Independent external validation of nomograms for predicting risk of low-trauma fracture and hip fracture

Hip Fracture Risk Assessment Tools for Adults Aged 80 Years and Older

Importance

While adults aged 80 years and older account for 70% of hip fractures in the US, performance of fracture risk assessment tools in this population is uncertain.

Objective

To compare performance of the Fracture Risk Assessment Tool (FRAX), Garvan Fracture Risk Calculator, and femoral neck bone mineral density (FNBMD) alone in 5-year hip fracture prediction.

Design, Setting and Participants

Prognostic analysis of 3 prospective cohort studies including participants attending an index examination (1997 to 2016) at age 80 years or older. Data were analyzed from March 2023 to April 2024.

Main Outcomes and Measures

Participants contacted every 4 or 6 months after index examination to ascertain incident hip fractures and vital status. Predicted 5-year hip fracture probabilities calculated using FRAX and Garvan models incorporating FNBMD and FNBMD alone. Model discrimination assessed by area under receiver operating characteristic curve (AUC). Model calibration assessed by comparing observed vs predicted hip fracture probabilities within predicted risk quintiles.

Results

A total of 8890 participants were included, with a mean (SD) age at index examination of 82.6 (2.7) years; 4906 participants (55.2%) were women, 866 (9.7%) were Black, 7836 (88.1%) were White, and 188 (2.1%) were other races and ethnicities. During 5-year follow-up, 321 women (6.5%) and 123 men (3.1%) experienced a hip fracture; 818 women (16.7%) and 921 men (23.1%) died before hip fracture. Among women, AUC was 0.69 (95% CI, 0.67-0.72) for FRAX, 0.69 (95% CI, 0.66-0.72) for Garvan, and 0.72 (95% CI, 0.69-0.75) for FNBMD alone (FNBMD superior to FRAX, P = .01; and Garvan, P = .01). Among men, AUC was 0.71 (95% CI, 0.66-0.75) for FRAX, 0.76 (95% CI, 0.72-0.81) for Garvan, and 0.77 (95% CI, 0.72-0.81) for FNBMD alone (P < .001 Garvan and FNBMD alone superior to FRAX). Among both sexes, Garvan greatly overestimated hip fracture risk among individuals in upper quintiles of predicted risk, while FRAX modestly underestimated risk among those in intermediate quintiles of predicted risk.

Conclusions and Relevance

In this prognostic study of adults aged 80 years and older, FRAX and Garvan tools incorporating FNBMD compared with FNBMD alone did not improve 5-year hip fracture discrimination. FRAX modestly underpredicted observed hip fracture probability in intermediate-risk individuals. Garvan markedly overpredicted observed hip fracture probability in high-risk individuals. Until better prediction tools are available, clinicians should prioritize consideration of hip BMD, life expectancy, and patient preferences in decision-making regarding drug treatment initiation for hip fracture prevention in late-life adults.

Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis

OBJECTIVES

Early identification of fracture risk in patients with osteoporosis is essential. Machine learning (ML) has emerged as a promising technique to predict the risk, whereas its predictive performance remains controversial. Therefore, we conducted this systematic review and meta-analysis to explore the predictive efficiency of ML for the risk of fracture in patients with osteoporosis.

METHODS

Relevant studies were retrieved from four databases (PubMed, Embase, Cochrane Library and Web of Science) until 31 May 2023. A meta-analysis of the C-index was performed using a random-effects model, while a bivariate mixed-effects model was used for the meta-analysis of sensitivity and specificity. In addition, subgroup analysis was performed according to the types of ML models and fracture sites.

RESULTS

Fifty-three studies were included in our meta-analysis, involving 15 209 268 patients, 86 prediction models specifically developed for the osteoporosis population and 41 validation sets. The most commonly used predictors in these models encompassed age, BMI, past fracture history, bone mineral density T-score, history of falls, BMD, radiomics data, weight, height, gender and other chronic diseases. Overall, the pooled C-index of ML was 0.75 (95% CI: 0.72, 0.78) and 0.75 (95% CI: 0.71, 0.78) in the training set and validation set, respectively; the pooled sensitivity was 0.79 (95% CI: 0.72, 0.84) and 0.76 (95% CI: 0.80, 0.81) in the training set and validation set, respectively; and the pooled specificity was 0.81 (95% CI: 0.75, 0.86) and 0.83 (95% CI: 0.72, 0.90) in the training set and validation set, respectively.

CONCLUSIONS

ML has a favourable predictive performance for fracture risk in patients with osteoporosis. However, most current studies lack external validation. Thus, external validation is required to verify the reliability of ML models.

PROSPERO REGISTRATION NUMBER

CRD42022346896.

Screening for the primary prevention of fragility fractures among adults aged 40 years and older in primary care: systematic reviews of the effects and acceptability of screening and treatment, and the accuracy of risk prediction tools

BACKGROUND

To inform recommendations by the Canadian Task Force on Preventive Health Care, we reviewed evidence on the benefits, harms, and acceptability of screening and treatment, and on the accuracy of risk prediction tools for the primary prevention of fragility fractures among adults aged 40 years and older in primary care.

METHODS

For screening effectiveness, accuracy of risk prediction tools, and treatment benefits, our search methods involved integrating studies published up to 2016 from an existing systematic review. Then, to locate more recent studies and any evidence relating to acceptability and treatment harms, we searched online databases (2016 to April 4, 2022 [screening] or to June 1, 2021 [predictive accuracy]; 1995 to June 1, 2021, for acceptability; 2016 to March 2, 2020, for treatment benefits; 2015 to June 24, 2020, for treatment harms), trial registries and gray literature, and hand-searched reviews, guidelines, and the included studies. Two reviewers selected studies, extracted results, and appraised risk of bias, with disagreements resolved by consensus or a third reviewer. The overview of reviews on treatment harms relied on one reviewer, with verification of data by another reviewer to correct errors and omissions. When appropriate, study results were pooled using random effects meta-analysis; otherwise, findings were described narratively. Evidence certainty was rated according to the GRADE approach.

