Development of a nomogram for individualizing hip fracture risk in men and women

Transferability of bone phenotyping and fracture risk assessment by μFRAC from first-generation high-resolution peripheral quantitative computed tomography to second-generation scan data

INTRODUCTION

The continued development of high-resolution peripheral quantitative computed tomography (HR-pQCT) has led to a second-generation scanner with higher resolution and longer scan region. However, large multicenter prospective cohorts were collected with first-generation HR-pQCT and have been used to develop bone phenotyping and fracture risk prediction (μFRAC) models. This study establishes whether there is sufficient universality of these first-generation trained models for use with second-generation scan data.

METHODS

HR-pQCT data were collected for a cohort of 60 individuals, who had been scanned on both first- and second-generation scanners on the same day to establish the universality of the HR-pQCT models. These data were each used as input to first-generation trained bone microarchitecture models for bone phenotyping and fracture risk prediction, and their outputs were compared for each study participant. Reproducibility of the models were assessed using same-day repeat scans obtained from first-generation (n = 37) and second-generation (n = 74) scanners.

RESULTS

Across scanner generations, the bone phenotyping model performed with an accuracy of 93.1%. Similarly, the 5-year fracture risk assessment by μFRAC was well correlated with a Pearson's (r) correlation coefficient of r > 0.83 for the three variations of μFRAC (varying inclusion of clinical risk factors, finite element analysis, and dual X-ray absorptiometry). The first-generation reproducibility cohort performed with an accuracy for categorical assignment of 100% (bone phenotyping) and a correlation coefficient of 0.99 (μFRAC), whereas the second-generation reproducibility cohort performed with an accuracy of 96.4% (bone phenotyping) and a correlation coefficient of 0.99 (μFRAC).

CONCLUSION

We demonstrated that bone microarchitecture models trained using first-generation scan data generalize well to second-generation scans, performing with a high level of accuracy and reproducibility. Less than 4% of individuals' estimated fracture risk led to a change in treatment threshold, and in general, these dissimilar outcomes using second-generation data tended to be more conservative.

Fracture risk prediction in postmenopausal women from GO Study: the comparison between FRAX, Garvan, and POL-RISK algorithms

In the longitudinal, retrospective study, the ability of the FRAX, Garvan, and POL-RISK algorithms to predict osteoporotic fractures was compared in a group of 457 women. Using the rigid threshold of 10% showed a significant discrepancy in sensitivity and specificity of all tools. New thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds allow for improving the diagnostic accuracy of all three calculators.

INTRODUCTION

The aim of the longitudinal, retrospective study was to compare three tools designed to assess fracture risk: FRAX, Garvan, and POL-RISK in their prediction of fracture incidence.

MATERIAL

The study group consisted of 457 postmenopausal women with a mean age of 64.21 ± 5.94 years from the Gliwice Osteoporosis (GO) Study. Comprehensive data on clinical factors related to fractures were collected for all participants. Bone densitometry was performed at the proximal femur using the Prodigy device (GE, USA). Fracture risk was established using the FRAX, Garvan, and POL-RISK algorithms. Data on the incidence of osteoporotic fractures were collected over the last 10 years.

RESULTS

During the period of observation 72, osteoporotic fractures occurred in 63 subjects. For a preliminary comparison of the predictive value of analyzed diagnostic tools, the fracture risk threshold of 10% was used. For FRAX, the fracture probability exceeding 10% was observed only in 11 subjects who experienced fractures; thus, the fracture was properly predicted only in 22.9% of women. For Garvan, the respective value was 90.5%, and for POL-RISK, it was 98.4%. That gave a very low true positive value for FRAX and a very high false positive value for Garvan and POL-RISK. Based on ROC curves, new thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds improve the diagnostic accuracy of all compared fracture prediction tools.

CONCLUSION

The current study showed that different fracture risk assessment tools, although having similar clinical purposes, require different cut-off thresholds for making therapeutic decisions. Better identification of patients requiring therapy based on such an approach may help reduce the number of new fractures.

Frailty in the prediction of delirium in the intensive care unit: A secondary analysis of the Deli study

BACKGROUND

Delirium is an acute disorder of attention and cognition with an incidence of up to 70% in the adult intensive care setting. Due to the association with significantly increased morbidity and mortality, it is important to identify who is at the greatest risk of an acute episode of delirium while being cared for in the intensive care. The objective of this study was to determine the ability of the cumulative deficit frailty index and clinical frailty scale to predict an acute episode of delirium among adults admitted to the intensive care.

METHODS

This study is a secondary analysis of the Deli intervention study, a hybrid stepped-wedge cluster randomized controlled trial to assess the effectiveness of a nurse-led intervention to reduce the incidence and duration of delirium among adults admitted to the four adult intensive care units in the south-west of Sydney, Australia. Important predictors of delirium were identified using a bootstrap approach and the absolute risks, based on the cumulative deficit frailty index and the clinical frailty scale are presented.

RESULTS

During the 10-mth data collection period (May 2019 and February 2020) 2566 patients were included in the study. Both the cumulative deficit frailty index and the clinical frailty scale on admission, plus age, sex, and APACHE III (AP III) score were able to discriminate between patients who did and did not experience an acute episode of delirium while in the intensive care, with AUC of 0.701 and 0.703 (moderate discriminatory ability), respectively. The addition of a frailty index to a prediction model based on age, sex, and APACHE III score, resulted in net reclassified of risk. Nomograms to individualize the absolute risk of delirium using these predictors are also presented.

CONCLUSION

We have been able to show that both the cumulative deficits frailty index and clinical frailty scale predict an acute episode of delirium among adults admitted to intensive care.

Performance of FRAX in older adults with frailty: the Framingham Heart Study

We compared the performance of FRAX according to frailty status in 3554 individuals from the Framingham Study. During 10-year follow-up, 6.9% and 3.0% of participants with and without frailty experienced MOF. Discrimination profiles were lower in participants with frailty compared to those without, but they improved when FRAX included BMD.

INTRODUCTION

Frailty increases fracture risk. FRAX was developed to predict fractures but never validated in individuals with frailty. We aimed to compare the predictive performance of FRAX (v4.3) in individuals with and without frailty.

METHODS

We conducted a cohort study using the Framingham Heart Study. Frailty was defined by the Fried phenotype. Major osteoporotic fractures (MOF) were ascertained from medical records during 10-year follow-up. To evaluate discrimination and calibration of FRAX, we calculated the area-under-the-receiver-operating characteristics curves (AUC) using logistic regression models and observed-to-predicted fracture probabilities. Analyses were stratified by frailty status.

RESULTS

Frailty was present in 550/3554 (15.5%) of participants. Participants with frailty were older (81.1 vs. 67.6 years), female (68.6% vs. 55.1%), and had greater mean FRAX scores (MOF: 15.9% vs. 10.1%) than participants without frailty. During follow-up, 38 participants with frailty (6.9%) and 91 without (3.0%) had MOFs. The AUC for FRAX (without BMD) was lower in participants with frailty (0.584; 95% CI 0.504-0.663) compared to those without (0.695; 95% CI 0.649-0.741); p value = 0.02. Among participants with frailty, the AUC improved when FRAX included BMD (AUC 0.658, p value < 0.01). FRAX overestimated MOF risk, with larger overestimations in individuals without frailty. Performance of FRAX for hip fracture was similar.

CONCLUSION

FRAX may have been less able to identify frail individuals at risk for fracture, as compared with individuals without frailty, unless information on BMD is available. This suggests that BMD captures features important for fracture prediction in frail persons. Future fracture prediction models should be developed among persons with frailty.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

Compressive buttress compared with off-axial screw fixation for vertical femoral neck fractures in young adults: a prospective, randomized controlled trial

BACKGROUND

To compare the clinical outcomes of compressive buttress screw (CBS) fixation, a novel screw fixation strategy, to off-axial screw fixation (off-axial partial threaded cannulated screw, OPTCS) for vertical femoral neck fractures (FNFs) in young adults.

METHODS

A total of 146 adults younger than 55 years old with high-energy Pauwels type III FNFs were randomized to receive CBS fixation or OPTCS fixation. Primary outcomes were complication rates, including fixation failure, fracture nonunion, and avascular necrosis of the femoral head (ANFH) at 24 months after treatment. Fixation loosening, femoral neck shortening and varus collapse, patient function and quality of life using the Harris hip score (HHS), and EuroQol-5 dimensional-5 levels (EQ-5D-5L) questionnaire (including EQ-5D-5L and EQ-VAS) were assessed as secondary outcomes at 24 months.

RESULTS

CBS and OPTCS fixation groups were similar with regard to demographics at baseline. At 24 months, patients in the CBS fixation cohort had a significantly lower rate of fixation failure (10.5% vs. 25.0%, p = 0.041) and fracture nonunion (1.8% vs. 18.3%, p = 0.003) compared with patients who received OPTCS fixation. There was no difference in rate of ANFH (7.0% vs. 11.7%, p = 0.389) between groups. Additionally, patients managed with CBS fixation showed significantly less fixation loosening (19.3% vs. 58.3%, p < 0.001), less severe femoral neck shortening and varus collapse (10.5% vs. 25.0%, p = 0.007), higher HHS (93 vs. 83, p = 0.001) and more excellent grade (68.4% vs. 36.7%, p = 0.008), higher EQ-5D-5L (0.814 vs, 0.581, p < 0.001) and EQ-VAS (85 vs. 80, p = 0.002).

CONCLUSION

CBS screw fixation confers significantly lower complication rate in addition to higher functional and quality of life outcomes for young adults with high-energy FNF compared with OPTCS fixation.

