Perspectives on Fracture Liaison Service in Austria: clinical and economic considerations.

Osteoporosis is a widespread disease and affects over 500,000 people in Austria. Fragility fractures are associated with it and represent not only an individual problem for the patients, but also an enormous burden for the healthcare system. While trauma surgery care is well provided in Vienna, there is an enormous treatment gap in secondary prevention after osteoporotic fracture. Systematic approaches such as the Fracture Liaison Service (FLS) aim to identify patients with osteoporosis after fracture, to clarify diagnostically, to initiate specific therapy, and to check therapy adherence. The aim of this article is to describe the practical implementation and operational flow of an already established FLS in Vienna. This includes the identification of potential FLS inpatients, the diagnostic workup, and recommendations for an IT solution for baseline assessment and follow-up of FLS patients. We summarize the concept, benefits, and limitations of FLS and provide prospective as well as clinical and economic considerations for a city-wide FLS, managed from a central location. Future concepts of FLS should include artificial intelligence for vertebral fracture detection and simple IT tools for the implementation of FLS in the outpatient sector.

Development and validation of the osteoporosis scale among the system of quality of life instruments for chronic diseases QLICD-OS (V2.0).

BACKGROUND: Quality of life of osteoporosis patients had caused widespread concern, due to high incidence and difficulty to cure. Scale specifics for osteoporosis and suitable for Chinese cultural background lacked. This study aimed to develop an osteoporosis scale in Quality of Life Instruments for Chronic Diseases system, namely QLICD-OS (V2.0). METHODS: Procedural decision-making approach of nominal group, focus group and modular approach were adopted. Our scale was developed based on experience of establishing scales at home and abroad. In this study, Quality of life measurements were performed on 127 osteoporosis patients before and after treatment to evaluate the psychometric properties. Validity was evaluated by qualitative analysis, item-domain correlation analysis, multi-scaling analysis and factor analysis; the SF-36 scale was used as criterion to carry out correlation analysis for criterion-related validity. The reliability was evaluated by the internal consistency coefficients Cronbach's α, test-retest reliability Pearson correlation r. Paired t-tests were performed on data of ​​the scale before and after treatment, with Standardized Response Mean (SRM) being calculated to evaluate the responsiveness. RESULTS: The QLICD-OS, composed of a general module (28 items) and an osteoporosis-specific module (14 items), had good content validity. Correlation analysis and factor analysis confirmed the construct, with the item having a strong correlation (most > 0.40) with its own domains/principle components, and a weak correlation (< 0.40) with other domains/principle components. Correlation coefficient between the similar domains of QLICD-OS and SF-36 showed reasonable criterion-related validity, with all coefficients r being greater than 0.40 exception of physical function of SF-36 and physical domain of QLICD-OS (0.24). Internal consistency reliability of QLICD-OS in all domains was greater than 0.7 except the specific module. The test-retest reliability coefficients (Pearson r) in all domains and overall score are higher than 0.80. Score changes after treatment were statistically significant, with SRM ranging from 0.35 to 0.79, indicating that QLICD-OS could be rated as medium responsiveness. CONCLUSION: As the first osteoporosis-specific quality of life scale developed by the modular approach in China, the QLICD-OS showed good reliability, validity and medium responsiveness, and could be used to measure quality of life in osteoporosis patients.

Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

[Osteoporosis - implications of the new guidelines in practice]. / Osteoporose ­ Implikation der neuen Leitlinie in die Praxis.

In September 2023, the guideline on the prophylaxis, diagnosis, and treatment of osteoporosis in postmenopausal women and men was published as a completely revised guideline. The implications for practice include a change in the justifying indication for performing a bone density measurement, the time interval over which the fracture risk is determined, the level and number of therapy thresholds, and the recommendations for the therapeutic approach that are adapted to the individual fracture risk present. Risk assessment for the prediction of spine and hip fractures is essential in the context of osteoporosis diagnostics. In addition to age and gender, there are a total of 33 risk factors to determine the individual risk of fracture. Much more attention is paid to the assessment of the risk of falls and, depending on the result, combined with recommendations for muscle training and protein intake from the age of 65. Risk indicators must also be taken into account when determining the indication for osteoporosis diagnosis, as well as the risk factors of the imminent risk of fracture. The indication for baseline diagnostics has changed from the >20% 10-year fracture risk to diagnostics in postmenopausal women and in men aged 50 years and older, depending on the fracture risk factor profile. This eliminates a specific fracture risk threshold for basic diagnostics. Thus, in the young patient group (50-60 years), the risk factors considered medically relevant for the indication for osteoporosis diagnosis must be taken into account. New thresholds as an indication for initiating therapy is the determination of fracture risk using a risk calculator over 3 years instead of 10 years. The indication for drug therapy should be based on the threshold values of the DVO risk model. The data clearly suggests a significantly faster and more effective fracture risk-reducing effect of anabolic therapy. This is recommended in the first sequence in cases of a very high risk of fracture from 10%/3 years with osteoanabolic active substances (teriparatide or romosozumab). Such a therapy sequence should be initiated directly and not delayed due to upcoming dental procedures. Follow-up therapy to consolidate the reduction of fracture risk should be chosen individually.