RESULTS

We included 4 randomized controlled trials (RCTs) and 1 controlled clinical trial (CCT) for the benefits and harms of screening, 1 RCT for comparative benefits and harms of different screening strategies, 32 validation cohort studies for the calibration of risk prediction tools (26 of these reporting on the Fracture Risk Assessment Tool without [i.e., clinical FRAX], or with the inclusion of bone mineral density (BMD) results [i.e., FRAX + BMD]), 27 RCTs for the benefits of treatment, 10 systematic reviews for the harms of treatment, and 12 studies for the acceptability of screening or initiating treatment. In females aged 65 years and older who are willing to independently complete a mailed fracture risk questionnaire (referred to as "selected population"), 2-step screening using a risk assessment tool with or without measurement of BMD probably (moderate certainty) reduces the risk of hip fractures (3 RCTs and 1 CCT, n = 43,736, absolute risk reduction [ARD] = 6.2 fewer in 1000, 95% CI 9.0-2.8 fewer, number needed to screen [NNS] = 161) and clinical fragility fractures (3 RCTs, n = 42,009, ARD = 5.9 fewer in 1000, 95% CI 10.9-0.8 fewer, NNS = 169). It probably does not reduce all-cause mortality (2 RCTs and 1 CCT, n = 26,511, ARD = no difference in 1000, 95% CI 7.1 fewer to 5.3 more) and may (low certainty) not affect health-related quality of life. Benefits for fracture outcomes were not replicated in an offer-to-screen population where the rate of response to mailed screening questionnaires was low. For females aged 68-80 years, population screening may not reduce the risk of hip fractures (1 RCT, n = 34,229, ARD = 0.3 fewer in 1000, 95% CI 4.2 fewer to 3.9 more) or clinical fragility fractures (1 RCT, n = 34,229, ARD = 1.0 fewer in 1000, 95% CI 8.0 fewer to 6.0 more) over 5 years of follow-up. The evidence for serious adverse events among all patients and for all outcomes among males and younger females (<65 years) is very uncertain. We defined overdiagnosis as the identification of high risk in individuals who, if not screened, would never have known that they were at risk and would never have experienced a fragility fracture. This was not directly reported in any of the trials. Estimates using data available in the trials suggest that among "selected" females offered screening, 12% of those meeting age-specific treatment thresholds based on clinical FRAX 10-year hip fracture risk, and 19% of those meeting thresholds based on clinical FRAX 10-year major osteoporotic fracture risk, may be overdiagnosed as being at high risk of fracture. Of those identified as being at high clinical FRAX 10-year hip fracture risk and who were referred for BMD assessment, 24% may be overdiagnosed. One RCT (n = 9268) provided evidence comparing 1-step to 2-step screening among postmenopausal females, but the evidence from this trial was very uncertain. For the calibration of risk prediction tools, evidence from three Canadian studies (n = 67,611) without serious risk of bias concerns indicates that clinical FRAX-Canada may be well calibrated for the 10-year prediction of hip fractures (observed-to-expected fracture ratio [O:E] = 1.13, 95% CI 0.74-1.72, I2 = 89.2%), and is probably well calibrated for the 10-year prediction of clinical fragility fractures (O:E = 1.10, 95% CI 1.01-1.20, I2 = 50.4%), both leading to some underestimation of the observed risk. Data from these same studies (n = 61,156) showed that FRAX-Canada with BMD may perform poorly to estimate 10-year hip fracture risk (O:E = 1.31, 95% CI 0.91-2.13, I2 = 92.7%), but is probably well calibrated for the 10-year prediction of clinical fragility fractures, with some underestimation of the observed risk (O:E 1.16, 95% CI 1.12-1.20, I2 = 0%). The Canadian Association of Radiologists and Osteoporosis Canada Risk Assessment (CAROC) tool may be well calibrated to predict a category of risk for 10-year clinical fractures (low, moderate, or high risk; 1 study, n = 34,060). The evidence for most other tools was limited, or in the case of FRAX tools calibrated for countries other than Canada, very uncertain due to serious risk of bias concerns and large inconsistency in findings across studies. Postmenopausal females in a primary prevention population defined as <50% prevalence of prior fragility fracture (median 16.9%, range 0 to 48% when reported in the trials) and at risk of fragility fracture, treatment with bisphosphonates as a class (median 2 years, range 1-6 years) probably reduces the risk of clinical fragility fractures (19 RCTs, n = 22,482, ARD = 11.1 fewer in 1000, 95% CI 15.0-6.6 fewer, [number needed to treat for an additional beneficial outcome] NNT = 90), and may reduce the risk of hip fractures (14 RCTs, n = 21,038, ARD = 2.9 fewer in 1000, 95% CI 4.6-0.9 fewer, NNT = 345) and clinical vertebral fractures (11 RCTs, n = 8921, ARD = 10.0 fewer in 1000, 95% CI 14.0-3.9 fewer, NNT = 100); it may not reduce all-cause mortality. There is low certainty evidence of little-to-no reduction in hip fractures with any individual bisphosphonate, but all provided evidence of decreased risk of clinical fragility fractures (moderate certainty for alendronate [NNT=68] and zoledronic acid [NNT=50], low certainty for risedronate [NNT=128]) among postmenopausal females. Evidence for an impact on risk of clinical vertebral fractures is very uncertain for alendronate and risedronate; zoledronic acid may reduce the risk of this outcome (4 RCTs, n = 2367, ARD = 18.7 fewer in 1000, 95% CI 25.6-6.6 fewer, NNT = 54) for postmenopausal females. Denosumab probably reduces the risk of clinical fragility fractures (6 RCTs, n = 9473, ARD = 9.1 fewer in 1000, 95% CI 12.1-5.6 fewer, NNT = 110) and clinical vertebral fractures (4 RCTs, n = 8639, ARD = 16.0 fewer in 1000, 95% CI 18.6-12.1 fewer, NNT=62), but may make little-to-no difference in the risk of hip fractures among postmenopausal females. Denosumab probably makes little-to-no difference in the risk of all-cause mortality or health-related quality of life among postmenopausal females. Evidence in males is limited to two trials (1 zoledronic acid, 1 denosumab); in this population, zoledronic acid may make little-to-no difference in the risk of hip or clinical fragility fractures, and evidence for all-cause mortality is very uncertain. The evidence for treatment with denosumab in males is very uncertain for all fracture outcomes (hip, clinical fragility, clinical vertebral) and all-cause mortality. There is moderate certainty evidence that treatment causes a small number of patients to experience a non-serious adverse event, notably non-serious gastrointestinal events (e.g., abdominal pain, reflux) with alendronate (50 RCTs, n = 22,549, ARD = 16.3 more in 1000, 95% CI 2.4-31.3 more, [number needed to treat for an additional harmful outcome] NNH = 61) but not with risedronate; influenza-like symptoms with zoledronic acid (5 RCTs, n = 10,695, ARD = 142.5 more in 1000, 95% CI 105.5-188.5 more, NNH = 7); and non-serious gastrointestinal adverse events (3 RCTs, n = 8454, ARD = 64.5 more in 1000, 95% CI 26.4-13.3 more, NNH = 16), dermatologic adverse events (3 RCTs, n = 8454, ARD = 15.6 more in 1000, 95% CI 7.6-27.0 more, NNH = 64), and infections (any severity; 4 RCTs, n = 8691, ARD = 1.8 more in 1000, 95% CI 0.1-4.0 more, NNH = 556) with denosumab. For serious adverse events overall and specific to stroke and myocardial infarction, treatment with bisphosphonates probably makes little-to-no difference; evidence for other specific serious harms was less certain or not available. There was low certainty evidence for an increased risk for the rare occurrence of atypical femoral fractures (0.06 to 0.08 more in 1000) and osteonecrosis of the jaw (0.22 more in 1000) with bisphosphonates (most evidence for alendronate). The evidence for these rare outcomes and for rebound fractures with denosumab was very uncertain. Younger (lower risk) females have high willingness to be screened. A minority of postmenopausal females at increased risk for fracture may accept treatment. Further, there is large heterogeneity in the level of risk at which patients may be accepting of initiating treatment, and treatment effects appear to be overestimated.