TRIAL REGISTRATION

This prospective, randomized controlled trial was approved by the institutional review board of our center, Ethics Committee of Shanghai sixth people's Hospital, and registered at www.chictr.org.cn (Approval Number: ChiCTR1900026283; Registered 29 September 2019-Retrospectively registered, https://www.chictr.org.cn/showproj.html?proj=43164 ).

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.

Utilize polygenic risk score to enhance fracture risk estimation and improve the performance of FRAX in patients with osteoporosis

This study examined the use of polygenic risk scores (PGS) in combination with the Fracture Risk Assessment Tool (FRAX) to enhance fragility fractures risk estimation in osteoporosis patients. Analyzing data from over 57,000 participants, PGS improved fracture risk estimation, especially for individuals with intermediate to low risks, allowing personalized preventive strategies.

INTRODUCTION

Osteoporosis and fragility fractures are multifactorial, with contributions from both clinical and genetic determinants. However, whether using polygenic risk scores (PGS) may enhance the risk estimation of osteoporotic fracture in addition to Fracture Risk Assessment Tool (FRAX) remains unknown. This study investigated the collective association of PGS and FRAX with fragility fracture.

METHODS

We conducted a cohort study from the Taiwan Precision Medicine Initiative (TPMI) at Taichung Veterans General Hospital, Taiwan. Genotyping was performed to compute PGS associated with bone mineral density (BMD). Phenome-wide association studies were executed to pinpoint phenotypes correlated with the PGS. Logistic regression analysis was conducted to ascertain factors associated with osteoporotic fractures.

RESULTS

Among all 57,257 TPMI participants, 3744 (904 men and 2840 women, with a mean age of 66.7) individuals had BMD testing, with 540 (14.42%) presenting with fractures. The 3744 individuals who underwent BMD testing were categorized into four quartiles (Q1-Q4) based on PGS; 540 (14.42%) presented with fractures. Individuals with PGS-Q1 exhibited lower BMD, a higher prevalence of major fractures, and elevated FRAX-major and FRAX-hip than those with PGS-Q4. PGS was associated with major fractures after adjusting age, sex, and FRAX scores. Notably, the risk of major fractures (PGS-Q1 vs. Q4) was significantly higher in the subgroups of FRAX-major scores < 10% and 10-20%, but not in participants with a FRAX-major score ≧ 20%.

CONCLUSIONS

Our study highlights the potential of PGS to augment fracture risk estimation in conjunction with FRAX, particularly in individuals with middle to low risks. Incorporating genetic testing could empower physicians to tailor personalized preventive strategies for osteoporosis.

Optimal fracture prediction thresholds for therapy onset, established from FRAX and Garvan algorithms: a longitudinal observation of the population representative female cohort from the RAC-OST-POL Study

The study shows that the use of unified cutoff thresholds to identify high fracture risks by two popular calculators-FRAX and Garvan-leads to a significant discrepancy between the prediction of fractures and their actual prevalence over the period of 10 years. On the basis of the ROC analyses, a proposal of differentiated thresholds is presented. They were established at 6% for FRAX major fracture risk, 1.4% for FRAX hip fracture risk, 14.4% for Garvan any fracture risk, and 8.8% for Garvan hip fracture risk.

PURPOSE/INTRODUCTION

The aim of the study was to verify how much were the tools, designed to predict fracture risks, precise vs. the actual fracture incidence values over a prospective observation.

METHODS

The study group consisted of a population-based postmenopausal sample from the RAC-OST-POL Study. At baseline, there were 978 subjects at the mean age of 66.4 ± 7.8 years and, after a 10-year follow-up, 640 women remained at the mean age of 75.0 ± 6.95 years. At baseline, the fracture risk was established by the FRAX and Garvan tools.

RESULTS

During the observation period, 190 osteoporotic fractures were identified in 129 subjects. When high-risk fracture cutoff thresholds (of 10% for major/any and 3% for hip fractures) were employed, only 19.59% of major fractures and 50% of hip fractures were identified in the high-risk group. For the Garvan tool, the percentage of correctly predicted fractures for any and hip fractures was 86.05% and 71.43%, respectively. Nevertheless, the fracture prediction by the Garvan tool was associated with the qualification of numerous subjects to the high-risk group, who subsequently did not experience a fracture in the 10-year follow-up period (false-positive prediction). Based on the ROC analyses, new high-risk thresholds were proposed individually for each calculator, improving the sensitivity, specificity, and diagnostic accuracy of these tools. They were established at 6% for FRAX major fracture risk, 1.4% for FRAX hip fracture risk, 14.4% for Garvan any fracture risk, and 8.8% for Garvan hip fracture risk.

CONCLUSIONS

The current prospective study enabled to establish new, optimal thresholds for therapy initiation. Such a modified approach may enable a more accurate identification of treatment requiring patients and, in consequence, reduce the number of new fractures.

BONEcheck: A digital tool for personalized bone health assessment

Objectives

Osteoporotic fracture is a significant public health burden associated with increased mortality risk and substantial healthcare costs. Accurate and early identification of high-risk individuals and mitigation of their risks is a core part of the treatment and prevention of fractures. Here we introduce a digital tool called 'BONEcheck' for personalized assessment of bone health.

Methods

The development of BONEcheck primarily utilized data from the prospective population-based Dubbo Osteoporosis Epidemiology Study and the Danish Nationwide Registry. BONEcheck has 3 modules: input data, risk estimates, and risk context. Input variables include age, gender, prior fracture, fall incidence, bone mineral density (BMD), comorbidities, and genetic variants associated with BMD.

Results

Based on the input variables, BONEcheck estimates the probability of any fragility fracture and hip fracture within 5 years, subsequent fracture risk, skeletal age, and time to reach osteoporosis. The probability of fracture is shown in both numeric and human icon array formats. The risk is also contextualized within the framework of treatment and management options on Australian guidelines, with consideration given to the potential fracture risk reduction and survival benefits. Skeletal age was estimated as the sum of chronological age and years of life lost due to a fracture or exposure to risk factors that elevate mortality risk.

Conclusions

BONEcheck is an innovative tool that empowers doctors and patients to engage in well-informed discussions and make decisions based on the patient's risk profile. Public access to BONEcheck is available via https://bonecheck.org and in Apple Store (iOS) and Google Play (Android).

Height Loss Is an Independent Predictor of Fracture Incidence in Postmenopausal Women: The Results from the Gliwice Osteoporosis Study (GO Study)

BACKGROUND

The aim of a longitudinal, retrospective study was to establish variables predicting fracture incidence over a decade.

METHODS

The study sample comprises a group of 457 postmenopausal women aged over 55 years, recruited from the database of an outpatient osteoporotic clinic. Several variables with potential influence on bone status, including the measurement of body height and hip bone densitometry, were collected. BMD at the femoral neck (FN BMD) was established using a Prodigy device (Lunar, GE, USA). Current body height was compared with the maximal historical body height in early adulthood, as reported by the patient.

RESULTS

Three hundred and ninety-four women did not have fractures during the follow up, and 63 subjects presented fractures. Subjects with fracture had lower FN BMD with a T-score of -1.86 ± 1.04 compared to -1.44 ± 0.89 in those without fractures (p < 0.001). Mean height loss (HL) was 3.47 ± 2.11 cm in fractured subjects and 2.50 ± 2.47 cm in unfractured ones, and differed significantly, p < 0.01. Fracture incidence was significantly related to age, rheumatoid arthritis, falls, and previous fractures. In the multivariable analysis using logistic regression, FN BMD, baseline fracture, and HL were identified as the significant predictors of fractures of follow up.

CONCLUSIONS

Osteoporotic fractures in postmenopausal women were predicted by FN BMD, prior fracture(s), and HL easily established during physical examination.

Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old

Targeted fracture prevention strategies among late-life adults should balance fracture risk versus competing mortality risk. Models have previously been constructed using Fine-Gray subdistribution methods. We used a machine learning method adapted for competing risk survival time to evaluate candidate risk factors and create models for hip fractures and competing mortality among men and women aged 80 years and older using data from three prospective cohorts (Study of Osteoporotic Fractures [SOF], Osteoporotic Fracture in Men study [MrOS], Health Aging and Body Composition study [HABC]). Random forest competing risk models were used to estimate absolute 5-year risk of hip fracture and absolute 5-year risk of competing mortality (excluding post-hip fracture deaths). Models were constructed for both outcomes simultaneously; minimal depth was used to rank and select variables for smaller models. Outcome specific models were constructed; variable importance was used to rank and select variables for inclusion in smaller random forest models. Random forest models were compared to simple Fine-Gray models with six variables selected a priori. Top variables for competing risk random forests were frailty and related components in men while top variables were age, bone mineral density (BMD) (total hip, femoral neck), and frailty components in women. In both men and women, outcome specific rankings strongly favored BMD variables for hip fracture prediction while frailty and components were strongly associated with competing mortality. Model discrimination for random forest models varied from 0.65 for mortality in women to 0.81 for hip fracture in men and depended on model choice and variables included. Random models performed slightly better than simple Fine-Gray model for prediction of competing mortality, but similarly for prediction of hip fractures. Random forests can be used to estimate risk of hip fracture and competing mortality among the oldest old. Modest gains in performance for mortality without hip fracture compared to Fine-Gray models must be weighed against increased complexity. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Development and validation of common data model-based fracture prediction model using machine learning algorithm

The need for an accurate country-specific real-world-based fracture prediction model is increasing. Thus, we developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort in Korea. The model includes history of fracture, age, lumbar spine and total hip T-score, and cardiovascular disease.

PURPOSE

Osteoporotic fractures are substantial health and economic burden. Therefore, the need for an accurate real-world-based fracture prediction model is increasing. We aimed to develop and validate an accurate and user-friendly model to predict major osteoporotic and hip fractures using a common data model database.