An osteoporosis knowledge assessment instrument - development and validation.

INTRODUCTION: The consequences of osteoporotic fractures are extremely detrimental to the individual as well as to society. Adopting effective preventative measures is a top public health priority.

Age-adjusted Charlson comorbidity index is associated with the risk of osteoporosis in older fall-prone men: a retrospective cohort study.

BACKGROUND: There is growing evidence linking the age-adjusted Charlson comorbidity index (aCCI), an assessment tool for multimorbidity, to fragility fracture and fracture-related postoperative complications. However, the role of multimorbidity in osteoporosis has not yet been thoroughly evaluated. We aimed to investigate the association between aCCI and the risk of osteoporosis in older adults at moderate to high risk of falling. METHODS: A total of 947 men were included from January 2015 to August 2022 in a hospital in Beijing, China. The aCCI was calculated by counting age and each comorbidity according to their weighted scores, and the participants were stratified into two groups by aCCI: low (aCCI < 5), and high (aCCI ≥5). The Kaplan Meier method was used to assess the cumulative incidence of osteoporosis by different levels of aCCI. The Cox proportional hazards regression model was used to estimate the association of aCCI with the risk of osteoporosis. Receiver operating characteristic (ROC) curve was adapted to assess the performance for aCCI in osteoporosis screening. RESULTS: At baseline, the mean age of all patients was 75.7 years, the mean BMI was 24.8 kg/m2, and 531 (56.1%) patients had high aCCI while 416 (43.9%) were having low aCCI. During a median follow-up of 6.6 years, 296 participants developed osteoporosis. Kaplan-Meier survival curves showed that participants with high aCCI had significantly higher cumulative incidence of osteoporosis compared with those had low aCCI (log-rank test: P < 0.001). When aCCI was examined as a continuous variable, the multivariable-adjusted model showed that the osteoporosis risk increased by 12.1% (HR = 1.121, 95% CI 1.041-1.206, P = 0.002) as aCCI increased by one unit. When aCCI was changed to a categorical variable, the multivariable-adjusted hazard ratios associated with different levels of aCCI [low (reference group) and high] were 1.00 and 1.557 (95% CI 1.223-1.983) for osteoporosis (P <  0.001), respectively. The aCCI (cutoff ≥5) revealed an area under ROC curve (AUC) of 0.566 (95%CI 0.527-0.605, P = 0.001) in identifying osteoporosis in older fall-prone men, with sensitivity of 64.9% and specificity of 47.9%. CONCLUSIONS: The current study indicated an association of higher aCCI with an increased risk of osteoporosis among older fall-prone men, supporting the possibility of aCCI as a marker of long-term skeletal-related adverse clinical outcomes.

Guidelines for fracture risk assessment and management of osteoporosis in postmenopausal women and men above the age of 50 in Qatar.

We present comprehensive guidelines for osteoporosis management in Qatar. Formulated by the Qatar Osteoporosis Association, the guidelines recommend the age-dependent Qatar fracture risk assessment tool for screening, emphasizing risk-based treatment strategies and discouraging routine dual-energy X-ray scans. They offer a vital resource for physicians managing osteoporosis and fragility fractures nationwide. PURPOSE: Osteoporosis and related fragility fractures are a growing public health issue with an impact on individuals and the healthcare system. We aimed to present guidelines providing unified guidance to all healthcare professionals in Qatar regarding the management of osteoporosis. METHODS: The Qatar Osteoporosis Association formulated guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men above the age of 50. A panel of six local rheumatologists who are experts in the field of osteoporosis met together and conducted an extensive review of published articles and local and international guidelines to formulate guidance for the screening and management of postmenopausal women and men older than 50 years in Qatar. RESULTS: The guidelines emphasize the use of the age-dependent hybrid model of the Qatar fracture risk assessment tool for screening osteoporosis and risk categorization. The guidelines include screening, risk stratification, investigations, treatment, and monitoring of patients with osteoporosis. The use of a dual-energy X-ray absorptiometry scan without any risk factors is discouraged. Treatment options are recommended based on risk stratification. CONCLUSION: Guidance is provided to all physicians across the country who are involved in the care of patients with osteoporosis and fragility fractures.