CONCLUSION

An offer of 2-step screening with risk assessment and BMD measurement to selected postmenopausal females with low prevalence of prior fracture probably results in a small reduction in the risk of clinical fragility fracture and hip fracture compared to no screening. These findings were most applicable to the use of clinical FRAX for risk assessment and were not replicated in the offer-to-screen population where the rate of response to mailed screening questionnaires was low. Limited direct evidence on harms of screening were available; using study data to provide estimates, there may be a moderate degree of overdiagnosis of high risk for fracture to consider. The evidence for younger females and males is very limited. The benefits of screening and treatment need to be weighed against the potential for harm; patient views on the acceptability of treatment are highly variable.

SYSTEMATIC REVIEW REGISTRATION

International Prospective Register of Systematic Reviews (PROSPERO): CRD42019123767.

Adjusting conventional FRAX estimates of fracture probability according to the number of prior falls in the preceding year

A greater propensity to falling is associated with higher fracture risk. This study provides adjustments to FRAX-based fracture probabilities accounting for the number of prior falls.

INTRODUCTION

Prior falls increase subsequent fracture risk but are not currently directly included in the FRAX tool. The aim of this study was to quantify the effect of the number of prior falls on the 10-year probability of fracture determined with FRAX®.

METHODS

We studied 21,116 women and men age 40 years or older (mean age 65.7 ± 10.1 years) with fracture probability assessment (FRAX®), self-reported falls for the previous year, and subsequent fracture outcomes in a registry-based cohort. The risks of death, hip fracture, and non-hip major osteoporotic fracture (MOF-NH) were determined by Cox proportional hazards regression for fall number category versus the whole population (i.e., an average number of falls). Ten-year probabilities of hip fracture and major osteoporotic fracture (MOF) were determined according to the number of falls from the hazards of death and fracture incorporated into the FRAX model for the UK. The probability ratios (number of falls vs. average number of falls) provided adjustments to conventional FRAX estimates of fracture probability according to the number of falls.

RESULTS

Compared with the average number of falls, the hazard ratios for hip fracture, MOF-NH and death were lower than unity in the absence of a fall history. Hazard ratios increased progressively with an increasing number of reported falls. The probability ratio rose progressively as the number of reported falls increased. Probability ratios decreased with age, an effect that was more marked the greater the number of prior falls.

CONCLUSION

The probability ratios provide adjustments to conventional FRAX estimates of fracture probability according to the number of prior falls.

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.

Development and Validation of a Diagnostic Nomogram to Predict the Anthracycline-Induced Early Cardiotoxicity in Children with Hematological Tumors

This study aimed to establish and validate an effective nomogram to predict the risk of cardiotoxicity in children after each anthracycline treatment. According to the inclusion and exclusion criteria, the eligible children were randomly divided into the training cohort (75%) and the validation cohort (25%). Least absolute shrinkage and selection operator (LASSO) regression was used to select the predictors and a nomogram was developed. Then, concordance index (C-index), the area under the curve (AUC), Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were employed to evaluate the performance and clinical utility of nomogram. Internal validation was processed to inspect the stability of the model. A total of 796 eligible children were included in this study and divided into a training set (n = 597) and a validation set (n = 199). LASSO regression analysis revealed that cumulative anthracycline dose, ejection fractions, NT-proBNP, and diastolic dysfunction were effective predictors of cardiotoxicity. The nomogram was established based on these variables. The C-index and the AUC of the predicting nomogram were 0.818 in the training cohort and 0.773 in the validation cohort, suggesting that the nomogram had good discrimination. The calibration curve of the nomogram presented no significant deviation from the reference line, and the P-value of the H-L test was 0.283, implying a preferable degree of calibration. The threshold of DCA also reflects that the nomogram is clinically useful. A nomogram was developed to predict anthracycline chemotherapy-induced cardiotoxicity in children with hematological tumors. The nomogram has a good prediction effect and can provide a reference for clinicians' diagnosis and treatment.

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.

Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal

Osteoporotic fractures (OF) are a global public health problem currently. Many risk prediction models for OF have been developed, but their performance and methodological quality are unclear. We conducted this systematic review to summarize and critically appraise the OF risk prediction models. Three databases were searched until April 2021. Studies developing or validating multivariable models for OF risk prediction were considered eligible. Used the prediction model risk of bias assessment tool to appraise the risk of bias and applicability of included models. All results were narratively summarized and described. A total of 68 studies describing 70 newly developed prediction models and 138 external validations were included. Most models were explicitly developed (n=31, 44%) and validated (n=76, 55%) only for female. Only 22 developed models (31%) were externally validated. The most validated tool was Fracture Risk Assessment Tool. Overall, only a few models showed outstanding (n=3, 1%) or excellent (n=32, 15%) prediction discrimination. Calibration of developed models (n=25, 36%) or external validation models (n=33, 24%) were rarely assessed. No model was rated as low risk of bias, mostly because of an insufficient number of cases and inappropriate assessment of calibration. There are a certain number of OF risk prediction models. However, few models have been thoroughly internally validated or externally validated (with calibration being unassessed for most of the models), and all models showed methodological shortcomings. Instead of developing completely new models, future research is suggested to validate, improve, and analyze the impact of existing models.

The Association Between Cadmium Exposure and Osteoporosis: A Longitudinal Study and Predictive Model in a Chinese Female Population

Objective: The association between cadmium exposure and osteoporosis has been rarely reported in longitudinal studies. In this study, we investigated the association between osteoporosis and cadmium exposure and developed predictive models in women in a longitudinal cohort. Materials and Methods: In total, 488 women living in southeastern China were included at baseline (1998). Cadmium in blood (BCd) and urine (UCd) and also renal dysfunction biomarkers and bone mineral density (BMD) were determined both at baseline and follow-up. A total of 307 subjects were finally included after excluding subjects that did not have exposure or effect biomarkers. Osteoporosis was defined based on T score ≤ -2.5. Multiple linear regression and multivariate logistic analysis were used to show the association between baseline data and follow-up osteoporosis. Based on the identified associated factors, nomograms were developed to graphically calculate the individual risk of osteoporosis. Results: The baseline BMD in subjects with osteoporosis was significantly lower than that in subjects without osteoporosis (0.59 vs. 0.71 g/cm2, p < 0.05). The prevalence of low bone mass at baseline was higher in subjects with osteoporosis than in those without osteoporosis (23.5 vs. 7.2%, p = 0.001). Logistic regression analysis demonstrated that age [odds ratio (OR) = 1.21, 95% confidence interval (CI): 1.16-1.27], UCd (OR = 1.03, 95% CI: 1.002-1.06) and the presence of low BMD (OR = 3.84, 95% CI: 1.49-9.89) were independent risk factors for osteoporosis. For those subjects with normal baseline BMD, age, UCd, and baseline BMD were also independent risk factors for osteoporosis. The OR value was 1.16 (95% CI: 1.10-1.22) for age, 2.27 (95% CI: 1.03-4.99) for UCd > 10 μg/g creatinine, and 0.39 (95% CI: 0.21-0.72) for BMDbaseline. We developed two nomograms to predict the risk of osteoporosis. The area under the curve was 0.88 (95% CI: 0.84-0.92) for total population and was 0.88 (95% CI: 0.84-0.92) for subjects with normal baseline BMD, respectively. Conclusion: Baseline age, UCd, and BMD were associated with follow-up osteoporosis in women. Nomograms showed good performance in predicting the risk of osteoporosis.