METHODS

The study included 20,107 and 13,353 participants aged ≥ 50 years with data on bone mineral density using dual-energy X-ray absorptiometry from the CDM database between 2008 and 2011 from the discovery and validation cohort, respectively. The main outcomes were major osteoporotic and hip fracture events. DeepHit and Cox proportional hazard models were used to identify predictors of fractures and to build scoring systems, respectively.

RESULTS

The mean age was 64.5 years, and 84.3% were women. During a mean of 7.6 years of follow-up, 1990 major osteoporotic and 309 hip fracture events were observed. In the final scoring model, history of fracture, age, lumbar spine T-score, total hip T-score, and cardiovascular disease were selected as predictors for major osteoporotic fractures. For hip fractures, history of fracture, age, total hip T-score, cerebrovascular disease, and diabetes mellitus were selected. Harrell's C-index for osteoporotic and hip fractures were 0.789 and 0.860 in the discovery cohort and 0.762 and 0.773 in the validation cohort, respectively. The estimated 10-year risks of major osteoporotic and hip fractures were 2.0%, 0.2% at score 0 and 68.8%, 18.8% at their maximum scores, respectively.

CONCLUSION

We developed scoring systems for osteoporotic fractures from hospital-based cohorts and validated them in an independent cohort. These simple scoring models may help predict fracture risks in real-world practice.

'Skeletal Age' for mapping the impact of fracture on mortality

Background

Fragility fracture is associated with an increased risk of mortality, but mortality is not part of doctor-patient communication. Here, we introduce a new concept called 'Skeletal Age' as the age of an individual's skeleton resulting from a fragility fracture to convey the combined risk of fracture and fracture-associated mortality for an individual.

Methods

We used the Danish National Hospital Discharge Register which includes the whole-country data of 1,667,339 adults in Denmark born on or before January 1, 1950, who were followed up to December 31, 2016 for incident low-trauma fracture and mortality. Skeletal age is defined as the sum of chronological age and the number of years of life lost (YLL) associated with a fracture. Cox's proportional hazards model was employed to determine the hazard of mortality associated with a specific fracture for a given risk profile, and the hazard was then transformed into YLL using the Gompertz law of mortality.

Results

During the median follow-up period of 16 years, there had been 307,870 fractures and 122,744 post-fracture deaths. A fracture was associated with between 1 and 7 years of life lost, with the loss being greater in men than women. Hip fractures incurred the greatest loss of life years. For instance, a 60-year-old individual with a hip fracture is estimated to have a skeletal age of 66 for men and 65 for women. Skeletal Age was estimated for each age and fracture site stratified by gender.

Conclusions

We propose 'Skeletal Age' as a new metric to assess the impact of a fragility fracture on an individual's life expectancy. This approach will enhance doctor-patient risk communication about the risks associated with osteoporosis.

Funding

National Health and Medical Research Council in Australia and Amgen Competitive Grant Program 2019.

Muscle strength and physical performance contribute to and improve fracture risk prediction in older people: A narrative review

Osteoporotic fractures present a major health problem with an increasing prevalence in older people. Fractures are associated with premature mortality, reduced quality of life, subsequent fracture, and increased costs. Hence, it is crucial to identify those at higher risk of fracture. Fracture risk assessment tools incorporated clinical risk factors to improve fracture predictive power over BMD alone. However, fracture risk prediction using these algorithms remains suboptimal, warranting further improvement. Muscle strength and physical performance measurements have been associated with fracture risk. In contrast, the contribution of sarcopenia, the composite condition of low muscle mass, muscle strength and/or physical performance, to fracture risk is unclear. It is uncertain whether this is due to the problematic definition of sarcopenia per se or limitations of the diagnostic tools and cut-off points of the muscle mass component. The recent position statement from the Sarcopenia Definition and Outcomes Consortium confirmed the inclusion of muscle strength and performance in the definition of sarcopenia but not DXA-assessed lean mass. Therefore, clinicians should focus on functional assessment (muscle strength and performance) rather than muscle mass, at least as assessed by DXA, as predictors of fractures. Muscle strength and performance are modifiable risk factors. Resistance exercise improves muscle parameters in the elderly, potentially leading to reduced risk of falls and fractures in the general population and in those who sustained a fracture. Therapists may consider exercise intervention to improve muscle parameters and potentially reduce the risk of fractures. The aim of this review was to explore 1) the contribution of muscle parameters (i.e., muscle mass, strength, and physical performance) to fracture risk in older adults, and 2) the added predictive accuracy of these parameters beyond the existing fracture assessment tools. These topics provide the rationale for investigating strength and physical performance interventions to reduce fracture risk. Most of the included publications showed that muscle mass is not a good predictor of fracture risk, while poor muscle strength and performance are associated with an increased risk of fracture, particularly in men, independent of age, BMD, and other risk factors for fractures. Muscle strength and performance can potentially improve the predictive accuracy in men beyond that obtained by the fracture risk assessment tools, Garvan FRC and FRAX.

Impaired Functional Status Increases Fracture Incidence in 10-year Follow-Up: The Results from RAC-OST-POL Study

INTRODUCTION

The aim of study was to establish the influence of baseline functional status on fracture incidence.

METHODOLOGY

In a prospective 10-years observation in epidemiological sample of postmenopausal women from RAC-OST-POL Study a thesis that affected functional status enhance fracture incidence was verified. At baseline, data were collected in 978 women at mean age 66.48±7.6 years and after 10 years of follow-up 640 subjects at mean age 75.04±6.95 years remained in the study. Functional status at baseline was established using Stand up and Go test (SAG) and Activity of Daily Living (IADL). Afterwards, annually data on fracture incidence were collected by phone interviews.

RESULTS

In the period of observation 190 low-energy fractures in 129 women were noted. The whole group was divided into subgroups: without fracture (n=511), with one fracture (n=91) and those ones who had more than one fracture (n=38). In fractured and unfractured subgroup mean SAG results were 11.36±4.28 and 10.36±2.76, respectively and differed significantly (p<0.01). With increasing number of fractures the SAG time was longer - it was 11.15±4.49 in one fracture subgroup and 11.87±3.73 in multiple fractures subgroup, with both values significantly higher than in no fracture subjects. The mean value of IADL was 23.56±1.60. In 576 (90%) women IADL reached maximal value of 24 points. In the rest of them (n=64) IADL score was between 11 and 23 points. Mean value of IADL in fractured and unfractured subgroup were 23.27±1.97 and 23.64±1.47, respectively and differed significantly (p<0.01).

CONCLUSION

The measures of functional status predict fractures in a prospective observation of representative epidemiological female sample.

Derivation and validation of the CFracture competing risk fracture prediction tool compared with QFracture in older people and people with comorbidity: a population cohort study

BACKGROUND

UK guidelines recommend the QFracture tool to predict the risk of major osteoporotic fracture and hip fracture, but QFracture calibration is poor, partly because it does not account for competing mortality risk. The aim of this study was to derive and validate a competing risk model to predict major osteoporotic fracture and hip fracture (CFracture) and compare its performance with that of QFracture in UK primary care.

METHODS

We used UK linked primary care data from the Clinical Practice Research Datalink GOLD database to identify people aged 30-99 years, split into derivation and validation cohorts. In the derivation cohort, we derived models (CFracture) using the same covariates as QFracture with Fine-Gray competing risk modelling, and included the Charlson Comorbidity Index score as an additional predictor of non-fracture death. In a separate validation cohort, we examined discrimination (using Harrell's C-statistic) and calibration of CFracture compared with QFracture. Reclassification analysis examined differences in the characteristics of patients reclassified as higher risk by CFracture but not by QFracture.

FINDINGS

The derivation cohort included 1 831 606 women and 1 789 820 men, and the validation cohort included 915 803 women and 894 910 men. Overall discrimination of CFracture was excellent (C-statistic=0·813 [95% CI 0·810-0·816] for major osteoporotic fracture and 0·914 [0·908-0·919] for hip fracture in women; 0·734 [0·729-0·740] for major osteoporotic fracture and 0·886 [0·877-0·895] for hip fracture in men) and was similar to QFracture. CFracture calibration overall and in people younger than 75 years was generally excellent. CFracture overpredicted major osteoporotic fracture and hip fracture in older people and people with comorbidity, but was better calibrated than QFracture. Patients classified as high-risk by CFracture but not by QFracture had a higher prevalence of current smoking and previous fracture, but lower prevalence of dementia, cancer, cardiovascular disease, renal disease, and diabetes.

INTERPRETATION

CFracture has similar discrimination to QFracture but is better calibrated overall and in younger people. Both models performed poorly in adults aged 85 years and older. Competing risk models should be recommended for fracture risk prediction to guide treatment recommendations.

FUNDING

National Institute for Health and Care Research, Wellcome Trust, Health Data Research UK.

Update of the fracture risk prediction tool FRAX: a systematic review of potential cohorts and analysis plan

We describe the collection of cohorts together with the analysis plan for an update of the fracture risk prediction tool FRAX with respect to current and novel risk factors. The resource comprises 2,138,428 participants with a follow-up of approximately 20 million person-years and 116,117 documented incident major osteoporotic fractures.

INTRODUCTION

The availability of the fracture risk assessment tool FRAX® has substantially enhanced the targeting of treatment to those at high risk of fracture with FRAX now incorporated into more than 100 clinical osteoporosis guidelines worldwide. The aim of this study is to determine whether the current algorithms can be further optimised with respect to current and novel risk factors.

METHODS

A computerised literature search was performed in PubMed from inception until May 17, 2019, to identify eligible cohorts for updating the FRAX coefficients. Additionally, we searched the abstracts of conference proceedings of the American Society for Bone and Mineral Research, European Calcified Tissue Society and World Congress of Osteoporosis. Prospective cohort studies with data on baseline clinical risk factors and incident fractures were eligible.

