Prediction of subsequent fragility fractures: application of machine learning.

BACKGROUND: Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients. METHODS: We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation. RESULTS: The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score. CONCLUSION: ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.

Bone metabolism factors in predicting the risk of osteoporosis fracture in the elderly.

OBJECTIVE: Osteoporosis (OS) is a systemic bone disease characterized by low bone mass and bone microstructure damage. This study. METHODS: According to the T value, 88 elderly fracture patients were grouped as the control group (without OS, 43 cases) and observation group (with T value <-2.5, which could be diagnosed as OS, 45 cases). The content of boney containing protein (BGP), total type 1 collagen amino terminal extender peptide (TPINP), ß-Crosslaps (ß-CTX), parathyroid hormone (PTH) and insulin-like growth factors-1 (IGF-1) was compared. Multivariate logistic regression was adopted to analyze the correlation between biochemical indexes and the occurrence of senile OS fracture and the related risk factors. The diagnostic value in the elderly was analyzed by receiver operating characteristic (ROC) curve. RESULTS: The levels of BGP, TPINP, ß-CTX, PTH and IGF-1 were elevated, and the level of IGF-1 was decreased in the observation group compared with the control group (P < 0.05). The elevated content of BGP, TPINP, ß-CTX and PTH, and the decreased expression of IGF-1 were influencing factors for OS fractures in the elderly (P < 0.05). The sensitivity and specificity to predict the occurrence of OS fractures in the elderly were 91.70% and 90.50%, respectively. The AUC of combined detection was 0.976 (95% CI: 0.952-1.000), which was memorably higher than single indicator detection (P < 0.05). Among 45 patients, 32 cases had good prognosis and 13 had poor prognosis. In comparison with the good prognosis group, the content of BGP, TPINP, ß-CTX and PTH were sensibly higher, the level of IGF-1 was prominently lower, and the proportion of fracture history was much higher in poor prognosis group (P < 0.05). Fracture history, BGP, TPINP, ß-CTX, PTH and IGF-1 were independent risk factors for poor prognosis of elderly OS fractures (P < 0.05). CONCLUSION: Bone metabolism factors were associated with poor prognosis of OS in the elderly. The combined detection had higher diagnostic value in calculating the risk of OS fracture in the elderly than single indicator detection.

Predicting osteoporotic fractures post-vertebroplasty: a machine learning approach with a web-based calculator.

PURPOSE: The aim of this study was to develop and validate a machine learning (ML) model for predicting the risk of new osteoporotic vertebral compression fracture (OVCF) in patients who underwent percutaneous vertebroplasty (PVP) and to create a user-friendly web-based calculator for clinical use. METHODS: A retrospective analysis of patients undergoing percutaneous vertebroplasty: A retrospective analysis of patients treated with PVP between June 2016 and June 2018 at Liuzhou People's Hospital was performed. The independent variables of the model were screened using Boruta and modelled using 9 algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (ROC_AUC), and clinical utility was assessed by clinical decision curve analysis (DCA). The best models were analysed for interpretability using SHapley Additive exPlanations (SHAP) and the models were deployed visually using a web calculator. RESULTS: Training and test groups were split using time. The SVM model performed best in both the training group tenfold cross-validation (CV) and validation group AUC, with an AUC of 0.77. DCA showed that the model was beneficial to patients in both the training and test sets. A network calculator developed based on the SHAP-based SVM model can be used for clinical risk assessment ( https://nicolazhang.shinyapps.io/refracture_shap/ ). CONCLUSIONS: The SVM-based ML model was effective in predicting the risk of new-onset OVCF after PVP, and the network calculator provides a practical tool for clinical decision-making. This study contributes to personalised care in spinal surgery.

The role of bone turnover markers in diagnosis, monitoring, and pathological fractures of osteoporosis.

