A meta-analysis of previous falls and subsequent fracture risk in cohort studies

The relationship between self-reported falls and fracture risk was estimated in an international meta-analysis of individual-level data from 46 prospective cohorts. Previous falls were associated with an increased fracture risk in women and men and should be considered as an additional risk factor in the FRAX® algorithm.

INTRODUCTION

Previous falls are a well-documented risk factor for subsequent fracture but have not yet been incorporated into the FRAX algorithm. The aim of this study was to evaluate, in an international meta-analysis, the association between previous falls and subsequent fracture risk and its relation to sex, age, duration of follow-up, and bone mineral density (BMD).

METHODS

The resource comprised 906,359 women and men (66.9% female) from 46 prospective cohorts. Previous falls were uniformly defined as any fall occurring during the previous year in 43 cohorts; the remaining three cohorts had a different question construct. The association between previous falls and fracture risk (any clinical fracture, osteoporotic fracture, major osteoporotic fracture, and hip fracture) was examined using an extension of the Poisson regression model in each cohort and each sex, followed by random-effects meta-analyses of the weighted beta coefficients.

RESULTS

Falls in the past year were reported in 21.4% of individuals. During a follow-up of 9,102,207 person-years, 87,352 fractures occurred of which 19,509 were hip fractures. A previous fall was associated with a significantly increased risk of any clinical fracture both in women (hazard ratio (HR) 1.42, 95% confidence interval (CI) 1.33-1.51) and men (HR 1.53, 95% CI 1.41-1.67). The HRs were of similar magnitude for osteoporotic, major osteoporotic fracture, and hip fracture. Sex significantly modified the association between previous fall and fracture risk, with predictive values being higher in men than in women (e.g., for major osteoporotic fracture, HR 1.53 (95% CI 1.27-1.84) in men vs. HR 1.32 (95% CI 1.20-1.45) in women, P for interaction = 0.013). The HRs associated with previous falls decreased with age in women and with duration of follow-up in men and women for most fracture outcomes. There was no evidence of an interaction between falls and BMD for fracture risk. Subsequent risk for a major osteoporotic fracture increased with each additional previous fall in women and men.

CONCLUSIONS

A previous self-reported fall confers an increased risk of fracture that is largely independent of BMD. Previous falls should be considered as an additional risk factor in future iterations of FRAX to improve fracture risk prediction.

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.

A systematic review on the performance of fracture risk assessment tools: FRAX, DeFRA, FRA-HS

PURPOSE

Preventing fragility fractures by treating osteoporosis may reduce disability and mortality worldwide. Algorithms combining clinical risk factors with bone mineral density have been developed to better estimate fracture risk and possible treatment thresholds. This systematic review supported panel members of the Italian Fragility Fracture Guidelines in recommending the use of best-performant tool. The clinical performance of the three most used fracture risk assessment tools (DeFRA, FRAX, and FRA-HS) was assessed in at-risk patients.

METHODS

PubMed, Embase, and Cochrane Library were searched till December 2020 for studies investigating risk assessment tools for predicting major osteoporotic or hip fractures in patients with osteoporosis or fragility fractures. Sensitivity (Sn), specificity (Sp), and areas under the curve (AUCs) were evaluated for all tools at different thresholds. Quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2; certainty of evidence (CoE) was evaluated using the Grading of Recommendations Assessment, Development and Evaluation approach.

RESULTS

Forty-three articles were considered (40, 1, and 2 for FRAX, FRA-HS, and DeFRA, respectively), with the CoE ranging from very low to high quality. A reduction of Sn and increase of Sp for major osteoporotic fractures were observed among women and the entire population with cut-off augmentation. No significant differences were found on comparing FRAX to DeFRA in women (AUC 59-88% vs. 74%) and diabetics (AUC 73% vs. 89%). FRAX demonstrated non-significantly better discriminatory power than FRA-HS among men.

CONCLUSION

The task force formulated appropriate recommendations on the use of any fracture risk assessment tools in patients with or at risk of fragility fractures, since no statistically significant differences emerged across different prediction tools.

Executive summary: Italian guidelines for diagnosis, risk stratification, and care continuity of fragility fractures 2021

Background

Fragility fractures are a major public health concern owing to their worrying and growing burden and their onerous burden upon health systems. There is now a substantial body of evidence that individuals who have already suffered a fragility fracture are at a greater risk for further fractures, thus suggesting the potential for secondary prevention in this field.