RESULTS

Of the 836 records retrieved, 53 were selected for full-text assessment after screening on title and abstract. Twelve cohorts were deemed eligible and of these, 4 novel cohorts were identified. These cohorts, together with 60 previously identified cohorts, will provide the resource for constructing an updated version of FRAX comprising 2,138,428 participants with a follow-up of approximately 20 million person-years and 116,117 documented incident major osteoporotic fractures. For each known and candidate risk factor, multivariate hazard functions for hip fracture, major osteoporotic fracture and death will be tested using extended Poisson regression. Sex- and/or ethnicity-specific differences in the weights of the risk factors will be investigated. After meta-analyses of the cohort-specific beta coefficients for each risk factor, models comprising 10-year probability of hip and major osteoporotic fracture, with or without femoral neck bone mineral density, will be computed.

CONCLUSIONS

These assembled cohorts and described models will provide the framework for an updated FRAX tool enabling enhanced assessment of fracture risk (PROSPERO (CRD42021227266)).

Risk assessment tools for osteoporosis and fractures in 2022

Osteoporosis is one of the frequently encountered non-communicable diseases in the world today. Several hundred million people have osteoporosis, with many more at risk. The clinical feature is a fragility fracture (FF), which results in major reductions in the quality and quantity of life, coupled with a huge financial burden. In recognition of the growing importance, the World Health Organisation established a working group 30 years ago tasked with providing a comprehensive report to understand and assess the risk of osteoporosis in postmenopausal women. Dual-energy X-ray absorptiometry (DXA) is the most widely endorsed technology for assessing the risk of fracture or diagnosing osteoporosis before a fracture occurs, but others are available. In clinical practice, important distinctions are essential to optimise the use of risk assessments. Traditional tools lack specificity and were designed for populations to identify groups at higher risk using a 'one-size-fits-all' approach. Much has changed, though the purpose of risk assessment tools remains the same. In 2022, many tools are available to aid the identification of those most at risk, either likely to have osteoporosis or suffer the clinical consequence. Modern technology, enhanced imaging, proteomics, machine learning, artificial intelligence, and big data science will greatly advance a more personalised risk assessment into the future. Clinicians today need to understand not only which tool is most effective and efficient for use in their practice, but also which tool to use for which patient and for what purpose. A greater understanding of the process of risk assessment, deciding who should be screened, and how to assess fracture risk and prognosis in older men and women more comprehensively will greatly reduce the burden of osteoporosis for patients, society, and healthcare systems worldwide. In this paper, we review the current status of risk assessment, screening and best practice for osteoporosis, summarise areas of uncertainty, and make some suggestions for future developments, including a more personalised approach for individuals.

A new prognostic nomogram for heterotopic ossification formation after elbow trauma : the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction (STEHOP) model

AIMS

Heterotopic ossification (HO) is a common complication after elbow trauma and can cause severe upper limb disability. Although multiple prognostic factors have been reported to be associated with the development of post-traumatic HO, no model has yet been able to combine these predictors more succinctly to convey prognostic information and medical measures to patients. Therefore, this study aimed to identify prognostic factors leading to the formation of HO after surgery for elbow trauma, and to establish and validate a nomogram to predict the probability of HO formation in such particular injuries.

METHODS

This multicentre case-control study comprised 200 patients with post-traumatic elbow HO and 229 patients who had elbow trauma but without HO formation between July 2019 and December 2020. Features possibly associated with HO formation were obtained. The least absolute shrinkage and selection operator regression model was used to optimize feature selection. Multivariable logistic regression analysis was applied to build the new nomogram: the Shanghai post-Traumatic Elbow Heterotopic Ossification Prediction model (STEHOP). STEHOP was validated by concordance index (C-index) and calibration plot. Internal validation was conducted using bootstrapping validation.

RESULTS

Male sex, obesity, open wound, dislocations, late definitive surgical treatment, and lack of use of non-steroidal anti-inflammatory drugs were identified as adverse predictors and incorporated to construct the STEHOP model. It displayed good discrimination with a C-index of 0.80 (95% confidence interval 0.75 to 0.84). A high C-index value of 0.77 could still be reached in the internal validation. The calibration plot showed good agreement between nomogram prediction and observed outcomes.

CONCLUSION

The newly developed STEHOP model is a valid and convenient instrument to predict HO formation after surgery for elbow trauma. It could assist clinicians in counselling patients regarding treatment expectations and therapeutic choices. Cite this article: Bone Joint J 2022;104-B(8):963-971.

Comparison of fracture risk calculators in elderly fallers: a hospital-based cross-sectional study

OBJECTIVE

Elderly patients presenting with falls are known to carry an extremely high risk of future fragility fractures. Current osteoporosis guidelines recommend using fracture risk calculators such as FRAX, QFracture or Garvan to guide management. However, they differ considerably in their inputs and may therefore provide contrasting risk estimations in certain individuals. In this study, we compare these risk calculators in a high-risk cohort of elderly patients admitted to hospital with falls.

DESIGN

Hospital-based cross-sectional study.

SETTING

Secondary care, London, UK.

PARTICIPANTS

Data from 120 consecutive elderly patients who had falls presenting to a single hospital over 4 months were collected. 10-year major and hip fracture risks were calculated using FRAX, QFracture and Garvan. 1-year major and hip fracture risks from QFracture were assessed against prospective incidence of fracture.

RESULTS

Median 10-year major fracture risk was: FRAX 19.5%, QFracture 26.0%, Garvan 32.5%. Median 10-year hip fracture risk was: FRAX 9.6%, QFracture 21.1%, Garvan 6.5%. Correlation between FRAX and QFracture was r=0.672 for major, r=0.676 for hip fracture (both p<0.0001); FRAX and Garvan r=0.778 (p<0.0001) for major, r=0.128 (p=0.206) for hip fracture; QFracture and Garvan r=0.658 (p<0.0001) for major, r=0.318 (p<0.001) for hip fracture. QFracture 1-year predicted major and hip fracture rates were 1.8% and 1.2%, respectively, compared with actual rates of 2.1% and 0%, respectively.

CONCLUSIONS

Although strong correlations between calculators were observed in the study cohort, there were differences of up to 13% between estimated risks. QFracture captured several elderly-specific inputs not considered by other calculators and so projected higher fracture risk than the other calculators. QFracture provided 1-year fracture risks that were comparable with the prospective observed fracture incidence in the cohort. This study has important clinical implications for the use of fracture risk calculators to guide treatment decisions, particularly in the high-risk cohort of elderly patients admitted to hospital following falls.

Performance of the Garvan Fracture Risk Calculator in Individuals with Diabetes: A Registry-Based Cohort Study

Diabetes increases fracture and falls risks. We evaluated the performance of the Garvan fracture risk calculator (FRC) in individuals with versus without diabetes. Using the population-based Manitoba bone mineral density (BMD) registry, we identified individuals aged 50-95 years undergoing baseline BMD assessment from 1 September 2012, onwards with diabetes and self-reported falls in the prior 12 months. Five-year Garvan FRC predictions were generated from clinical risk factors, with and without femoral neck BMD. We identified non-traumatic osteoporotic fractures (OF) and hip fractures (HF) from population-based data to 31 March 2018. Fracture risk stratification was assessed from area under the receiver operating characteristic curves (AUROC). Cox regression analysis was performed to examine the effect of diabetes on fractures, adjusted for Garvan FRC predictions. The study population consisted of 2618 women with and 14,064 without diabetes, and 636 and 2201 men with and without the same, respectively. The Garvan FRC provided significant OF and HF risk stratification in women with diabetes, similar to those without diabetes. Analyses of OF in men were limited by smaller numbers; no significant difference was evident by diabetes status. Cox regression showed that OF risk was 23% greater in women with diabetes adjusted for Garvan FRC including BMD (hazard ratio [HR] 1.23, 95% confidence interval [CI] 1.01-1.49), suggesting it slightly underestimated risk; a non-significant increase in diabetes-related HF risk was noted (HR 1.37, 95% CI 0.88-2.15). Garvan FRC shows similar fracture risk stratification in individuals with versus without diabetes, but may underestimate this risk.

Osteoporosis in men

Osteoporosis in men is a common but often overlooked disorder by clinicians. The criterion for osteoporosis diagnosis in men is similar to that in women-namely, a bone mineral density (BMD) that is 2·5 standard deviations or more below the mean for the young adult population (aged 20-29 years; T-score -2·5 or lower), measured at the hip or lumbar spine. Sex steroids are important for bone health in men and, as in women, oestrogens have a key role. Most men generally have bigger and stronger bones than women and typically have less bone loss during their lifetime. Men typically fracture less often than women, although they have a higher mortality rate after a fracture. Secondary osteoporosis is more common in men than in women. Lifestyle changes, adequate calcium, vitamin D intake, and exercise programmes are recommended for the management of osteoporosis in men. Bisphosphonates, denosumab, and teriparatide have been shown to increase BMD and are used for pharmacological treatment. In this Review, we report an updated overview of osteoporosis in men, describe new treatments and concepts, and discuss persistent controversies in the area.