BACKGROUND: We investigated the utility of specific biomarkers-namely, c-terminal telopeptide (CTX), n-telopeptide (NTX), deoxypyridinoline (DPD), and tartrate-resistant acid phosphatase (TRAP)-compared to conventional diagnostic methods. We hy-pothesized that these novel biomarkers could hold substantial value in the diagnosis, treatment, and monitoring of osteoporosis. METHODS: The study was conducted over a three-year period, from January 1, 2020, to January 1, 2023. We enrolled a total of 520 patients aged 50 years or older who had been diagnosed with osteoporosis. Patients undergoing steroid treatments, which are known to contribute to osteoporosis, were excluded from the study. Additionally, we carefully selected and matched a control group consisting of 500 patients based on demographic characteristics relevant to the diagnosis of osteoporosis. This meticulous selection process resulted in a comprehensive cohort comprising 1,020 patients. Throughout the study, patients were closely monitored for a duration of one year to track the occurrence of pathological fractures and assess their overall prognosis. RESULTS: As a result of our rigorous investigation, we identified CTX, NTX, DPD, and TRAP as pivotal biomarkers that play a crucial role in evaluating bone health, monitoring treatment effectiveness, and detecting pathological fractures in the context of osteoporosis. CONCLUSION: Our study underscores the significance of these biomarkers in advancing the diagnosis and management of osteo-porosis, offering valuable insights into the disease's progression and treatment outcomes.

[Fragility Fractures: Preoperative assessment and optimisation]. / Fragilitätsfraktur beim geriatrischen Patienten: Präoperative Abklärung und Optimierung.

Fragility fractures are associated with high morbidity and mortality. An interdisciplinary collaboration and an individualized, patient-centered approach are essential to ensure an optimized preoperative period and to improve perioperative safety. Preoperative responsibilities of trauma surgery include in the first step the identification of fragility fractures and the necessity for geriatric involvement. Orthogeriatric co-management (OCM) focuses on the identification of the medical, functional and social needs of the patient. In the preoperative period attention is focussed on acute diseases in need of treatment that have a negative impact on the course of further treatment and the prevention of delirium.

Deep learning in the radiologic diagnosis of osteoporosis: a literature review.

OBJECTIVE: Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. METHODS: We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. RESULTS: A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). CONCLUSIONS: Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

Three-year follow-up of a novel orthopedic ward fracture liaison services (OWFLS) model.

OBJECTIVE: We established an orthopedic ward fracture liaison services (OWFLS) model and evaluated its role in improving detection rates of bone metabolic markers, treatment rates, and long-term treatability. METHODS: This observational retrospective cohort study included 120 patients aged >50 years hospitalized for primary osteoporotic fracture from January 2018 to January 2019 (group A: not included in OWFLS). Group B (included in OWFLS) comprised 120 patients from February 2019 to February 2020. We compared rates of bone metabolic index testing, treatment, and adherence; symptomatic improvement; and recurrent fracture between groups. RESULTS: Rates of bone metabolism index testing (50% vs. 0%) and medication use (94.2% vs. 64.2%) were significantly higher after OWFLS implementation. There was no significant difference in adherence rates at 3 months between groups (97.3% vs. 93.5%). Adherence rates at 1 and 3 years were better in group B than A (73.5% vs. 51.9%; 57.5% vs. 26%, respectively). Recurrence of bone pain at 1 and 3 years was significantly lower in group B than A (20.4% vs. 46.8%; 45.1% vs. 76.6%, respectively). CONCLUSIONS: OWFLS improved the detection rate of bone metabolism indicators, treatment rate, and patient adherence and reduced recurrence of bone pain. OWFLS may be suitable for settings lacking human resources.

[Update on the DVO Guideline 2023 "Prophylaxis, diagnosis and treatment of osteoporosis in postmenopausal women and in men aged over 50"-What's new for rheumatology?] / Update DVO-Leitlinie 2023 "Prophylaxe, Diagnostik und Therapie der Osteoporose bei postmenopausalen Frauen und bei Männern ab dem 50. Lebensjahr" ­ Was ist neu für die Rheumatologie?