Purpose

This guideline aims to provide evidence-based recommendations for recognizing, stratifying the risk, treating, and managing patients with fragility fracture. This is a summary version of the full Italian guideline.

Methods

The Italian Fragility Fracture Team appointed by the Italian National Health Institute was employed from January 2020 to February 2021 to (i) identify previously published systematic reviews and guidelines on the field, (ii) formulate relevant clinical questions, (iii) systematically review literature and summarize evidence, (iv) draft the Evidence to Decision Framework, and (v) formulate recommendations.

Results

Overall, 351 original papers were included in our systematic review to answer six clinical questions. Recommendations were categorized into issues concerning (i) frailty recognition as the cause of bone fracture, (ii) (re)fracture risk assessment, for prioritizing interventions, and (iii) treatment and management of patients experiencing fragility fractures. Six recommendations were overall developed, of which one, four, and one were of high, moderate, and low quality, respectively.

Conclusions

The current guidelines provide guidance to support individualized management of patients experiencing non-traumatic bone fracture to benefit from secondary prevention of (re)fracture. Although our recommendations are based on the best available evidence, questionable quality evidence is still available for some relevant clinical questions, so future research has the potential to reduce uncertainty about the effects of intervention and the reasons for doing so at a reasonable cost.

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.

How well do the FRAX (Australia) and Garvan calculators predict incident fractures? Data from the Geelong Osteoporosis Study

This study reports that both FRAX and Garvan calculators underestimated fractures in Australian men and women, particularly in those with osteopenia or osteoporosis. Major osteoporotic fractures were poorly predicted, while both calculators performed acceptably well for hip fractures.

INTRODUCTION

This study assessed the ability of the FRAX (Australia) and Garvan calculators to predict fractures in Australian women and men.

METHODS

Women (n = 809) and men (n = 821) aged 50-90 years, enrolled in the Geelong Osteoporosis Study, were included. Fracture risk was estimated using FRAX and Garvan calculators with and without femoral neck bone mineral density (BMD) (FRAX_BMD, FRAX_noBMD, Garvan_BMD, Garvan_noBMD). Incident major osteoporotic (MOF), fragility, and hip fractures over the following 10 years were verified radiologically. Differences between observed and predicted numbers of fractures were assessed using a chi-squared test. Diagnostics indexes were calculated.

RESULTS

In women, 115 MOF, 184 fragility, and 42 hip fractures occurred. For men, there were 73, 109, and 17 fractures, respectively. FRAX underestimated MOFs, regardless of sex or inclusion of BMD. FRAX accurately predicted hip fractures, except in women with BMD (20 predicted, p = 0.004). Garvan underestimated fragility fractures except in men using BMD (88 predicted, p = 0.109). Garvan accurately predicted hip fractures except for women without BMD (12 predicted, p < 0.001). Fractures were underestimated primarily in the osteopenia and osteoporosis groups; MOFs in the normal BMD group were only underestimated by FRAX_BMD and fragility fractures by Garvan_noBMD, both in men. AUROCs were not different between scores with and without BMD, except for fragility fractures predicted by Garvan in women (0.696, 95% CI 0.652-0.739 and 0.668, 0.623-0.712, respectively, p = 0.008) and men, which almost reached significance (0.683, 0.631-0.734, and 0.667, 0.615-0.719, respectively, p = 0.051). Analyses of sensitivity and specificity showed overall that MOFs and fragility fractures were poorly predicted by both FRAX and Garvan, while hip fractures were acceptably predicted.

CONCLUSIONS

Overall, the FRAX and Garvan calculators underestimated MOF and fragility fractures, particularly in individuals with osteopenia or osteoporosis. Hip fractures were predicted better by both calculators. AUROC analyses suggest that Garvan_BMD performed better than Garvan_noBMD for prediction of fragility fractures.

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.

A Substruction Approach to Assessing the Theoretical Validity of Measures

BACKGROUND

Validity is about the logic, meaningfulness, and evidence used to defend inferences made when interpreting results. Substruction is a heuristic or process that visually represent the hierarchical structure between theory and measures.

PURPOSE

To describe substruction as a method for assessing the toretical validity of research measures.

METHODS

Using Fawcett's Conceptual-Theoretical-Empirical Structure. an exemplar is presented of substruction from the Individual and Family Self-Management Theory to the Striving to be strong study concepts and empirical measures.

RESULTS

Substruction tables display evidence supporting theoretical validity of the instruments used in the study.

CONCLUSION

A high degree of congruence between theory and measure is critical to support the validity of the theory and to support attributions made about moderating, mediating, causal relationships, and intervention effects.