Digital health interventions for osteoporosis and post-fragility fracture care

The growing burden from osteoporosis and fragility fractures highlights a need to improve osteoporosis management across healthcare systems. Sub-optimal management of osteoporosis is an area suitable for digital health interventions. While fracture liaison services (FLSs) are proven to greatly improve care for people with osteoporosis, such services might benefit from technologies that enhance automation. The term 'Digital Health' covers a variety of different tools including clinical decision support systems, electronic medical record tools, patient decision aids, patient apps, education tools, and novel artificial intelligence (AI) algorithms. Within the scope of this review are AI solutions that use algorithms within health system registries to target interventions. Clinician-targeted, patient-targeted, or system-targeted digital health interventions could be used to improve management and prevent fragility fractures. This review was commissioned by The Royal Osteoporosis Society and Bone Research Academy during the production of the 2020 Research Roadmap (https://theros.org.uk), with the intention of identifying gaps where targeted research funding could lead to improved patient health. We explore potential uses of digital technology in the general management of osteoporosis. Evidence suggests that digital technologies can support multidisciplinary teams to provide the best possible patient care based on current evidence and to support patients in self-management. However, robust randomised controlled studies are still needed to assess the effectiveness and cost-effectiveness of these technologies.

Muscle Strength and Physical Performance Improve Fracture Risk Prediction Beyond Garvan and FRAX: The Osteoporotic Fractures in Men (MrOS) Study

Muscle strength and physical performance are associated with fracture risk in men. However, it is not known whether these measurements enhance fracture prediction beyond Garvan and FRAX tools. A total of 5665 community-dwelling men, aged ≥65 years, from the Osteoporotic Fractures in Men (MrOS) Study, who had data on muscle strength (grip strength) and physical performance (gait speed and chair stand tests), were followed from 2000 to 2019 for any fracture, major osteoporotic fracture (MOF), initial hip, and any hip fracture. The contributions to different fracture outcomes were assessed using Cox's proportional hazard models. Tool-specific analysis approaches and outcome definitions were used. The added predictive values of muscle strength and physical performance beyond Garvan and FRAX were assessed using categorical net reclassification improvement (NRI) and relative importance analyses. During a median follow-up of 13 (interquartile range 7-17) years, there were 1014 fractures, 536 MOFs, 215 initial hip, and 274 any hip fractures. Grip strength and chair stand improved prediction of any fracture (NRI for grip strength 3.9% and for chair stand 3.2%) and MOF (5.2% and 6.1%). Gait speed improved prediction of initial hip (5.7%) and any hip (7.0%) fracture. Combining grip strength and the relevant performance test further improved the models (5.7%, 8.9%, 9.4%, and 7.0% for any, MOF, initial, and any hip fractures, respectively). The improvements were predominantly driven by reclassification of those with fracture to higher risk categories. Apart from age and femoral neck bone mineral density, muscle strength and performance were ranked equal to or better than the other risk factors included in fracture models, including prior fractures, falls, smoking, alcohol, and glucocorticoid use. Muscle strength and performance measurements improved fracture risk prediction in men beyond Garvan and FRAX. They were as or more important than other established risk factors. These measures should be considered for inclusion in fracture risk assessment tools. © 2021 American Society for Bone and Mineral Research (ASBMR).

Predictive performance of the Garvan Fracture Risk Calculator: a registry-based cohort study

The G arvan Fracture Risk Calculator predicts risk of osteoporotic fractures. We evaluated its predictive performance in 16,682 women and 2839 men from Manitoba, Canada, and found significant risk stratification, with a strong gradient across scores. The tool outperformed clinical risk factors and bone mineral density for fracture risk stratification.

INTRODUCTION

The optimal model for fracture risk estimation to guide treatment decision-making remains controversial. Our objective was to evaluate the predictive performance of the Garvan Fracture Risk Calculator (FRC) in a large clinical registry from Manitoba, Canada.

METHODS

Using the population-based Manitoba Bone Mineral Density (BMD) registry, we identified women and men aged 50-95 years undergoing baseline BMD assessment from September 1, 2012, onwards. Five-year Garvan FRC predictions were generated from clinical risk factors (CRFs) with and without femoral neck BMD. We identified incident non-traumatic osteoporotic fractures (OFs) and hip fractures (HFs) from population-based healthcare data sources to March 31, 2018. Fracture risk was assessed from area under the receiver operating characteristic curve (AUROC). Cox regression analysis and calibration ratios (5-year observed/predicted) were assessed for risk quintiles. All analyses were sex stratified.

RESULTS

We included 16,682 women (mean age 66.6 + / - SD 8.7 years) and 2839 men (mean age 68.7 + / - SD 10.2 years). During a mean observation time of 2.6 years, incident OFs were identified in 681 women and 140 men and HFs in 199 women and 22 men. AUROC showed significant fracture risk stratification with the Garvan FRC. Tool predictions without BMD were better than from age or decreasing weight, and the tool with BMD performed better than BMD alone. Garvan FRC with BMD performed better than without BMD, especially for HF prediction (AUROC 0.86 in women, 0.82 in men). There was a strong gradient of increasing risk across Garvan FRC quintiles (highest versus lowest, hazard ratios women 5.75 and men 3.43 for any OF; women 101.6 for HF). Calibration differences were noted, with both over- and underestimation in risk.

CONCLUSIONS

Garvan FRC outperformed CRFs and BMD alone for fracture risk stratification, particularly for HF, but may require recalibration for accurate predictions in this population.

Fracture risk assessment by the FRAX model

The introduction of the FRAX algorithms has facilitated the assessment of fracture risk on the basis of fracture probability. FRAX integrates the influence of several well-validated risk factors for fracture with or without the use of bone mineral density. Since age-specific rates of fracture and death differ across the world, FRAX models are calibrated with regard to the epidemiology of hip fracture (preferably from national sources) and mortality (usually United Nations sources). Models are currently available for 73 nations or territories covering more than 80% of the world population. FRAX has been incorporated into more than 80 guidelines worldwide, although the nature of this application has been heterogeneous. The limitations of FRAX have been extensively reviewed. Arithmetic procedures have been proposed in order to address some of these limitations, which can be applied to conventional FRAX estimates to accommodate knowledge of dose exposure to glucocorticoids, concurrent data on lumbar spine bone mineral density, information on trabecular bone score, hip axis length, falls history, type 2 diabetes, immigration status and recency of prior fracture.

Glucocorticoids Increase Fracture Risk and Fracture Prevalence Independently from Bone Mineral Density and Clinical Risk Factors: Results from the Gliwice Osteoporosis (GO) Study

The aim of the study was to establish the influence of glucocorticoids (GC) on fracture risk, probability, and prevalence. A set of 1548 postmenopausal women were divided into study group - treated with GC (n=114, age 66.48±7.6 years) and controls (n=1434, age 66.46±6.83 years). Data on clinical risk factors for osteoporosis and fractures were collected. Hip bone densitometry was performed using a device Prodigy (GE, USA). Fracture probability was established by FRAX, and fracture risk by Garvan algorithm and POL-RISK. Fracture risk and fracture probability were significantly greater for GC-treated women in comparison to controls. In the study group, there were 24, 3, 24, and 6 fractures noted at spine, hip, forearm, and arm, respectively. The respective numbers of fractures reported in controls at those skeletal sites were: 186, 23, 240, and 25. The use of GCs increased significantly prevalence of all major, spine and arm fractures. Also the number of all fractures was affected by GC use. Following factors significantly increased fracture probability: age (OR 1.04 per each year; 95% CI: 1.03-1.06), GC use (OR 1.54; 95% CI: 1.03-2.31), falls (OR 2.09; 95% CI: 1.60-2.73), and FN T-score (OR 0.62 per each unit; 95% CI: 0.54-0.71). In conclusion, in patients treated with GCs the fracture risk, probability, and prevalence were increased. This effect was evident regardless of whether GC therapy is included in the algorithm as a risk factor (FRAX, POL-RISK) or not taken into consideration (Garvan nomogram).

Conceptual design of the dual X-ray absorptiometry health informatics prediction system for osteoporosis care

Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.

Prediction of imminent fracture risk in Canadian women and men aged 45 years or older: external validation of the Fracture Risk Evaluation Model (FREM)

The Fracture Risk Evaluation Model (FREM) identifies individuals at high imminent risk of major osteoporotic fractures. We validated FREM on 74,828 individuals from Manitoba, Canada, and found significant fracture risk stratification for all FREM scores. FREM performed better than age alone but not as well as FRAX® with BMD.

INTRODUCTION

The FREM is a tool developed from Danish public health registers (hospital diagnoses) to identify individuals over age 45 years at high imminent risk of major osteoporotic fractures (MOF) and hip fracture (HF). In this study, our aim was to examine the ability of FREM to identify individuals at high imminent fracture risk in women and men from Manitoba, Canada.

METHODS

We used the population-based Manitoba Bone Mineral Density (BMD) Program registry, and identified women and men aged 45 years or older undergoing baseline BMD assessment with 2 years of follow-up data. From linked population-based data sources, we constructed FREM scores using up to 10 years of prior healthcare information.

RESULTS

The study population comprised 74,828 subjects, and during the 2 years of observation, 1612 incident MOF and 299 incident HF occurred. We found significant fracture risk stratification for all FREM scores, with AUC estimates of 0.63-0.66 for MOF for both sexes and 0.84 for women and 0.65-0.67 for men for HF. FREM performed better than age alone but not as well as FRAX® with BMD. The inclusion of physician claims data gave slightly better performance than hospitalization data alone. Overall calibration for 1-year MOF prediction was reasonable, but HF prediction was overestimated.

CONCLUSION

In conclusion, the FREM algorithm shows significant fracture risk stratification when applied to an independent clinical population from Manitoba, Canada. Overall calibration for MOF prediction was good, but hip fracture risk was systematically overestimated indicating the need for recalibration.