In October 2023, the organization of the German-speaking scientific osteological societies (DVO) published the revised guideline on the "Prophylaxis, diagnosis and treatment of osteoporosis in postmenopausal women and in men aged over 50." This review article reflects the new features of the guideline and their relevance in the care of patients with inflammatory rheumatic diseases.A key innovation is the change from the 10-year fracture risk to the 3­year fracture risk. Basic diagnostics are currently performed without a defined fracture threshold. Treatment thresholds for specific osteological therapy constitute another key innovation, defined as 3% to < 5%, 5% to < 10%, and from 10% for vertebral body and femoral neck fractures. If the 3­year fracture risk is > 10%, osteoanabolic therapy should primarily be carried out and antiresorptive therapy is initiated following osteoanabolic therapy. In addition, patients with osteoporosis and prolonged glucocorticoid therapy should primarily be treated osteoanabolically with teriparatide. In summary, the changes to the DVO guideline reflect the latest scientific study findings in osteology and lead to detailed differential therapy for osteoporosis.

[Expert consensus on integrated traditional Chinese and western medicine diagnosis and treatment for osteoporotic fractures].

Osteoporotic fractures represent the most severe complications of osteoporosis,characterized by insidious onset,high mortality and disability rates,and a steadily increasing incidence,imposing a significant socioeconomic burden. Western medicine has advantages in diagnosis and surgical interventions,while traditional Chinese medicine excels in holistic management and the restoration of bodily equilibrium. The integration of both traditional Chinese medicine (TCM) and western medicine emerges as an effective therapeutic strategy for osteoporotic fractures. In order to propagate the concept of integrated diagnosis and treatment,foster the advancement of integrated medical techniques for osteoporotic fractures,and establish standardized and normative protocols for disease prevention,diagnosis,and treatment,a consensus expert group,led by Geriatric Branch of Chinese Geriatrics Society,the Young Osteoporosis Group of Orthopedics Branch of Chinese Medical Association,Osteoporosis Group of Orthopedics Branch of Chinese Physician Association,and Osteoporosis Professional Committee of the Shanghai Society of Integrated Traditional Chinese and Western Medicine,was established. This group engaged in deliberations and formulated the "Expert Consensus on Integrated Traditional Chinese and Western Medicine Diagnosis and Treatment of Osteoporotic Fractures" elucidating the concept of integrated medicine and offering recommendations in the domains of prevention,diagnosis,and treatment,with the aspiration of ameliorating the prognosis of osteoporotic fractures and enhancing the quality of life for these patients.

[Secondary fracture prevention/Update osteoporosis guidelines 2023 of the Umbrella Organization Osteology]. / Sekundäre Frakturprävention/Update Leitlinie Osteoporose 2023 des Dachverbandes Osteologie.

The S3 guidelines on the prophylaxis, diagnostics and treatment of osteoporosis 2023 were completely revised and updated between 2021 and 2023 in accordance with the Association of the Scientific Medical Societies of Germany (AWMF) regulations. The guideline committee consisted of delegates from the 20 specialist societies of the Umbrella Organization Osteology (Dachverband Osteologie, DVO) as well as delegates from the German Society of General Medicine and Family Medicine (DEGAM), the German Society for Nephrology (DGfN) and the Federal Self-help Association for Osteoporosis (BfO).The guidelines focus on preventive measures, diagnostic procedures and treatment approaches for osteoporosis in men aged 50 years and over and postmenopausal women. The main aim is the optimization of care processes, reduction of fracture incidences and maintenance or improvement of the quality of life and functional capacity of patients affected by fractures. A major update to the guidelines includes the introduction of a new risk calculator that can take more risk factors (n = 33) into account and that can estimate the risk of vertebral body and proximal femoral fractures for a 3-year period (previously 10 years). This results in new thresholds for diagnostics and treatment. The programmed app is currently not yet certified as a medical product and a paper version is therefore currently available for patient care with the planned integration of a web-based version of the risk calculator. From the perspective of trauma surgery, the recommendations and innovations for manifest osteoporosis are of particular clinical importance. The focus of the DVO guidelines update is therefore on the implementation of secondary fracture prevention in trauma surgery, orthopedic and geriatric traumatology in the clinical and practical daily routine.