Application of machine learning algorithms to identify people with low bone density.

Background: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis. Objective: This study aimed to develop a machine learning algorithm to effectively identify people with low bone density, using readily available demographic and blood biochemical data. Methods: Using NHANES 2017-2020 data, participants over 50 years old with complete femoral neck BMD data were selected. This cohort was randomly divided into training (70%) and test (30%) sets. Lasso regression selected variables for inclusion in six machine learning models built on the training data: logistic regression (LR), support vector machine (SVM), gradient boosting machine (GBM), naive Bayes (NB), artificial neural network (ANN) and random forest (RF). NHANES data from the 2013-2014 cycle was used as an external validation set input into the models to verify their generalizability. Model discrimination was assessed via AUC, accuracy, sensitivity, specificity, precision and F1 score. Calibration curves evaluated goodness-of-fit. Decision curves determined clinical utility. The SHAP framework analyzed variable importance. Results: A total of 3,545 participants were included in the internal validation set of this study, of whom 1870 had normal bone density and 1,675 had low bone density Lasso regression selected 19 variables. In the test set, AUC was 0.785 (LR), 0.780 (SVM), 0.775 (GBM), 0.729 (NB), 0.771 (ANN), and 0.768 (RF). The LR model has the best discrimination and a better calibration curve fit, the best clinical net benefit for the decision curve, and it also reflects good predictive power in the external validation dataset The top variables in the LR model were: age, BMI, gender, creatine phosphokinase, total cholesterol and alkaline phosphatase. Conclusion: The machine learning model demonstrated effective classification of low BMD using blood biomarkers. This could aid clinical decision making for osteoporosis prevention and management.

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.

Development of a program theory for osteoporosis patient education in Denmark: a qualitative study based on realist evaluation.

BACKGROUND: Osteoporosis patient education is offered in many countries worldwide. When evaluating complex interventions like these, it is important to understand how and why the intervention leads to effects. This study aimed to develop a program theory of osteoporosis patient education in Danish municipalities with a focus on examining the mechanisms of change i.e. what is about the programs that generate change. METHODS: The program theory was developed in an iterative process. The initial draft was based on a previous published systematic review, and subsequently the draft was continually refined based on findings from observations (10 h during osteoporosis patient education) and interviews (individual interviews with six employees in municipalities and three health professionals at hospitals, as well as four focus group interviews with participants in patient education (in total 27 informants)). The transcribed interviews were analyzed using thematic analysis and with inspiration from realist evaluation the mechanisms as well as the contextual factors and outcomes were examined. RESULTS: Based on this qualitative study we developed a program theory of osteoporosis patient education and identified four mechanisms: motivation, recognizability, reassurance, and peer reflection. For each mechanism we examined how contextual factors activated the mechanism as well as which outcomes were achieved. For instance, the participants' motivation is activated when they meet in groups, and thereafter outcomes such as more physical activity may be achieved. Recognizability is activated by the participants' course of disease, which may lead to better ergonomic habits. Reassurance may result in more physical activity, and this mechanism is activated in newly diagnosed participants without previous fractures. Peer reflection is activated when the participants meet in groups, and the outcome healthier diet may be achieved. CONCLUSIONS: We developed a program theory and examined how and why osteoporosis patient education is likely to be effective. Understanding these prerequisites is important for future implementation and evaluation of osteoporosis patient education.

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.

A Study on Intelligent Optical Bone Densitometry.

Osteoporosis is a prevalent chronic disease worldwide, particularly affecting the aging population. The gold standard diagnostic tool for osteoporosis is Dual-energy X-ray Absorptiometry (DXA). However, the expensive cost of the DXA machine and the need for skilled professionals to operate it restrict its accessibility to the general public. This paper builds upon previous research and proposes a novel approach for rapidly screening bone density. The method involves utilizing near-infrared light to capture local body information within the human body. Deep learning techniques are employed to analyze the obtained data and extract meaningful insights related to bone density. Our initial prediction, utilizing multi-linear regression, demonstrated a strong correlation (r = 0.98, p-value = 0.003**) with the measured Bone Mineral Density (BMD) obtained from Dual-energy X-ray Absorptiometry (DXA). This indicates a highly significant relationship between the predicted values and the actual BMD measurements. A deep learning-based algorithm is applied to analyze the underlying information further to predict bone density at the wrist, hip, and spine. The prediction of bone densities in the hip and spine holds significant importance due to their status as gold-standard sites for assessing an individual's bone density. Our prediction rate had an error margin below 10% for the wrist and below 20% for the hip and spine bone density.