Prediction of Osteoporotic Fractures in Elderly Individuals: A Derivation and Internal Validation Study Using Healthcare Administrative Data

In Canada and other countries, osteoporosis is monitored as part of chronic disease population surveillance programs. Although fractures are the principal manifestation of osteoporosis, very few algorithms are available to identify individuals at high risk of osteoporotic fractures in current surveillance systems. The objective of this study was to derive and validate predictive models to accurately identify individuals at high risk of osteoporotic fracture using information available in healthcare administrative data. More than 270,000 men and women aged ≥66 years were randomly selected from the Quebec Integrated Chronic Disease Surveillance System. Selected individuals were followed between fiscal years 2006-2007 and 2015-2016. Models were constructed for prediction of hip/femur and major osteoporotic fractures for follow-up periods of 5 and 10 years. A total of 62 potential predictors measurable in healthcare administrative databases were identified. Predictor selection was performed using a manual backward algorithm. The predictive performance of the final models was assessed using measures of discrimination, calibration, and overall performance. Between 20 and 25 predictors were retained in the final prediction models (eg, age, sex, social deprivation index, most of the major and minor risk factors for osteoporosis, diabetes, Parkinson's disease, cognitive impairment, anemia, anxio-depressive disorders). Discrimination of the final models was higher for the prediction of hip/femur fracture than major osteoporotic fracture and higher for prediction for a 5-year than a 10-year period (hip/femur fracture for 5 years: c-index = 0.77; major osteoporotic fracture for 5 years: c-index = 0.71; hip/femur fracture for 10 years: c-index = 0.73; major osteoporotic fracture for 10 years: c-index = 0.68). The predicted probabilities globally agreed with the observed probabilities. In conclusion, the derived models had adequate predictive performance in internal validation. As a final step, these models should be validated in an external cohort and used to develop indicators for surveillance of osteoporosis. © 2021 American Society for Bone and Mineral Research (ASBMR).

Risk factors for injuries in New Zealand older adults with complex needs: a national population retrospective study

BACKGROUND

Falls and falls-related injuries are common among older adults. Injuries in older adults lead to poor outcomes and lower quality of life. The objective of our study was to identify factors associated with fall-related injuries among home care clients in New Zealand.

METHODS

The study cohort consisted of 75,484 community-dwelling people aged 65 years or older who underwent an interRAI home care assessment between June 2012 and June 2018 in New Zealand. The injuries included for analysis were fracture of the distal radius, hip fracture, pelvic fracture, proximal humerus fracture, subarachnoid haemorrhage, traumatic subdural haematoma, and vertebral fracture. Unadjusted and adjusted competing risk regression models were used to identify factors associated with fall-related injuries.

RESULTS

A total of 7414 (9.8%) people sustained a falls-related injury over the 6-year period, and most injuries sustained were hip fractures (4735 63.9%). The rate of injurious falls was 47 per 1000 person-years. The factors associated with injury were female sex, older age, living alone, Parkinson's disease, stroke/CVA, falls, unsteady gait, tobacco use, and being underweight. Cancer, dyspnoea, high BMI, and a decrease in the amount of food or fluid usually consumed, were associated with a reduced risk of sustaining an injury. After censoring hip fractures the risks associated with other types of injury were sex, age, previous falls, dyspnoea, tobacco use, and BMI.

CONCLUSIONS

While it is important to reduce the risk of falls, it is especially important to reduce the risk of falls-related injuries. Knowledge of risk factors associated with these types of injuries can help to develop focused intervention programmes and development of a predictive model to identify those who would benefit from intervention programmes.

Height loss in postmenopausal women-do we need more for fracture risk assessment? Results from the GO Study

Human body height loss of 3-4 cm or more may be considered a simple indicator of increasing fracture risk, where the information is very similar to the results from fracture risk assessments by available online calculators, all of them based on a multiple variable approaches.

INTRODUCTION

The aim of the study was to assess the relationship between body height loss (HL) and fracture risk in postmenopausal women from the Gliwice Osteoporosis (GO) Study.

METHODS

The study sample included 1735 postmenopausal women, aged over 55 years and recruited at the Osteoporotic Outpatient Clinic. The mean age of the study participants was 68.15 ± 8.16 years. Fracture risk was established, using the fracture risk assessment tool (FRAX) (10-year probability of major and hip fractures), the Garvan calculator (any and hip fractures, 5 and 10 years) and the Polish (POL-RISK) algorithm, available at www. fracture - risk .pl (any fractures, 5 years). Bone densitometry at the femoral neck was performed, using a Prodigy device (Lunar, GE, USA). Body heights were measured before bone densitometry, using a wall stadiometer and compared with the maximum body heights, measured in early adulthood and reported by the study participants themselves.

RESULTS

In 199 women, the body heights, measured during the study, did not change in comparison to their corresponding values in early adulthood, while being decreased in the other 1536 women. The mean height loss (HL) in the whole study group was 3.95 ± 3.24 cm. That HL correlated significantly with the calculated fracture risk (the r range from 0.13 to 0.39, p < 0.0001). In general, regarding the patients with fracture risk close to the recommended therapeutic thresholds, HL was around 3-4 cm, except of the values from the FRAX calculator for major fractures, where the commonly used therapeutic threshold (20%) was related to HL of approximately 6.5 cm. In subjects with HL between 3.5 and 4 cm (n = 208), the FRAX value for major fractures was 6.83 ± 3.74.

CONCLUSIONS

Body height measurements, carried out to establish HL, provide an important information for clinical practice, where HL of 3-4 cm or more may be considered a simple indicator of increasing fracture risk.

Personalized fracture risk assessment: where are we at?

Introduction: Osteoporotic fracture imposes a significant health care burden globally. Personalized assessment of fracture risk can potentially guide treatment decisions. Over the past decade, a number of risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX®, have been developed and implemented in clinical practice. Areas covered: This article reviews recent development and validation results concerning the prognostic performance of the two tools. The main areas of review are the need for personalized fracture risk prediction, purposes of risk prediction, predictive performance in terms of discrimination and calibration, concordance between the Garvan and FRAX tools, genetic profiling for improving predictive performance, and treatment thresholds. In some validation studies, FRAX tended to underestimate fracture by as high as 50%. Studies have shown that the predicted risk from the Garvan tool is highly concordant with clinical decision. Expert opinion: Although there are some discrepancy in fracture risk prediction between Garvan and FRAX, both tools are valid and can aid patients and doctors communicate about risk and make informed decision. The ideal of personalized risk assessment for osteoporosis patients will be realized through the incorporation of genetic profiling into existing fracture risk assessment tools.

Burden of osteoporosis in Vietnam: An analysis of population risk

Purpose

To estimate the proportion of men and women aged 50 years and older who would be classified as "high risk" for fracture and eligible for anti-fracture treatment.

Methods

The study involved 1421 women and 652 men aged 50 years and older, who were recruited from the general population in Ho Chi Minh City, Vietnam. Fracture history was ascertained from each individual. Bone mineral density (BMD) was measured at the lumbar spine and femoral neck by DXA (Hologic Horizon). The diagnosis of osteoporosis was based on the T-scores ≤ -2.50 derived from either femoral neck or lumbar spine BMD. The 10-year risks of major fractureand hip fracture were estimated from FRAX version for Thai population. The criteria for recommended treatment were based on the US National Osteoporosis Foundation (NOF).

Results

The average age of women and men was ~60 yr (SD 7.8). Approximately 11% (n = 152) of women and 14% (n = 92) of men had a prior fracture. The prevalence of osteoporosis was 27% (n = 381; 95% CI, 25 to 29%) in women and 13% (n = 87; 95% CI, 11 to 16%) in men. Only 1% (n = 11) of women and 0.1% (n = 1) of men had 10-year risk of major fracture ≥ 20%. However, 23% (n = 327) of women and 9.5% (n = 62) of men had 10-year risk of hip fracture ≥ 3%. Using the NOF recommended criteria, 49% (n = 702; 95% CI, 47 to 52%) of women and 35% (n = 228; 95% CI, 31 to 39%) of men would be eligible for therapy.

Conclusion

Almost half of women and just over one-third of men aged 50 years and older in Vietnam meet the NOF criteria for osteoporosis treatment. This finding can help develop guidelines for osteoporosis treatment in Vietnam.

Validation of the Fracture Risk Evaluation Model (FREM) in predicting major osteoporotic fractures and hip fractures using administrative health data

BACKGROUND

Prevention of osteoporotic fractures remains largely insufficient, and effective means to identify patients at high, short-term fracture risk are needed. The FREM tool is available for automated case finding of men and women aged 45 years or older at high imminent (1-year) risk of osteoporotic fractures, based on administrative health data with a 15-year look-back. The aim of this study was to validate the performance of FREM, and the effect of applying a shorter look-back period. We also evaluated FREM for 5-year fracture risk prediction.

METHODS

Using Danish national health registers we generated consecutive general population cohorts for the years 2014 through 2018. Within each year and across the full time period we estimated the individual fracture risk scores and determined the actual occurrence of major osteoporotic fractures (MOF) and hip fractures. Risk scores were calculated with 15- and 5-year look-back periods. The discriminative ability was evaluated by area under the receiver operating curve (AUC), and negative predictive value (NPV) and positive predictive value (PPV) were estimated applying a calculated risk cut-off of 2% for MOF and 0.3% for hip fractures.

RESULTS

Applying a 15-year look-back, AUC was around 0.75-0.76 for MOF and 0.84-0.87 for hip fractures in 2014, with minor decreases in the subsequent fracture cohorts (2015 to 2018). Applying a 5-year look-back generated similar results, with only marginally lower AUC. In the 5-year risk prediction setting, AUC-values were 0.70-0.72 for MOF and 0.81-0.84 for hip fractures. Generally, PPVs were low, while NPVs were very high.

CONCLUSION

FREM predicts the 1- and 5-year risk of MOF and hip fractures with acceptable vs excellent discriminative power, respectively, when applying both a 15- and a 5-year look-back. Hence, the FREM tool may be applied to improve identification of individuals at high imminent risk of fractures using administrative health data.