Treatment indications and thresholds of intervention: consensus and controversies in osteoporosis

A few indications for treatment and thresholds for intervention in osteoporosis have been propounded in the literature and recommended in guidelines. These include a bone mineral density (BMD) T-score ≤ -2.5, fracture probability-based scores and the presence of a fragility fracture. A low BMD is associated with an increased risk of fracture. However, a BMD T-score of ≤ -2.5 on its own does not capture fracture risk in its entirety. Fracture risk assessment tools that are based on clinical risk factors arose from the misgivings about using BMD T-scores in isolation to assess fracture risk. Algorithms such as FRAX, Garvan etc, integrate various clinical risk factors with or without BMD to compute the probability of a hip fracture or a major osteoporotic fracture over a finite period. These probabilities do not yield distinctive thresholds by themselves and need to be interpreted wisely and adopted by consensus. Evidence exists to show that treatment can decrease the risk of sustaining a second fracture. Therefore, patients with a fragility fracture should be considered for treatment. In this narrative interview, we will explore the strengths and limitations of some of these indications for treatment and will discuss the various points of contention surrounding them.

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.

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.

Decline in Muscle Strength and Performance Predicts Fracture Risk in Elderly Women and Men

CONTEXT

Muscle strength and performance are associated with fractures. However, the contribution of their rate of decline is unclear.

OBJECTIVE

To assess the independent contribution of the rate of decline in muscle strength and performance to fracture risk.

DESIGN, SETTING, AND PARTICIPANTS

Community-dwelling women (n = 811) and men (n = 440) aged 60 years or older from the prospective Dubbo Osteoporosis Epidemiology Study followed from 2000 to 2018 for incident fracture. Clinical data, appendicular lean mass/height2 (ht)2, bone mineral density, quadricep strength/ht (QS), timed get-up-and-go (TGUG), 5 times repeated sit-to-stand (5xSTS), and gait speed (GS) measured biennially. Rates of decline in muscle parameters were calculated using ordinary least squares regression and fracture risk was assessed using Cox's models.

MAIN OUTCOME

Incident low-trauma fracture ascertained by x-ray report.

RESULTS

Apart from lean mass in women, all muscle parameters declined over time. Greater rates of decline in physical performance were associated with increased fracture risk in women (Hazard ratios [HRs] ranging from 2.1 (95% CI: 1.5-2.9) for GS to 2.7 (95% CI: 1.9-3.6) for 5xSTS, while in men only the decline in GS was associated with fracture risk (HR: 3.4 [95% CI: 1.8-6.3]). Baseline performance and strength were also associated with increased fracture risk in men (HRs ranging from 1.8 (95% CI: 1.1-3.0) for QS to 2.5 (95% CI: 1.5-4.1) for TGUG, but not in women.

CONCLUSION

Rate of decline in physical performance in both genders, and baseline strength and performance in men, contributed independently to fracture risk. Sit-to-stand and GS were the tests most consistently associated with fractures. Further studies are required to determine whether muscle strength and/or performance improve the predictive accuracy of fracture prediction models.

Fracture prediction from self-reported falls in routine clinical practice: a registry-based cohort study

A simple question construct regarding number of falls in the previous year, ascertained by a single question, was strongly associated with incident fractures in routine clinical practice using a population-based dual-energy X-ray absorptiometry (DXA) registry.

INTRODUCTION

There is conflicting evidence from research cohorts that falls independently increase fracture risk. We examined the independent effects of falls on subsequent fractures in a large clinical registry of bone mineral density (BMD) results for the Province of Manitoba, Canada that has been systematically collecting self-reported falls information since September 1, 2012.

METHODS

The study population consisted of 24,943 women and men aged 40 years and older (mean age 65.5 ± 10.2 years) with fracture probability assessment (FRAX), self-reported falls for the previous year (categorized as none, 1, 2, or > 3) and fracture outcomes. Adjusted hazard ratios (HR) with 95 confidence intervals (CI) for time to fracture were estimated using Cox proportional hazards models.

RESULTS

During mean observation time of 2.7 ± 1.0 years, 863 (3.5%) sustained one or more major osteoporotic fractures (MOF), 212 (0.8%) sustained a hip fracture, and 1210 (4.9%) sustained any incident fracture. Compared with no falls in the previous year (referent), there was a gradient of increasing risk for fracture with increasing number of falls (all P < 0.001). Results showed minimal attenuation with covariate adjustment. When adjusted for baseline fracture probability (FRAX score with BMD) the HR for MOF increased from 1.49 (95% CI 1.25-1.78) for one fall to 1.74 (1.33-2.27) for two falls to 2.62 (2.06-3.34) for ≥ 3 falls. HRs were similar for any incident fracture and slightly greater for prediction of hip fracture, reaching 3.41 (95% CI 2.19-5.31) for ≥ 3 previous falls.

CONCLUSIONS

Self-report number of falls in the previous year is strongly associated with incident fracture risk in the routine clinical practice setting, and this risk is independent of age, sex, BMD, and baseline fracture probability. Moreover, there is dose-response with multiple falls (up to a maximum of 3) conferring greater risk than a single fall.

Predictors of imminent non-vertebral fracture in elderly women with osteoporosis, low bone mass, or a history of fracture, based on data from the population-based Canadian Multicentre Osteoporosis Study (CaMos)

Using data from the Canadian Multicentre Osteoporosis Study, several risk factors predictive of imminent (2-year) risk of low-trauma non-vertebral fracture among high-risk women were identified, including history of falls, history of low-trauma fracture, poorer physical function, and lower T score. Careful consideration should be given to targeting this population for therapy.

PURPOSE

Fracture risk assessment has focused on a long-term horizon and populations with a broad risk range. For elderly women with osteoporosis or low bone mass, or a history of fragility fractures ("high-risk women"), risk prediction over a shorter horizon may have greater clinical relevance.

METHODS

A repeated-observations design and data from the Canadian Multicentre Osteoporosis Study were employed. Study population comprised women aged ≥ 65 years with T score (total hip, femoral neck, spine) ≤ - 1.0 or prior fracture. Hazard ratios (HR) for predictors of low-trauma non-vertebral fracture during 2-year follow-up were estimated using multivariable shared frailty model.

RESULTS

The study population included 3228 women who contributed 5004 observations; 4.8% experienced low-trauma non-vertebral fracture during the 2-year follow-up. In bivariate analyses, important risk factors included age, back pain, history of falls, history of low-trauma fracture, physical function, health status, and total hip T score. In multivariable analyses, only four independent predictors were identified: falls in past 12 months (≥ 2 falls: HR = 1.9; 1 fall: HR = 1.5), low-trauma fracture in past 12 months (≥ 1 fracture: HR = 1.7), SF-36 physical component summary score (≤ 42.0: HR = 1.6), and total hip T score (≤ - 3.5: HR = 3.7; > - 3.5 to ≤ - 2.5: HR = 2.5; > - 2.5 to ≤ - 1: HR = 1.3).