Development and experimental validation of an energy metabolism-related gene signature for diagnosing of osteoporosis.

Osteoporosis is usually caused by excessive bone resorption and energy metabolism plays a critical role in the development of osteoporosis. However, little is known about the role of energy metabolism-related genes in osteoporosis. This study aimed to explore the important energy metabolism-related genes involved in the development of osteoporosis and develop a diagnosis signature for osteoporosis. The GSE56814, GSE62402, and GSE7158 datasets were downloaded from the NCBI Gene Expression Omnibus. The intersection of differentially expressed genes between high and low levels of body mineral density (BMD) and genes related to energy metabolism were screened as differentially expressed energy metabolism genes (DE-EMGs). Subsequently, a DE-EMG-based diagnostic model was constructed and differential expression of genes in the model was validated by RT-qPCR. Furthermore, a receiver operating characteristic curve and nomogram model were constructed to evaluate the predictive ability of the diagnostic model. Finally, the immune cell types in the merged samples and networks associated with the selected optimal DE-EMGs were constructed. A total of 72 overlapped genes were selected as DE-EMGs, and a five DE-EMG based diagnostic model consisting B4GALT4, ADH4, ACAD11, B4GALT2, and PPP1R3C was established. The areas under the curve of the five genes in the merged training dataset and B4GALT2 in the validation dataset were 0.784 and 0.790, respectively. Moreover, good prognostic prediction ability was observed using the nomogram model (C index = 0.9201; P = 5.507e-14). Significant differences were observed in five immune cell types between the high- and low-BMD groups. These included central memory, effector memory, and activated CD8 T cells, as well as regulatory T cells and activated B cells. A network related to DE-EMGs was constructed, including hsa-miR-23b-3p, DANCR, 17 small-molecule drugs, and two Kyoto Encyclopedia of Genes and Genomes pathways, including metabolic pathways and pyruvate metabolism. Our findings highlighted the important roles of DE-EMGs in the development of osteoporosis. Furthermore, the DANCR/hsa-miR-23b-3p/B4GALT4 axis might provide novel molecular insights into the process of osteoporosis development.

Raman spectroscopic analysis for osteoporosis identification in humans with hip fractures.

Osteoporosis is a significant health concern. While multiple techniques have been utilized to diagnose this condition, certain limitations still persist. Raman spectroscopy has shown promise in predicting bone strength in animal models, but its application to humans requires further investigation. In this study, we present an in vitro approach for predicting osteoporosis in 10 patients with hip fractures using Raman spectroscopy. Raman spectra were acquired from exposed femoral heads collected during surgery. Employing a leave-one-out cross-validated linear discriminant analysis (LOOCV-LDA), we achieved accurate classification (90 %) between osteoporotic and osteopenia groups. Additionally, a LOOCV partial least squares regression (PLSR) analysis based on the complete Raman spectra demonstrated a significant prediction (r2 = 0.84, p < 0.05) of bone mineral density as measured by dual X-ray absorptiometry (DXA). To the best of our knowledge, this study represents the first successful demonstration of Raman spectroscopy correlating with osteoporotic status in humans.

Understanding primary care providers' attitudes towards preventive screenings to patients with inflammatory bowel disease.

BACKGROUND: Preventive care is important for managing inflammatory bowel disease (IBD), yet primary care providers (PCPs) often face challenges in delivering such care due to discomfort and unfamiliarity with IBD-specific guidelines. This study aims to assess PCPs' attitudes towards, and practices in, providing preventive screenings for IBD patients, highlighting areas for improvement in guideline dissemination and education. METHODS: Using a web-based opt-in panel of PCPs (DocStyles survey, spring 2022), we assessed PCPs' comfort level with providing/recommending screenings and the reasons PCPs felt uncomfortable (n = 1,503). Being likely to provide/recommend screenings for depression/anxiety, skin cancer, osteoporosis, and cervical cancer were compared by PCPs' comfort level and frequency of seeing patients with IBD. We estimated adjusted odd ratios (AORs) of being likely to recommend screenings and selecting responses aligned with IBD-specific guidelines by use of clinical practice methods. RESULTS: About 72% of PCPs reported being comfortable recommending screenings to patients with IBD. The top reason identified for not feeling comfortable was unfamiliarity with IBD-specific screening guidelines (55%). Being comfortable was significantly associated with being likely to provide/recommend depression/anxiety (AOR = 3.99) and skin cancer screenings (AOR = 3.19) compared to being uncomfortable or unsure. Percentages of responses aligned with IBD-specific guidelines were lower than those aligned with general population guidelines for osteoporosis (21.7% vs. 27.8%) and cervical cancer screenings (34.9% vs. 43.9%), and responses aligned with IBD-specific guidelines did not differ by comfort level for both screenings. Timely review of guidelines specific to immunosuppressed patients was associated with being likely to provide/recommend screenings and selecting responses aligned with IBD-specific guidelines. CONCLUSIONS: Despite a general comfort among PCPs in recommending preventive screenings for IBD patients, gaps in knowledge regarding IBD-specific screening guidelines persist. Enhancing awareness and understanding of these guidelines through targeted education and resource provision may bridge this gap.

Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data.

Osteoporosis is a major public health concern that significantly increases the risk of fractures. The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. A total of 10,000 complete patient records of primary healthcare data in the German Disease Analyzer database (IMS HEALTH) were included, of which 1293 diagnosed with osteoporosis and 8707 without the condition. The demographic characteristics and chronic disease data, including age, gender, lipid disorder, cancer, COPD, hypertension, heart failure, CHD, diabetes, chronic kidney disease, and stroke were collected from electronic health records. Ten different machine learning algorithms were employed to construct the predictive mode. The performance of the model was further validated and the relative importance of features in the model was analyzed. Out of the ten machine learning algorithms, the Stacker model based on Logistic Regression, AdaBoost Classifier, and Gradient Boosting Classifier demonstrated superior performance. The Stacker model demonstrated excellent performance through ten-fold cross-validation on the training set and ROC curve analysis on the test set. The confusion matrix, lift curve and calibration curves indicated that the Stacker model had optimal clinical utility. Further analysis on feature importance highlighted age, gender, lipid metabolism disorders, cancer, and COPD as the top five influential variables. In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies.

Optimising health after early menopause.

The typical age at menopause is 50-51 years in high-income countries. However, early menopause is common, with around 8% of women in high-income countries and 12% of women globally experiencing menopause between the ages of 40 years and 44 years. Menopause before age 40 years (premature ovarian insufficiency) affects an additional 2-4% of women. Both early menopause and premature ovarian insufficiency can herald an increased risk of chronic disease, including osteoporosis and cardiovascular disease. People who enter menopause at younger ages might also experience distress and feel less supported than those who reach menopause at the average age. Clinical practice guidelines are available for the diagnosis and management of premature ovarian insufficiency, but there is a gap in clinical guidance for early menopause. We argue that instead of distinct age thresholds being applied, early menopause should be seen on a spectrum between premature ovarian insufficiency and menopause at the average age. This Series paper presents evidence for the short-term and long-term consequences of early menopause. We offer a practical framework for clinicians to guide diagnosis and management of early menopause, which considers the nature and severity of symptoms, age and medical history, and the individual's wishes and priorities to optimise their quality of life and short-term and long-term health. We conclude with recommendations for future research to address key gaps in the current evidence.

[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.

Knowledge and Awareness of Osteoporosis: A Survey of Physical Therapy Providers in Saudi Arabia.

Background: Osteoporosis "OP" is classified as one of the most serious health conditions worldwide. OP increases the skeletal fracture risk by 35%, particularly at hip, spine, and wrist joints. Healthcare professionals should be aware of OP clinical signs and have good knowledge while managing all patients. Objectives: This study aims to investigate the current level of osteoporosis knowledge and awareness among physical therapy providers in Saudi Arabia. Methods: One hundred and sixty-eight physical therapy providers participated in this cross-sectional electronic survey from February to July of 2021. The participants completed the Osteoporosis Knowledge Assessment Tool questionnaire (OKAT). Descriptive analysis was utilized to assess the current level of osteoporosis knowledge among physical therapy providers. Results: Among the 168 participants, 55% (n = 92) were over 31 years old and 45% (n = 76) were 30 years old or under. The majority of participants 37% (n = 62) had more than 10 years of experience, 45% (n = 76) mainly treat orthopedic conditions, and 70% (n = 117) live in the western region. The results showed that 67.9% (n = 114) of participants had good knowledge about osteoporosis, while 19.6% (n = 33) had poor knowledge, and only 12.5% (n = 21) had excellent knowledge. Conclusion: Physical therapy providers in Saudi Arabia have a good knowledge of osteoporosis. The overall OP preventive measure knowledge questions were poor. It is crucial for physical therapy providers to act appropriately to prevent falls and mitigate any potential risks.