Management of osteoporosis in older men

As many as one out of three fragility fractures occur in older men and the outcome of major osteoporotic fractures, in particular hip fractures, is worse in men than in women. Osteoporosis in older men is thus an important threat to the quality of life of individual patients and a considerable burden for society. However, only a small minority of older men with high or very high fracture risk are receiving therapy. This does not need to be so as tools for fracture risk assessment are available and several drugs have been approved for treatment. Nevertheless, the evidence base for the management of osteoporosis in older men remains limited. This narrative review summarises the evidence for older men on the burden of osteoporosis, the pathophysiology of fragility fractures, the clinical presentation, diagnosis and risk assessment, the patient evaluation, and the non-pharmacological and pharmacological management.

The Utility of Genetic Risk Score to Improve Performance of FRAX for Fracture Prediction in US Postmenopausal Women

The ability of the fracture risk assessment tool (FRAX) in discriminating fracture and non-fracture in postmenopausal women remains suboptimal. Adding a genetic profile may improve the performance of FRAX. Three genetic risk scores (GRSs) (GRS_fracture, GRS_BMD, GRS_eBMD) were calculated for each participant in the Women's Health Initiative Study (n = 23,981), based on the summary statistics of three comprehensive osteoporosis-related genome-wide association studies (GWAS). The primary outcomes were incident major osteoporotic fracture (MOF) and hip fracture (HF). The association between each GRS and fracture risk were evaluated in separate Cox Proportional Hazard models, with FRAX clinical risk factors adjusted for. The discrimination ability of each model was assessed using Area Under the Curve (AUC). The predictive improvement attributable to each GRSs was assessed using the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI). GRS_BMD and GRS_eBMD were significantly associated with MOF and HF risk, independent of the base FRAX risk factors. Compare to the base FRAX model, the models with GRS_fracture, GRS_BMD, and GRS_eBMD improved the reclassification of MOF by 0.5% (95% CI, 0.2% to 0.9%, p = p < .01), 0.3% (95% CI, 0.1% to 0.6%, p = 0.01), and 2.1% (95% CI, 0.3% to 2.8%, p < .01), respectively. Similar results were also observed when using HF as an outcome. Our study suggested that the addition of genetic profiles provide limited improvements in the reclassification of FRAX for MOF and HF.

Approaches to Fracture Risk Assessment and Prevention

PURPOSE OF REVIEW

To critically assess recent evidence concerning osteoporosis fracture risk.

RECENT FINDINGS

Robust instruments exist for predicting factures incorporating well-documented risk factors especially prior fracture whose magnitude varies with site, occurrence time, and age. Stratifying time-since-prior fracture has resulted in the concept of imminent fracture risk and increased focus on secondary fracture prevention. Secondary fracture prevention recommendations include fracture liaison service, pharmacologic and non-pharmacologic multidisciplinary intervention, and communicating that fractures in older adults are the predictable consequence of underlying osteoporosis rather than unfortunate accidents. Quality improvement in osteoporosis care includes diagnosing osteoporosis on the basis of clinical fractures rather than exclusively relying on bone density testing; applying diagnostic rather than screening approaches to patients with prior fractures; regularly updating fall and fracture histories; performing a physical exam focused on spinal curvature, posture, and musculoskeletal function; reviewing images to identify prevalent fractures that may have been missed; and general use of fracture risk algorithms at all stages of osteoporosis management. Communicating effectively with patients about osteoporosis and fractures, their consequences, and pharmacological and non-pharmacological management is the cornerstone of high-value care.

Epidemiological transition to mortality and refracture following an initial fracture

This study sought to redefine the concept of fracture risk that includes refracture and mortality, and to transform the risk into "skeletal age". We analysed data obtained from 3521 women and men aged 60 years and older, whose fracture incidence, mortality, and bone mineral density (BMD) have been monitored since 1989. During the 20-year follow-up period, among 632 women and 184 men with a first incident fracture, the risk of sustaining a second fracture was higher in women (36%) than in men (22%), but mortality risk was higher in men (41%) than in women (25%). The increased risk of mortality was not only present with an initial fracture, but was accelerated with refractures. Key predictors of post-fracture mortality were male gender (hazard ratio [HR] 2.4; 95% CI, 1.79–3.21), advancing age (HR 1.67; 1.53–1.83), and lower femoral neck BMD (HR 1.16; 1.01–1.33). A 70-year-old man with a fracture is predicted to have a skeletal age of 75. These results were incorporated into a prediction model to aid patient-doctor discussion about fracture vulnerability and treatment decisions.

Five-year fracture risk assessment in postmenopausal women, using both the POL-RISK calculator and the Garvan nomogram: the Silesia Osteo Active Study

The study project was designed to assess the concordance of clinical results in the assessment of 5-year fracture risk of any fracture, carried out by two methods: the Garvan algorithm and the POL-RISK model. The study group included 389 postmenopausal women of Caucasian race. The concordance of results, obtained by those two models, turned out to be moderate, and the threshold for high fracture risk group was 11% in the POL-RISK model.

PURPOSE

The goal of the study was to evaluate the concordance of results in fracture risk assessments between the Garvan Fracture Risk Calculator and POL-RISK, a new Polish algorithm, and to define an optimal threshold for intervention.

METHODS

The study was a part of the Silesia Osteo Active Study. A group of 389 postmenopausal women, aged 65.2±6.9 years (mean ± SD), was randomly selected from the general population of Zabrze, Poland. All the participants had bone densitometry examination to assess the bone mineral density of the femoral neck. The mean femoral neck T-score was (-0.99) ± 1.05 SD. 6.4% of the women revealed osteoporosis. Five-year risk of any fracture was assessed, using the Garvan and POL-RISK calculators. The performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC).

RESULTS

The median 5-year risk of any fracture was 7% (range 1-54%) in the Garvan model and 8.8% (range 1.1-45.5%) in the POL-RISK algorithm. There was a significant correlation between the results obtained by both methods (r=0.6, p<0.005). For the thresholds, assumed at 8% and 13% (according to recommendation derived from Garvan tool), the rates of concordance of results between both calculators were 76% and 84%, respectively. In ROC analysis for the POL-RISK method, performed with reference to the Garvan method at two different cut-offs, assumed to be high fracture risk indicators (8% and 13%), the AUC values were 0.865 and 0.884, respectively. The optimal threshold for high fracture risk in the POL-RISK algorithm was ≥ 11%, which yielded a sensitivity of 0.94 and a specificity of 0.71.

CONCLUSION

The obtained data demonstrate a moderate concordance of results between the POL-RISK algorithm and the Garvan model, illustrated by low and high fracture risk cut-offs, established in ROC analysis. In addition, the threshold of 11% in the POL-RISK method was the optimal level for "high risk".

Predictors of Fracture in Older Women With Osteopenic Hip Bone Mineral Density Treated With Zoledronate

A recent analysis has found that during treatment with denosumab, women attaining higher bone densities (BMD) are less likely to have incident fractures. We have reexamined this important question using data from our recent trial of zoledronate in osteopenic women. One thousand women randomized to treatment with zoledronate were followed for 6 years. Of those, 122 sustained fragility fractures during follow-up. Baseline age, nonvertebral fracture history, total hip BMD, and calculated fracture risk were all significantly different between those who had fractures during the study and those who did not. BMDs achieved during the study were higher in those without incident fractures. However, achieved BMDs were very closely related to baseline values (r = 0.93, p < 0.0001). The increase in BMD during zoledronate treatment was not different between those who had incident fractures and those who did not (0.15 < p < 0.78), and change in BMD was not predictive of fracture (univariate logistic regression analysis). Stepwise regression analysis of all baseline variables showed the best independent predictors of fracture to be age (odds ratio [OR] = 1.08, 95% confidence interval [CI] 1.04-1.13, p = 0.0003), baseline spine BMD (OR = 0.81, 95% CI 0.67-0.96, p = 0.016), and history of nonvertebral fracture (OR = 1.69, 95% CI 1.06-2.69, p = 0.028). Addition of change in BMD to this model did not improve its predictive power. If changes in BMD were included in the stepwise regression analysis of baseline variables, they did not emerge as significant predictors of fracture. It is concluded that age, fracture history, and baseline BMD determine the risk of new fractures. Differences in achieved BMD between those who do or do not fracture arise from the close relationship between baseline and achieved BMDs. These findings suggest that targeting any particular BMD during treatment is unlikely to be a useful or valid strategy. © 2020 American Society for Bone and Mineral Research (ASBMR).

A Simple-to-Use Score for Identifying Individuals at High Risk of Denosumab-Associated Hypocalcemia in Postmenopausal Osteoporosis: A Real-World Cohort Study

Since denosumab-associated hypocalcemia occurs infrequently, data on its incidence and risk factors are limited. We aimed to evaluate risk factors and develop a useful score for identifying individuals at risk of denosumab-associated hypocalcemia. In this retrospective cohort, 790 consecutive female patients who received 60 mg denosumab at least once between 2016 and 2017 were analyzed. Based on biochemical records from a large-scale single-center, mild and moderate hypocalcemia were defined as albumin-corrected calcium (cCa) levels < 8.5 and < 8.0 mg/dL (< 2.12 and < 2.0 mmol/L), respectively. Mild and moderate hypocalcemia were observed in 8.2% and 1.0% patients, respectively. Patients who developed mild hypocalcemia had lower baseline cCa (8.9 vs. 9.3 mg/dL and 2.22 vs. 2.32mmo/L) and estimated glomerular filtration rate (75.0 vs. 83.2 mL/min/1.73 m²) and more frequent loop diuretic use (10.8% vs. 4.4%; all p < 0.05). In multivariate analysis, low baseline cCa (OR 1.29; 95% CI 1.20-1.40) and chronic kidney disease (CKD) stages 3b-5 were associated with elevated mild hypocalcemia risk (OR 2.92; 95% CI 1.38-6.20). Loop diuretics use was associated with mild hypocalcemia (OR 2.61; 95% CI 1.11-6.18) by univariate analysis, independent of baseline cCa and CKD stage. A scoring approach identified two risk groups: (1) patients without CKD (eGFR ≥ 45) and cCa < 8.5 mg/dL (2.12 mmol/L) and (2) patients with CKD (eGFR < 45) and cCa < 9.5 mg/dL (2.37 mmol/L).