CONCLUSIONS

Imminent risk of low-trauma non-vertebral fracture is elevated among high-risk women with a history of falls or low-trauma fracture, poorer physical function, and lower T score. Careful consideration should be given to identifying and targeting this population for therapy.

Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression

There is no consensus on which tool is the most accurate to assess fracture risk. The results of this systematic review suggest that QFracture, Fracture Risk Assessment Tool (FRAX) with BMD, and Garvan with BMD are the tools with the best discriminative ability. More studies assessing the comparative performance of current tools are needed.

INTRODUCTION

Many tools exist to assess fracture risk. This review aims to determine which tools have the best predictive accuracy to identify individuals at high risk of non-traumatic fracture.

METHODS

Studies assessing the accuracy of tools for prediction of fracture were searched in MEDLINE, EMBASE, Evidence-Based Medicine Reviews, and Global Health. Studies were eligible if discrimination was assessed in a population independent of the derivation cohort. Meta-analyses and meta-regressions were performed on areas under the ROC curve (AUCs). Gender, mean age, age range, and study quality were used as adjustment variables.

RESULTS

We identified 53 validation studies assessing the discriminative ability of 14 tools. Given the small number of studies on some tools, only FRAX, Garvan, and QFracture were compared using meta-regression models. In the unadjusted analyses, QFracture had the best discriminative ability to predict hip fracture (AUC = 0.88). In the adjusted analysis, FRAX with BMD (AUC = 0.81) and Garvan with BMD (AUC = 0.79) had the highest AUCs. For prediction of major osteoporotic fracture, QFracture had the best discriminative ability (AUC = 0.77). For prediction of osteoporotic or any fracture, FRAX with BMD and Garvan with BMD had higher discriminative ability than their versions without BMD (FRAX: AUC = 0.72 vs 0.69, Garvan: AUC = 0.72 vs 0.65). A significant amount of heterogeneity was present in the analyses.

CONCLUSIONS

QFracture, FRAX with BMD, and Garvan with BMD have the highest discriminative performance for predicting fracture. Additional studies in which the performance of current tools is assessed in the same individuals may be performed to confirm this conclusion.

Screening to Prevent Osteoporotic Fractures: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force

IMPORTANCE

Osteoporotic fractures cause significant morbidity and mortality.

OBJECTIVE

To update the evidence on screening and treatment to prevent osteoporotic fractures for the US Preventive Services Task Force.

DATA SOURCES

PubMed, the Cochrane Library, EMBASE, and trial registries (November 1, 2009, through October 1, 2016) and surveillance of the literature (through March 23, 2018); bibliographies from articles.

STUDY SELECTION

Adults 40 years and older; screening cohorts without prevalent low-trauma fractures or treatment cohorts with increased fracture risk; studies assessing screening, bone measurement tests or clinical risk assessments, pharmacologic treatment.

DATA EXTRACTION AND SYNTHESIS

Dual, independent review of titles/abstracts and full-text articles; study quality rating; random-effects meta-analysis.

MAIN OUTCOMES AND MEASURES

Incident fractures and related morbidity and mortality, diagnostic and predictive accuracy, harms of screening or treatment.

RESULTS

One hundred sixty-eight fair- or good-quality articles were included. One randomized clinical trial (RCT) (n = 12 483) comparing screening with no screening reported fewer hip fractures (2.6% vs 3.5%; hazard ratio [HR], 0.72 [95% CI, 0.59-0.89]) but no other statistically significant benefits or harms. The accuracy of bone measurement tests to identify osteoporosis varied (area under the curve [AUC], 0.32-0.89). The pooled accuracy of clinical risk assessments for identifying osteoporosis ranged from AUC of 0.65 to 0.76 in women and from 0.76 to 0.80 in men; the accuracy for predicting fractures was similar. For women, bisphosphonates, parathyroid hormone, raloxifene, and denosumab were associated with a lower risk of vertebral fractures (9 trials [n = 23 690]; relative risks [RRs] from 0.32-0.64). Bisphosphonates (8 RCTs [n = 16 438]; pooled RR, 0.84 [95% CI, 0.76-0.92]) and denosumab (1 RCT [n = 7868]; RR, 0.80 [95% CI, 0.67-0.95]) were associated with a lower risk of nonvertebral fractures. Denosumab reduced the risk of hip fracture (1 RCT [n = 7868]; RR, 0.60 [95% CI, 0.37-0.97]), but bisphosphonates did not have a statistically significant association (3 RCTs [n = 8988]; pooled RR, 0.70 [95% CI, 0.44-1.11]). Evidence was limited for men: zoledronic acid reduced the risk of radiographic vertebral fractures (1 RCT [n = 1199]; RR, 0.33 [95% CI, 0.16-0.70]); no studies demonstrated reductions in clinical or hip fractures. Bisphosphonates were not consistently associated with reported harms other than deep vein thrombosis (raloxifene vs placebo; 3 RCTs [n = 5839]; RR, 2.14 [95% CI, 0.99-4.66]).

CONCLUSIONS AND RELEVANCE

In women, screening to prevent osteoporotic fractures may reduce hip fractures, and treatment reduced the risk of vertebral and nonvertebral fractures; there was not consistent evidence of treatment harms. The accuracy of bone measurement tests or clinical risk assessments for identifying osteoporosis or predicting fractures varied from very poor to good.

Individualized fracture risk assessment: State-of-the-art and room for improvement

Fragility fracture is a serious clinical event, because it is associated with increased risk of mortality and reduced quality of life. The risk of fracture is determined by multiple risk factors, and their effects may be interactional. Over the past 10 years, a number of predictive models (e.g., FRAX, Garvan Fracture Risk Calculator, and Qfracture) have been developed for individualized assessment of fracture risk. These models use different risk profiles to estimate the probability of fracture over 5- and 10-year period. The ability of these models to discriminate between those individuals who will and will not have a fracture (i.e., area under the receiver operating characteristic curve [AUC]) is generally acceptable-to-good (AUC, 0.6 to 0.8), and is highly variable between populations. The calibration of existing models is poor, particularly in Asian populations. There is a strong need for the development and validation of new prediction models based on Asian data for Asian populations. We propose approaches to improve the accuracy of existing predictive models by incorporating new markers such as genetic factors, bone turnover markers, trabecular bone score, and time-variant factors. New and more refined models for individualized fracture risk assessment will help identify those most likely to sustain a fracture, those most likely to benefit from treatment, and encouraging them to modify their risk profile to decrease risk.

Predictors of near-term fracture in osteoporotic women aged ≥65 years, based on data from the study of osteoporotic fractures

Using data from the Study of Osteoporotic Fractures (SOF), several clinical characteristics predictive of near-term (1-year) risk of hip and non-vertebral fracture among elderly osteoporotic women were identified, and a subset of those for hip fracture was incorporated into a risk assessment tool. Additional research is needed to validate study findings.

INTRODUCTION

While several risk factors are known to contribute to long-term fracture risk in women with osteoporosis, factors predicting fracture risk over a shorter time horizon, such as over a 1-year period, are less well-established.