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.

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.

External validation and comparison of three prediction tools for risk of osteoporotic fractures using data from population based electronic health records: retrospective cohort study

OBJECTIVE

 To directly compare the performance and externally validate the three most studied prediction tools for osteoporotic fractures-QFracture, FRAX, and Garvan-using data from electronic health records.

DESIGN

 Retrospective cohort study.

SETTING

 Payer provider healthcare organisation in Israel.

PARTICIPANTS

 1 054 815 members aged 50 to 90 years for comparison between tools and cohorts of different age ranges, corresponding to those in each tools' development study, for tool specific external validation.

MAIN OUTCOME MEASURE

 First diagnosis of a major osteoporotic fracture (for QFracture and FRAX tools) and hip fractures (for all three tools) recorded in electronic health records from 2010 to 2014. Observed fracture rates were compared to probabilities predicted retrospectively as of 2010.

RESULTS

 The observed five year hip fracture rate was 2.7% and the rate for major osteoporotic fractures was 7.7%. The areas under the receiver operating curve (AUC) for hip fracture prediction were 82.7% for QFracture, 81.5% for FRAX, and 77.8% for Garvan. For major osteoporotic fractures, AUCs were 71.2% for QFracture and 71.4% for FRAX. All the tools underestimated the fracture risk, but the average observed to predicted ratios and the calibration slopes of FRAX were closest to 1. Tool specific validation analyses yielded hip fracture prediction AUCs of 88.0% for QFracture (among those aged 30-100 years), 81.5% for FRAX (50-90 years), and 71.2% for Garvan (60-95 years).

CONCLUSIONS

 Both QFracture and FRAX had high discriminatory power for hip fracture prediction, with QFracture performing slightly better. This performance gap was more pronounced in previous studies, likely because of broader age inclusion criteria for QFracture validations. The simpler FRAX performed almost as well as QFracture for hip fracture prediction, and may have advantages if some of the input data required for QFracture are not available. However, both tools require calibration before implementation.

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.

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.

Competing mortality and fracture risk assessment

SUMMARY

Failure to account for competing mortality gave higher estimates of 10-year fracture probability than if appropriate adjustment is made for competing mortality, particularly among subgroups with higher mortality. A modified Kaplan-Meier method is easy to implement and provides an alternative approach to existing methods for competing mortality risk adjustment.

INTRODUCTION

A unique feature of FRAX(®) is that 10-year fracture probability accounts for mortality as a competing risk. We compared the effect of competing mortality adjustment on nonparametric and parametric methods of fracture probability estimation.

METHODS

The Manitoba Bone Mineral Density (BMD) database was used to identify men and women age ≥50 years with FRAX probabilities calculated using femoral neck BMD (N = 39,063). Fractures were assessed from administrative data (N = 2,543 with a major osteoporotic fracture, N = 549 with a hip fracture during mean 5.3 years follow-up).

RESULTS

The following subgroups with higher mortality were identified: men, age >80 years, high fracture probability, and presence of diabetes. Failure to account for competing mortality in these subgroups overestimated fracture probability by 16-56 % with the standard nonparametric (Kaplan-Meier) method and 15-29 % with the standard parametric (Cox) model. When the outcome was hip fractures, failure to account for competing mortality overestimated hip fracture probability by 18-36 % and 17-35 %, respectively. A simple modified Kaplan-Meier method showed very close agreement with methods that adjusted for competing mortality (within 2 %).

CONCLUSIONS

Failure to account for competing mortality risk gives considerably higher estimates of 10-year fracture probability than if adjustment is made for this competing risk.

FRAX and the assessment of the risk of developing a fragility fracture

Osteoporosis is common and the health and financial cost of fragility fractures is considerable. The burden of cardiovascular disease has been reduced dramatically by identifying and targeting those most at risk. A similar approach is potentially possible in the context of fragility fractures. The World Health Organization created and endorsed the use of FRAX, a fracture risk assessment tool, which uses selected risk factors to calculate a quantitative, patient-specific, ten-year risk of sustaining a fragility fracture. Treatment can thus be based on this as well as on measured bone mineral density. It may also be used to determine at-risk individuals, who should undergo bone densitometry. FRAX has been incorporated into the national osteoporosis guidelines of countries in the Americas, Europe, the Far East and Australasia. The United Kingdom National Institute for Health and Clinical Excellence also advocates its use in their guidance on the assessment of the risk of fragility fracture, and it may become an important tool to combat the health challenges posed by fragility fractures.