Comparison of screening tools for optimizing fracture prevention in Canada

The best screening strategy to identify treatment qualification based upon indicators of high fracture risk (low-trauma fractures of the hip, spine, or multiple fracture episodes at other sites; high fracture probability with the Canadian fracture risk assessment [FRAX®] tool major osteoporotic fracture [MOF] computed with bone mineral density [BMD] > 20%; or vertebral fracture on vertebral fracture assessment [VFA]) was FRAX-MOF without BMD using a cutoff of ≥ 10%.

PURPOSE

To inform clinical practice guidelines in Canada, we compared multiple screening tools using the population-based Manitoba BMD Program registry.

METHODS

The study populations consisted of (a) 28,906 individuals > 50 years or older, and (b) 15,429 women age > 65 years undergoing baseline BMD assessment (2010-2018). We considered two treatment qualifications: Treatment Approach 1: prior high-risk fracture, high fracture probability (FRAX-MOF with BMD > 20%), or vertebral fracture on VFA; Treatment Approach 2: Approach 1 or an osteoporotic BMD T score. Candidate screening tools were FRAX-MOF without BMD, age alone, weight alone, SCORE, ORAI, SOFSURF, OSIRIS, ABONE, and OST. Healthcare records were assessed for the presence of incident fracture diagnoses.

RESULTS

Among all individuals, FRAX-MOF without BMD demonstrated the best ability to identify those satisfying Treatment Approach 1 (area under the curve [AUC 0.863]) and was significantly better than all other screening tools (P < 0.001). For identification of individuals satisfying Treatment Approach 2, FRAX-MOF without BMD showed moderate stratification (AUC 0.735), slightly lower than OSIRIS (AUC 0.752, P < 0.05), similar to SCORE (AUC 0.739, P > 0.05) and significantly better than all other screening tools (P < 0.05). For prediction of incident MOF, FRAX-MOF without BMD achieved the highest performance (AUC 0.652), and was significantly better than all other screening tools except OSIRIS. AUCs among women age > 65 years tended to be greater with a similar ranking, and no tool outperformed FRAX-MOF without BMD. Based upon a summary score, the highest ranked strategy was FRAX-MOF without BMD using a cutoff of 10%.

CONCLUSIONS

All screening tools show some ability to identify individuals qualifying for treatment and stratify risk for incident fracture. For treatment based upon indicators of high fracture risk, the best performing strategy was FRAX-MOF without BMD using a cutoff of ≥ 10%.

A Risk Assessment Tool for Predicting Fragility Fractures and Mortality in the Elderly

Existing fracture risk assessment tools are not designed to predict fracture-associated consequences, possibly contributing to the current undermanagement of fragility fractures worldwide. We aimed to develop a risk assessment tool for predicting the conceptual risk of fragility fractures and its consequences. The study involved 8965 people aged ≥60 years from the Dubbo Osteoporosis Epidemiology Study and the Canadian Multicentre Osteoporosis Study. Incident fracture was identified from X-ray reports and questionnaires, and death was ascertained though contact with a family member or obituary review. We used a multistate model to quantify the effects of the predictors on the transition risks to an initial and subsequent incident fracture and mortality, accounting for their complex interrelationships, confounding effects, and death as a competing risk. There were 2364 initial fractures, 755 subsequent fractures, and 3300 deaths during a median follow-up of 13 years (interquartile range [IQR] 7-15). The prediction model included sex, age, bone mineral density, history of falls within 12 previous months, prior fracture after the age of 50 years, cardiovascular diseases, diabetes mellitus, chronic pulmonary diseases, hypertension, and cancer. The model accurately predicted fragility fractures up to 11 years of follow-up and post-fracture mortality up to 9 years, ranging from 7 years after hip fractures to 15 years after non-hip fractures. For example, a 70-year-old woman with a T-score of -1.5 and without other risk factors would have 10% chance of sustaining a fracture and an 8% risk of dying in 5 years. However, after an initial fracture, her risk of sustaining another fracture or dying doubles to 33%, ranging from 26% after a distal to 42% post hip fracture. A robust statistical technique was used to develop a prediction model for individualization of progression to fracture and its consequences, facilitating informed decision making about risk and thus treatment for individuals with different risk profiles. © 2020 American Society for Bone and Mineral Research.

Post-GWAS Polygenic Risk Score: Utility and Challenges

Over the past decade, through genome‐wide association studies, more than 300 genetic variants have been identified to be associated with either BMD or fracture risk. These genetic variants are common in the general population, but they exert small to modest effects on BMD, suggesting that the utility of any single variant is limited. However, a combination of effect sizes from multiple variants in the form of the polygenic risk score (PRS) can provide a useful indicator of fracture risk beyond that obtained by conventional clinical risk factors. In this perspective, we review the progress of genetics of osteoporosis and approaches for creating PRSs, their uses, and caveats. Recent studies support the idea that the PRS, when integrated into existing fracture prediction models, can help clinicians and patients alike to better assess the fracture risk for an individual, and raise the possibility of precision risk assessment. © 2020 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Development and validation of the fracture risk scale home care (FRS-HC) that predicts one-year incident fracture: an electronic record-linked longitudinal cohort study

BACKGROUND

Fractures have dire consequences including pain, immobility, and death. People receiving home care are at higher risk for fractures than the general population. Yet, current fracture risk assessment tools require additional testing and assume a 10-year survival rate, when many die within one year. Our objectives were to develop and validate a scale that predicts one-year incident hip fracture using the home care resident assessment instrument (RAI-HC).

METHODS

This is a retrospective cohort study of linked population data. People receiving home care in Ontario, Canada between April 1st, 2011 and March 31st, 2015 were included. Clinical data were obtained from the RAI-HC which was linked to the Discharge Abstract Database and National Ambulatory Care Reporting System to capture one-year incident hip fractures. Seventy-five percent (n = 238,011) of the sample were randomly assigned to a derivation and 25% (n = 79,610) to a validation sample. A decision tree was created with the derivation sample using known fracture risk factors. The final nodes of the decision tree were collapsed into 8 risk levels and logistic regression was performed to determine odds of having a fracture for each level. c-Statistics were calculated to compare the discriminative properties of the full, derivation, and validation samples.

RESULTS

Approximately 60% of the sample were women and 53% were 80 years and older. A total of 11,526 (3.6%) fractures were captured over the 1-year time period. Of these, 5057 (43.9%) were hip fractures. The proportion who experienced a hip fracture in the next year ranged from 0.3% in the lowest risk level to 5.2% in the highest risk level. People in the highest risk level had 18.8 times higher odds (95% confidence interval, 14.6 to 24.3) of experiencing a hip fracture within one year than those in the lowest. c-Statistics were similar for the full (0.658), derivation (0.662), and validation (0.645) samples.

CONCLUSIONS

The FRS-HC predicts hip fracture over one year and should be used to guide clinical care planning for home care recipients at high risk for fracture. Our next steps are to develop a fracture risk clinical assessment protocol to link treatment recommendations with identified fracture risk.

Targeted bone density testing for optimizing fracture prevention in Canada

The Canadian FRAX® tool used without bone mineral density (BMD) is highly sensitive for identifying individuals qualifying for pharmacotherapy based upon an intervention threshold of 20% for major osteoporotic fracture risk (MOF) computed with BMD.

INTRODUCTION

This analysis was performed to inform initial BMD testing as part of Osteoporosis Canada's Guidelines Update for women and men at average risk, assuming a pharmacotherapy intervention threshold of 20% for FRAX® MOF computed with BMD.

METHODS

Women and men age 50 + without previous low-trauma fracture or high-risk medication use were identified in a BMD registry for the province of Manitoba, Canada. Fracture probability assessments with the Canadian FRAX® tool were computed without and with BMD (denoted MOF-clinical and MOF-BMD, respectively).

RESULTS

The study population consisted of 50,700 women (mean age 65.5 ± 9.4 years) and 4152 men (69.2 ± 10.0 years). FRAX MOF-clinical score was > 10% in 33.8% of women and 13.3% of men (P < 0.001). The median (interquartile range [IQR]) age for MOF-clinical to reach 10% in women was 70 (69-72) and 65 years (62-67) years in the absence and presence of additional FRAX clinical risk factors, respectively. In men, comparable ages were 83 years [82-86] and 76 [70-78] years. Using MOF-BMD of 20% as the intervention threshold, 4.3% of women and 0.7% of men qualified for treatment. MOF-clinical > 10% had high sensitivity to identify those qualifying for treatment (99.3% in women and 99.1% in men). An age-based rule ("BMD testing is indicated at age 70 if no additional FRAX clinical risk factors are present, or at age 65 if one or more clinical risk factors exists") gave similarly high sensitivity (women 99.9% and men > 99.9%).

CONCLUSIONS

FRAX without BMD offers an effective strategy to identify individuals meeting the current Canadian treatment threshold based upon FRAX® with BMD (≥ 20%). Moreover, this can be operationalized using simple age cutoffs of 70 years in the absence of additional clinical risk factors and 65 years in the presence of additional clinical risk factors.