METHODS

We utilized a repeated-observations design and data from the Study of Osteoporotic Fractures to identify factors contributing to near-term risk of hip fracture and any non-vertebral fracture, respectively, among osteoporotic women aged ≥65 years. Potential predictors of hip fracture and any non-vertebral fracture over the 1-year period subsequent to each qualifying SOF exam were examined using multivariable frailty models. Because the discriminative ability of the hip fracture model was acceptable, a corresponding risk-prediction tool was also developed.

RESULTS

Study population included 2499 women with osteoporosis, who contributed 6811 observations. Incidence of fracture in the 1-year period subsequent to each exam was 2.2% for hip fracture and 6.6% for any non-vertebral fracture. Independent predictors of hip fracture included low total hip T-score, prior fracture, and risk factors for falls (multivariable model c-statistic = 0.71 (95% CI 0.67-0.76)). Independent predictors of any non-vertebral fracture included age, total hip T-score, prior falls, prior fracture, walking speed, Parkinson's disease or stroke, and smoking (multivariable model c-statistic = 0.62 (0.59-0.65)).

CONCLUSIONS

Several clinical characteristics predictive of hip and non-vertebral fracture within a 1-year follow-up period among elderly women with osteoporosis were identified, and a subset of those for hip fracture was incorporated into a risk assessment tool. Assessment of these risk factors may help guide osteoporosis treatment choices by identifying patients in whom there is urgency to treat. Additional research is needed to validate the findings of this study and the accuracy of the risk assessment tool.

Fracture Risk Assessment: From Population to Individual

Fracture caused by osteoporosis remains a major public health burden on contemporary populations because fracture is associated with a substantial increase in the risk of mortality. Early identification of high-risk individuals for prevention is a priority in osteoporosis research. Over the past decade, few risk prediction models, including the Garvan Fracture Risk Calculator (Garvan) and FRAX^®, have been developed to provide absolute (individualized) risk of fracture. Recent validation studies suggested that the area under the receiver operating characteristic curve in fracture discrimination ranged from 0.61 to 0.83 for FRAX^® and from 0.63 to 0.88 for Garvan, with hip fractures having a better discrimination than fragility fractures as a group. Although the prognostic performance of Garvan and FRAX^® for fracture prediction is not perfect and there is room for further improvement, these predictive models can aid patients and doctors communicate about fracture risk in the medium term and to make rational decisions. However, the application of these predictive models in making decisions for an individual should take into account the individual's perception of the importance of fracture relative to other diseases.

Overview of Fracture Prediction Tools

The characterization of risk factors for fracture that contribute significantly to fracture risk, over and above that provided by the bone mineral density, has stimulated the development of risk assessment tools. The more adequately evaluated tools, all available online, include the FRAX® tool, the Garvan fracture risk calculator and, in the United Kingdom only, QFracture®. Differences in the input variables, output, and model construct give rise to marked differences in the computed risks from each calculator. Reasons for the differences include the derivation of fracture probability (FRAX) rather than incidence (Garvan and QFracture), limited calibration (Garvan), and inappropriate source information (QFracture). These differences need to be taken into account in the evaluation of assessment guidelines.

Worldwide Fracture Prediction

The substantial increase in the burden of non-communicable diseases in general and osteoporosis in particular, necessitates the establishment of efficient and targeted diagnosis and treatment strategies. This chapter reviews and compares different tools for osteoporosis screening and diagnosis; it also provides an overview of different treatment guidelines adopted by countries worldwide. While access to dual-energy X-ray absorptiometry to measure bone mineral density (BMD) is limited in most areas in the world, the introduction of risk calculators that combine risk factors, with or without BMD, have resulted in a paradigm shift in osteoporosis screening and management. To-date, forty eight risk assessment tools that allow risk stratification of patients are available, however only few are externally validated and tested in a population-based setting. These include Osteoporosis Self-Assessment Tool; Osteoporosis Risk Assessment Instrument; Simple Calculated Osteoporosis Risk Estimation; Canadian Association of Radiologists and Osteoporosis Canada calculator; Fracture Risk Assessment Calculator (FRAX); Garvan; and QFracture. These tools vary in the number of risk factors incorporated. We present a detailed analysis of the development, characteristics, validation, performance, advantages and limitations of these tools. The World Health Organization proposes a dual-energy X-ray absorptiometry-BMD T-score ≤ -2.5 as an operational diagnostic threshold for osteoporosis, and many countries have also adopted this cut-off as an intervention threshold in their treatment guidelines. With the introduction of the new fracture assessment calculators, many countries chose to include fracture risk as one of the major criteria to initiate osteoporosis treatment. Of the 52 national guidelines identified in 36 countries, 30 included FRAX derived risk in their intervention threshold and 22 were non-FRAX based.  No universal tool or guideline approach will address the needs of all countries worldwide.  Osteoporosis screening and management guidelines are best tailored according to the needs and resources of individual counties. While few countries have succeeded in generating valuable epidemiological data on osteoporotic fractures, to validate their risk calculators and base their guidelines, many have yet to find the resources to assess variations and secular trends in fractures, the performance of various calculators, and ultimately adopt the most convenient care pathway algorithms.

Diabetes and Bone Disease

The World Health Organization estimates that diabetes mellitus occurs in more than 415 million people; this number could double by the year 2040. Epidemiologic data have shown that the skeletal system may be a target of diabetes-mediated damage, leading to the development of diabetes-induced osteoporosis. T1D and T2D have been associated with an increased risk of fracture. Bone mineral density and fracture risk prediction tools developed for the general population capture some of the risk associated with diabetes. Recent adaptations to these tools have improved their efficacy in patients with diabetes.

DXA-Based Measurements in Diabetes: Can They Predict Fracture Risk?

In the absence of a fragility fracture, osteoporosis is usually diagnosed from bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA). Osteoporosis is an increasingly prevalent disease, as is diabetes [in particular type 2 diabetes (T2D)], in part due to aging populations worldwide. It has been suggested that an increased risk of fracture may be another complication ensuing from longstanding diabetes. The purpose of this review is to concentrate on skeletal parameters and techniques readily available from DXA scanning, and their utility in routine clinical practice for predicting fracture risk. In addition to BMD, other applications and measures from DXA include trabecular bone score (TBS), skeletal geometry and DXA-based finite-element analysis, vertebral fracture assessment, and body composition. In type 1 diabetes (T1D), BMD and FRAX^R (when secondary osteoporosis is included without BMD) only partially account for the excess risk of fracture in T1D. Consistent data exist to show that BMD and FRAX^R can be used to stratify fracture risk in T2D, but do not account for the increased risk of fracture. However, several adjustments to the FRAX score can be made as proxies for T2D to inform the use of FRAX by primary care practitioners. Examples include using the rheumatoid arthritis input (as a proxy for T2D), lumbar spine TBS (to adjust FRAX probability) or an altered hip T-score (lowered by 0.5 units). These adjustments can improve fracture risk prediction in T2D and help to avoid systematically underestimating the risk of osteoporosis-related fractures in those with diabetes.