Analyzing the risk of osteoporosis and fracture in rheumatoid arthritis patients who have been treated with various biologics.

BACKGROUND: Rheumatoid arthritis (RA) is a major risk factor for osteoporosis/osteoporotic fractures. We aimed to elucidate the role of treatment choices among osteoporosis/osteoporotic fractures. METHODOLOGY: We utilized the Chang-Gung Research Database to assess the risks of osteoporosis/osteoporotic fractures among independently treated RA patients, using retrospective time-to-event outcomes analysis. RESULTS: A total of 3509 RA patients with a mean of 63.1 ± 8.6 years were analyzed. Among all, 1300 RA patients (37%) were diagnosed with newly diagnosed osteoporosis. The crude incidence of newly diagnosed osteoporosis was the highest among those treated with other conventional disease-modifying anti-rheumatic drugs (cDMARDs; 74.1 events/1000-PYs, 95%CI 66.0-82.3), followed by those with a non-treatment period (68 events/1000-PYs, 95%CI 63.1-72.9), methotrxate (MTX) monotherapy (60.7 events/1000-PYs, 95%CI 41.2-80.3), MTX plus other cDMARDs (51.9 events/1000-PYs, 95%CI 43.4-60.3), and abatacept/rituximab (48.6 events/1000-PYs, 95%CI 14.9-82.3). The lowest crude incidence was found in patients treated with anti-TNFi biologics (40.4 events/1000-PYs, 95%CI 28.6-52.2) and other biologic disease-modifying anti-rheumatic drugs (bDMARDs; 40.1 events/1000-PYs, 95%CI 8.0-72.1). A total of 270 patients (20.8%) suffered from an incident fracture during follow-ups. The crude incidence of fracture was the highest among those treated with abatacept/rituximab (49.0 events/1000-PYs, 95%CI 6.0-91.9), followed by those with non-treatment periods (24.3 events/1000-PYs, 95%CI 19.3-29.4), other cDMARDs (24.2 events/1000-PYs, 95%CI 18.1-30.2), anti-TNFi biologics (20.2 events/1000-PYs, 95%CI 8.8-31.6). Other bDMARDs (13.3 events/1000-PYs, 95%CI 0-39.2), MTX mono (12.5 events/1000-PYs, 95%CI 0.3-24.8), and MTX plus other cDMARDs (11.4 events/1000-PYs, 95%CI 5.4-17.4) were low incidences. CONCLUSION: The treatment option has emerged as a critical determinant in the context of future osteoporosis and osteoporotic fracture risks among RA. These findings offer a valuable resource for clinicians, empowering them to tailor bespoke treatment strategies for RA patients, thereby mitigating the potential for future osteoporosis and fractures.

The relationship between vertebral compression rates and systemic inflammatory scores in osteoporotic vertebral fractures.

