77 per person per year (PPPY), and for those with osteoporotic fractures, the total direct costs were USD 9716.26 PPPY, of which 56% of the costs were attributable to surgery procedures. Prior to fractures, the main cost components were medication, representing 61%, and physician visits, representing 18%. The findings of this study indicated the economic impact of osteoporosis and related fractures. With the aging population in Saudi Arabia, the burden of disease could increase significantly, which highlights the need for effective prevention strategies to minimize the economic burden of osteoporosis.