Risk Assessment Tools for Osteoporosis Screening in Postmenopausal Women: A Systematic Review

Osteoporotic fractures are common in postmenopausal women. Tools are available to estimate the risk of low bone mineral density (BMD) or fracture. This systematic review retrieved articles that evaluated osteoporosis risk assessment tools among postmenopausal women in North America. For identifying BMD T-score ≤-2.5, most studies of the Simple Calculated Osteoporosis Risk Estimation tool (SCORE) and Osteoporosis Risk Assessment Instrument (ORAI) reported sensitivity ≥90 %. Area under the receiver operating characteristic curve (AUC) was usually <0.75 for SCORE and ≥0.75 for ORAI. Among women 50-64 years old, a Fracture Risk Assessment Tool (FRAX) threshold ≥9.3 % had a sensitivity of 33 % for identifying BMD T-score ≤-2.5 and 26 % for predicting major osteoporotic fracture (MOF). For predicting MOF, sensitivity was higher for SCORE and Osteoporosis Self-assessment Tool equation (OST), and higher in women ≥65 years old. For predicting BMD T-score ≤-2.5 in women ≥65 years old, the sensitivities of SCORE; ORAI; and Age, Body Size, No Estrogen (ABONE) were very high. No optimal osteoporosis risk assessment tool is available for identifying low BMD and MOF risk.

The accuracy of osteoporotic fracture risk prediction tools: a systematic review and meta-analysis

OBJECTIVES

To identify and synthesise the best available evidence on the accuracy of the currently available tools for predicting fracture risk.

METHODS

We systematically searched PubMed MEDLINE, Embase and Cochrane databases to 2014. Two reviewers independently selected articles, collected data from studies, and carried out a hand search of the references of the included studies. The Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS) checklist was used, and the primary outcome was the area under the curve (AUC) and 95% CIs, obtained from receiver operating characteristic (ROC) analyses. We excluded tools if they had not been externally validated or were designed for specific disease populations. Random effects meta-analyses were performed with the selected tools.

RESULTS

Forty-five studies met inclusion criteria, corresponding to 13 different tools. Only three tools had been tested more than once in a population-based setting: FRAX (26 studies in 9 countries), GARVAN (6 studies in 3 countries) and QFracture (3 studies in the UK, 1 also including Irish participants). Twenty studies with these three tools were included in a total of 17 meta-analyses (for hip or major osteoporotic fractures; men or women; with or without bone mineral density).

CONCLUSIONS

Most of the 13 tools are feasible in clinical practice. FRAX has the largest number of externally validated and independent studies. The overall accuracy of the different tools is satisfactory (>0.70), with QFracture reaching 0.89 (95% CI 0.88 to 0.89). Significant methodological limitations were observed in many studies, suggesting caution when comparing tools based solely on the AUC.

External validation of the Garvan nomograms for predicting absolute fracture risk: the Tromsø study

Background

Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort.

Methods

The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance.

Results

The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture.

Conclusions

The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction.

Performance of risk assessment instruments for predicting osteoporotic fracture risk: a systematic review

We systematically reviewed the literature on the performance of osteoporosis absolute fracture risk assessment instruments. Relatively few studies have evaluated the calibration of instruments in populations separate from their development cohorts, and findings are mixed. Many studies had methodological limitations making susceptibility to bias a concern.

INTRODUCTION

The aim of this study was to systematically review the literature on the performance of osteoporosis clinical fracture risk assessment instruments for predicting absolute fracture risk, or calibration, in populations other than their derivation cohorts.

METHODS

We performed a systematic review, and MEDLINE, Embase, Cochrane Library, and multiple other literature sources were searched. Inclusion and exclusion criteria were applied and data extracted, including information about study participants, study design, potential sources of bias, and predicted and observed fracture probabilities.

RESULTS

A total of 19,949 unique records were identified for review. Fourteen studies met inclusion criteria. There was substantial heterogeneity among included studies. Six studies assessed the WHO's Fracture Risk Assessment (FRAX) instrument in five separate cohorts, and a variety of risk assessment instruments were evaluated in the remainder of the studies. Approximately half found good instrument calibration, with observed fracture probabilities being close to predicted probabilities for different risk categories. Studies that assessed the calibration of FRAX found mixed performance in different populations. A similar proportion of studies that evaluated simple risk assessment instruments (≤5 variables) found good calibration when compared with studies that assessed complex instruments (>5 variables). Many studies had methodological features making them susceptible to bias.

CONCLUSIONS

Few studies have evaluated the performance or calibration of osteoporosis fracture risk assessment instruments in populations separate from their development cohorts. Findings are mixed, and many studies had methodological limitations making susceptibility to bias a possibility, raising concerns about use of these tools outside of the original derivation cohorts. Further studies are needed to assess the calibration of instruments in different populations prior to widespread use.

Comparison between various fracture risk assessment tools

The suboptimal performance of bone mineral density as the sole predictor of fracture risk and treatment decision making has led to the development of risk prediction algorithms that estimate fracture probability using multiple risk factors for fracture, such as demographic and physical characteristics, personal and family history, other health conditions, and medication use. We review theoretical aspects for developing and validating risk assessment tools, and illustrate how these principles apply to the best studied fracture probability tools: the World Health Organization FRAX®, the Garvan Fracture Risk Calculator, and the QResearch Database's QFractureScores. Model development should follow a systematic and rigorous methodology around variable selection, model fit evaluation, performance evaluation, and internal and external validation. Consideration must always be given to how risk prediction tools are integrated into clinical practice guidelines to support better clinical decision making and improved patient outcomes. Accurate fracture risk assessment can guide clinicians and individuals in understanding the risk of having an osteoporosis-related fracture and inform their decision making to mitigate these risks.

Secondary prevention and estimation of fracture risk

The key questions addressed in this chapter are: • How can individual risk of fracture be best estimated? • What is the best system to prevent a further fracture? • How to implement systems for preventing further fractures? Absolute fracture risk calculators (FRCs) provide a means to estimate an individual's future fracture risk. FRCs are widely available and provide clinicians and patients a platform to discuss the need for intervention to prevent fragility fractures. Despite availability of effective osteoporosis medicines for almost two decades, most patients presenting with new fragility fractures do not receive secondary preventive care. The Fracture Liaison Service (FLS) model has been shown in a number of countries to eliminate the care gap in a clinically and cost-effective manner. Leading international and national organisations have developed comprehensive resources and/or national strategy documents to provide guidance on implementation of FLS in local, regional and national health-care systems.

New horizons in fracture risk assessment

Fracture is the clinical outcome of concern in osteoporosis, a disease variably defined over the last 30 years, mostly in terms of bone mineral density (BMD). However, an 'osseocentric' view of the condition may have hampered our understanding of how best to identify patients at the greatest risk of fragility fracture. More recently, the identification of a number of clinical risk factors for fragility fracture and the creation of fracture risk assessment tools, such as FRAX®, QFracture and Garvan have helped in a move towards clinically useful definitions, using the common currency of 10-year major osteoporotic and 10-year hip fracture risks. However, there are a large number of available fracture risk assessment tools and there remain few validation studies comparing their performance. The National Institute for Health and Clinical Excellence has recently advocated the use of these methods in case finding and studies are underway in their clinical application. It seems likely that the operational definition of osteoporosis must now include fracture risk, which will never replace fracture incidence as a measure of clinical efficacy but may be used in future studies to define patient groups likely to benefit from intervention. We still need to understand more about the performance of these tools, particularly in the context of specific patient groups, such as those with vertebral osteoporosis, the frail, those who fall and patients with secondary osteoporosis.