OBJECTIVE: There are many scientific reports on systemic inflammation scores (SIS) associated with decreased bone mineral density in osteoporotic vertebral disease. However, there are no studies on the association of inflammation scores with the risk of collapse in osteoporotic vertebral collapse fractures. The aim of this study was to examine the correlation between the product of platelet and neutrophil counts (PPN), platelet/lymphocyte ratio (PLR), neutrophil/lymphocyte ratio (NLR), and systemic immune inflammation index (SII) derived from complete blood count analysis in cases of osteoporotic vertebral fractures and fracture severity based on vertebral collapse rates. PATIENTS AND METHODS: This study is a retrospective analysis of a cohort of 50 patients aged 50 years or older who presented with osteoporotic vertebral fractures and underwent kyphoplasty at our clinic from 2018 to 2023. The study included both men and women. Computed tomography (CT) and magnetic resonance imaging (MRI) were used to diagnose and differentiate osteoporotic vertebral compression fractures from burst fractures and pathologic fractures. All compression rate measurements were performed with CT. The compression rate of the most affected vertebra (MAV-CR) was calculated. Groups were divided into two categories based on their compression rates: <50% and ≥50%. Initial PPN, PLR, NLR, and SII parameters were used as systemic inflammation scores. RESULTS: No statistically significant differences were found between MAV-CR groups in PPN, PLR, NLR, and SII parameters (p>0.05). No statistically significant correlation was observed between inflammation scores and MAV-CR groups (p>0.05). In this comparison, no significant difference was observed between the selected CBC parameters and the groups divided according to the compression rate (WBC: p=0.725, PC: p=0.069, NC: p=0.732, LC: p=0.513). ROC analysis was performed to analyze the diagnostic tests (AUC=0.372 for PPN, AUC=0.509 for PLR, AUC=0.525 for NLR, and AUC=0.435 for SII). None of the systemic inflammation scores had any predictive value for osteoporotic vertebral collapse fractures. CONCLUSIONS: Although it has been established in the scientific literature that systemic inflammation scores are associated with osteoporotic vertebral fractures, our analysis indicates no statistically significant correlation between the parameters of PPN, PLR, NLR, and SII and the severity of compression fractures in individuals with osteoporotic vertebral fractures. In this study, using systemic inflammation scores as a predictive test for the severity of osteoporotic vertebral fractures does not seem appropriate.

Clinical Use of Trabecular Bone Score: The 2023 ISCD Official Positions.

Osteoporosis can currently be diagnosed by applying the WHO classification to bone mineral density (BMD) assessed by dual-energy x-ray absorptiometry (DXA). However, skeletal factors other than BMD contribute to bone strength and fracture risk. Lumbar spine TBS, a grey-level texture measure which is derived from DXA images has been extensively studied, enhances fracture prediction independent of BMD and can be used to adjust fracture probability from FRAX® to improve risk stratification. The purpose of this International Society for Clinical Densitometry task force was to review the existing evidence and develop recommendations to assist clinicians regarding when and how to perform, report and utilize TBS. Our review concluded that TBS is most likely to alter clinical management in patients aged ≥ 40 years who are close to the pharmacologic intervention threshold by FRAX. The TBS value from L1-L4 vertebral levels, without vertebral exclusions, should be used to calculate adjusted FRAX probabilities. L1-L4 vertebral levels can be used in the presence of degenerative changes and lumbar compression fractures. It is recommended not to report TBS if extreme structural or pathological artifacts are present. Monitoring and reporting TBS change is unlikely to be helpful with the current version of the TBS algorithm. The next version of TBS software will include an adjustment based upon directly measured tissue thickness. This is expected to improve performance and address some of the technical factors that affect the current algorithm which may require modifications to these Official Positions as experience is acquired with this new algorithm.

An overview of the use of the fracture risk assessment tool (FRAX) in osteoporosis.

FRAX®, a simple-to-use fracture risk calculator, was first released in 2008 and since then has been used increasingly worldwide. By calculating the 10-year probabilities of a major osteoporotic fracture and hip fracture, it assists clinicians when deciding whether further investigation, for example a bone mineral density measurement (BMD), and/or treatment is needed to prevent future fractures. In this review, we explore the literature around osteoporosis and how FRAX has changed its management. We present the characteristics of this tool and describe the use of thresholds (diagnostic and therapeutic). We also present arguments as to why screening with FRAX should be considered. FRAX has several limitations which are described in this review. This review coincides with the release of a version, FRAXplus, which addresses some of these limitations.

Assessment for bone health in patients with differentiated thyroid carcinoma after postoperative thyroid-stimulating hormone suppression therapy: a new fracture risk assessment algorithm.