Risk assessment tools to identify women with increased risk of osteoporotic fracture: complexity or simplicity? A systematic review

A huge number of risk assessment tools have been developed. Far from all have been validated in external studies, more of them have absence of methodological and transparent evidence, and few are integrated in national guidelines. Therefore, we performed a systematic review to provide an overview of existing valid and reliable risk assessment tools for prediction of osteoporotic fractures. Additionally, we aimed to determine if the performance of each tool was sufficient for practical use, and last, to examine whether the complexity of the tools influenced their discriminative power. We searched PubMed, Embase, and Cochrane databases for papers and evaluated these with respect to methodological quality using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS) checklist. A total of 48 tools were identified; 20 had been externally validated, however, only six tools had been tested more than once in a population-based setting with acceptable methodological quality. None of the tools performed consistently better than the others and simple tools (i.e., the Osteoporosis Self-assessment Tool [OST], Osteoporosis Risk Assessment Instrument [ORAI], and Garvan Fracture Risk Calculator [Garvan]) often did as well or better than more complex tools (i.e., Simple Calculated Risk Estimation Score [SCORE], WHO Fracture Risk Assessment Tool [FRAX], and Qfracture). No studies determined the effectiveness of tools in selecting patients for therapy and thus improving fracture outcomes. High-quality studies in randomized design with population-based cohorts with different case mixes are needed.

Differing risk profiles for individual fracture sites: evidence from the Global Longitudinal Study of Osteoporosis in Women (GLOW)

The purposes of this study were to examine fracture risk profiles at specific bone sites, and to understand why model discrimination using clinical risk factors is generally better in hip fracture models than in models that combine hip with other bones. Using 3-year data from the GLOW study (54,229 women with more than 4400 total fractures), we present Cox regression model results for 10 individual fracture sites, for both any and first-time fracture, among women aged ≥55 years. Advanced age is the strongest risk factor in hip (hazard ratio [HR] = 2.3 per 10-year increase), pelvis (HR = 1.8), upper leg (HR = 1.8), and clavicle (HR = 1.7) models. Age has a weaker association with wrist (HR = 1.1), rib (HR = 1.2), lower leg (not statistically significant), and ankle (HR = 0.81) fractures. Greater weight is associated with reduced risk for hip, pelvis, spine, and wrist, but higher risk for first lower leg and ankle fractures. Prior fracture of the same bone, although significant in nine of 10 models, is most strongly associated with spine (HR = 6.6) and rib (HR = 4.8) fractures. Past falls are important in all but spine models. Model c indices are ≥0.71 for hip, pelvis, upper leg, spine, clavicle, and rib, but ≤0.66 for upper arm/shoulder, lower leg, wrist, and ankle fractures. The c index for combining hip, spine, upper arm, and wrist (major fracture) is 0.67. First-time fracture models have c indices ranging from 0.59 for wrist to 0.78 for hip and pelvis. The c index for first-time major fracture is 0.63. In conclusion, substantial differences in risk profiles exist among the 10 bones considered.

Fracture risk assessment: state of the art, methodologically unsound, or poorly reported?

Osteoporotic fractures, including hip fractures, are a global health concern associated with significant morbidity and mortality as well as a major economic burden. Identifying individuals who are at an increased risk of osteoporotic fracture is an important challenge to be resolved. Recently, multivariable prediction tools have been developed to assist clinicians in the management of their patients by calculating their 10-year risk of fracture (FRAX, QFracture, Garvan) using a combination of known risk factors. These prediction models have revolutionized the way clinicians assess the risk of fracture. Studies evaluating the performance of prediction models in this and other areas of medicine have, however, been characterized by poor design, methodological conduct, and reporting. We examine recently developed fracture prediction models and critically discuss issues in their design, validation, and transparency.

Fracture risk assessment

Having traditionally relied on measurements of bone mineral density, it is now established that the consideration of other risk variables improves the categorisation of fracture risk. Whereas several models are available, the FRAX models are the most extensively used. The approach uses easily obtained clinical risk factors to estimate 10 year fracture probability, with or without femoral neck bone mineral density (BMD), to enhance fracture risk prediction. It has been constructed and validated using primary data from population based cohorts around the world, including centres from North America, Europe, Asia and Australia. The FRAX® tool should not be considered as a gold standard, but rather as a platform technology on which to build as new validated risk indicators become available. Notwithstanding, the present models provide an aid to enhance patient assessment by the integration of clinical risk factors alone and/or in combination with BMD.

Pitfalls in the external validation of FRAX

SUMMARY

Recent studies have evaluated the performance of FRAX® in independent cohorts. The interpretation of most is problematic for reasons summarised in this perspective.

INTRODUCTION

FRAX is an extensively validated assessment tool for the prediction of fracture in men and women. The aim of this study was to review the methods used since the launch of FRAX to further evaluate this instrument.

METHODS

This covers a critical review of studies investigating the calibration of FRAX or assessing its performance characteristics in external cohorts.

RESULTS

Most studies used inappropriate methodologies to compare the performance characteristics of FRAX with other models. These included discordant parameters of risk (comparing incidence with probabilities), comparison with internally derived predictors and inappropriate use and interpretation of receiver operating characteristic curves. These deficits markedly impair interpretation of these studies.

CONCLUSION

Cohort studies that have evaluated the performance of FRAX need to be interpreted with caution and preferably re-evaluated.

Predicting fractures in an international cohort using risk factor algorithms without BMD

Clinical risk factors are associated with increased probability of fracture in postmenopausal women. We sought to compare prediction models using self-reported clinical risk factors, excluding BMD, to predict incident fracture among postmenopausal women. The GLOW study enrolled women aged 55 years or older from 723 primary-care practices in 10 countries. The population comprised 19,586 women aged 60 years or older who were not receiving antiosteoporosis medication and were followed annually for 2 years. Self-administered questionnaires were used to collect data on characteristics, fracture risk factors, previous fractures, and health status. The main outcome measure compares the C index for models using the WHO Fracture Risk (FRAX), the Garvan Fracture Risk Calculator (FRC), and a simple model using age and prior fracture. Over 2 years, 880 women reported incident fractures including 69 hip fractures, 468 "major fractures" (as defined by FRAX), and 583 "osteoporotic fractures" (as defined by FRC). Using baseline clinical risk factors, both FRAX and FRC showed a moderate ability to correctly order hip fracture times (C index for hip fracture 0.78 and 0.76, respectively). C indices for "major" and "osteoporotic" fractures showed lower values, at 0.61 and 0.64. Neither algorithm was better than the model based on age + fracture history alone (C index for hip fracture 0.78). In conclusion, estimation of fracture risk in an international primary-care population of postmenopausal women can be made using clinical risk factors alone without BMD. However, more sophisticated models incorporating multiple clinical risk factors including falls were not superior to more parsimonious models in predicting future fracture in this population.