Purpose: The fracture risk assessment tool (FRAX) is used to assess the 10-year risk of major site and hip fractures; however, whether this tool can be applied to patients receiving levothyroxine-based thyroid-stimulating hormone (TSH) suppressive therapy for postoperative differentiated thyroid cancer (DTC) patients is yet to be clarified. Methods and design: A total of 64 patients with DTC following thyroidectomy and oral levothyroxine for TSH suppression therapy and 30 gender- and age-matched controls were collected. The fracture risk was compared between the affected groups with different TSH levels. FRAX was used to calculate the fracture risk with and without bone mineral density (BMD). The TSH level was converted to an age-weighted score to estimate the fracture risk of postoperatively differentiated thyroid cancer patients. The sensitivity, specificity, and area under the AUC curve of the traditional FRAX and the new algorithm for osteoporosis diagnosis were compared. The dual-energy X-ray bone mineral density measurement T score was used as the gold standard to diagnose osteoporosis. Results: There were 24 patients in the T ≥ -1-2.5 group, 23 in the -2.5 < T < -1 group, and 17 in the T ≤ -2.5 group. The T score of BMD in the disease group was significantly lower than that in the control group (p < 0.05). The risk of MOF and hip fracture without a T score were significantly different under various TSH levels (p < 0.05). The area under the curve (AUC) of FRAX without BMD for predicting major osteoporotic fractures (PMOF) and major hip fractures (PHF) was 0.694 and 0.683, respectively. The cutoff values were 2.15% and 0.25%, respectively. The AUC of FRAX with BMD for PMOF and PHF was 0.976 and 0.989, respectively, and the cutoff values were 4.15% and 1.1%, respectively. The AUC of FRAX without BMD for PMOF and PHF was 0.708 and 0.72, respectively, and the cutoff values were 5.5% and 1.55%, respectively. Conclusions: FRAX is suitable for postoperative DTC patients after TSH suppressive therapy. In the absence of BMD, TSH weighted by age can improve the specificity of FRAX in the diagnosis of osteoporosis in this population.

AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review.

Osteoporotic fractures impose a substantial burden on patients with diabetes due to their unique characteristics in bone metabolism, limiting the efficacy of conventional fracture prediction tools. Artificial intelligence (AI) algorithms have shown great promise in predicting osteoporotic fractures. This review aims to evaluate the application of traditional fracture prediction tools (FRAX, QFracture, and Garvan FRC) in patients with diabetes and osteoporosis, review AI-based fracture prediction achievements, and assess the potential efficiency of AI algorithms in this population. This comprehensive literature search was conducted in Pubmed and Web of Science. We found that conventional prediction tools exhibit limited accuracy in predicting fractures in patients with diabetes and osteoporosis due to their distinct bone metabolism characteristics. Conversely, AI algorithms show remarkable potential in enhancing predictive precision and improving patient outcomes. However, the utilization of AI algorithms for predicting osteoporotic fractures in diabetic patients is still in its nascent phase, further research is required to validate their efficacy and assess the potential advantages of their application in clinical practice.

A simple fragility fracture risk score for type 2 diabetes patients: a derivation, validation, comparison, and risk stratification study.

OBJECTIVES: The aims of this study were to develop and validate 2 simple scores for stratification of the risks of (1) any fragility (AF) and (2) major osteoporotic fracture (MOF) in type 2 diabetes (T2D) patients; we also compared the performance of these scores with that of the Fracture Risk Assessment Tool (FRAX) and its adjustments. DESIGN AND METHODS: In this longitudinal cohort study, 1855 patients with T2D were enrolled from January 2015 to August 2019. Cox proportional hazard regression was used to model the 5-year risk of AF and MOF. These scores were internally validated using a bootstrap resampling method of 1000. RESULTS: During a median follow-up of 5 years, 119 (6.42%) cases of AF and 92 (4.96%) cases of MOFs were identified. Both the concordance index (C-index) and calibration plots indicated improved identification performance using the newly established scores. Furthermore, these scores also showed improved outcomes regarding the decision curve analysis (DCA) and area under the curve (AUC) compared to the widely used FRAX and its derivatives. More importantly, these scores successfully separated T2D patients into risk groups according to significant differences in fracture incidence. CONCLUSIONS: These novel scores enable simple and reliable fracture risk stratification in T2D patients. Future work is needed to validate these findings in external cohort(s).

Effect of bedside health education for elderly patients with fragility fracture by specialist physicians on the diagnosis and treatment of osteoporosis during hospitalization and the visiting rate to osteoporosis clinic after discharge in a high-volume orthopedic hospital.

We retrospectively analyzed 12,999 elderly patients with fragility fracture and found that the detection rate of bone mineral density (BMD) and bone turnover markers (BTMs), the treatment rate of osteoporosis, and the visiting rate to the osteoporosis specialist clinic after discharge are significantly enhanced in fragility fracture patients after receiving health education on osteoporosis-related knowledge during hospitalization. PURPOSE: To observe the effect of health education on the diagnosis and treatment of osteoporosis during hospitalization and the rate of come back to osteoporosis clinic after discharge in elderly patients with fragility fracture. METHODS: A retrospective analysis was performed on 12,999 elderly patients with fragility fracture admitted to Xi'an Honghui Hospital from March 2021 to December 2022. The patients were divided into the health education group and the non-health education group according to whether they received health education on osteoporosis-related knowledge during hospitalization. The diagnosis and treatment of osteoporosis during hospitalization and the outpatient treatment of osteoporosis after discharge were compared between the two groups. RESULTS: Among the 7784 patients in the health education group, 4551 (58.47%) received BMD test, 798 (10.25%) received BTMs test, 3990 (51.26%) received anti-osteoporosis medications (AOMs) treatment, and 1232 (15.83%) came back to the osteoporosis specialist clinic after discharge. Among the 5215 patients in the non-health education group, 681 (13.06%) received BMD test, 6 (0.12%) received BTMs test, 2071 (39.71%) received AOMs treatment, and 440 (8.44%) came back to the osteoporosis specialist clinic within one month after discharge. CONCLUSION: The education of osteoporosis-related knowledge for patients with fragility fracture contribute to enhance the detection rate of BMD and BTMs and the treatment rate of osteoporosis during hospitalization, and increase the rate of coming back to the osteoporosis clinic after discharge.

Age-dependent gender differences in the diagnosis and treatment of osteoporosis during hospitalization in patients with fragility fractures.

BACKGROUND: There is a gender difference in the acceptance of osteoporosis diagnosis and treatment in patients after fragility fractures, but this difference is rarely assessed during hospitalization, and it is unclear whether these differences are age-dependent. This study aimed to evaluate the differences between male and female fragility fracture patients of different age groups who received the diagnosis and treatment of osteoporosis during hospitalization. METHODS: 31,265 fragility fracture patients aged ≥ 50 years from the Fragility Fracture Management Database in a high-volume orthopedic hospital from December 2019 to February 2023 were included in this study. We compared the differences in the rates of men and women with fragility fracture who received the measurement of bone mineral density (BMD) and bone metabolism biochemical markers (BMBMs) and treatment with anti-osteoporosis medications (AOMs), and follow-up to the internal medicine clinic within 3 months after discharge, across all age groups and across different age stages (50-59, 60-69, 70-79, and ≥ 80 years). RESULTS: The detection rates of female patients receiving BMD and BMBMs during hospitalization were 31.88% and 5.30%, respectively, compared with 22.23% and 2.69% for men. The rate of receiving any AOMs treatment was 44.63% for women and 31.60% for men. The follow-up rate of returning to the internal medicine clinic within 3 months after discharge was 9.79% for women compared to 3.00% for men. There was a significant difference between males compared to females (P < 0.0001). Analysis of patients by different age group revealed that differences in the diagnosis and treatment of osteoporosis were found only in patients under 80 years of age, while gender differences in the return to the internal medicine clinic for follow-up after discharge were present in all age groups. CONCLUSIONS: Gender differences present in osteoporosis management in patients with fragility fracture during hospitalization, especially for patients under 80 years of age. This finding suggests that orthopedic surgeons neglect to manage osteoporosis in male patients with fragility fracture during hospitalization.