Garvan Fracture Risk Calculator Research Articles

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

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. 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. 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. 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. 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. 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.

Fracture prediction tools in diabetes.

Type 1 (T1D) and 2 diabetes (T2D) are associated with increased risk of fracture independent of bone mineral density (BMD). Fracture risk prediction tools can identify individuals at highest risk, and therefore, most likely to benefit from antifracture therapy. This review summarizes recent advances in fracture prediction tools as applied to individuals with diabetes. The Fracture Risk Assessment (FRAX) tool, Garvan Fracture Risk Calculator (FRC), and QFracture tool are validated tools for fracture risk prediction. FRAX is most widely used internationally, and considers T1D (but not T2D) under secondary osteoporosis disorders. FRAX underestimates fracture risk in both T1D and T2D. Trabecular bone score and other adjustments for T2D-associated risk improve FRAX-based estimations. Similar adjustments for T1D are not identified. Garvan FRC does not incorporate diabetes as an input but does includes falls. Garvan FRC slightly underestimates osteoporotic fracture risk in women with diabetes. QFracture incorporates both T1D and T2D and falls as input variables, but has not been directly validated in individuals with diabetes. Further research is needed to validate and compare available fracture prediction tools and their performance in individuals with diabetes.

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

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

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

The optimal model for fracture risk estimation to guide treatment decision-making remains controversial. Our objective was toevaluate the predictive performance of the Garvan Fracture Risk Calculator (FRC) in a large clinical registry from Manitoba, Canada. Using the population-based Manitoba Bone Mineral Density (BMD) registry, we identified women and men aged 50-95years 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. We included 16,682 women (mean age 66.6 + / - SD 8.7years) and 2839 men (mean age 68.7 + / - SD 10.2years). During a mean observation time of 2.6years, 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. Garvan FRC outperformed CRFs and BMD alone for fracture risk stratification, particularly for HF, but may require recalibration for accurate predictions in this population.

Multimorbidity Increases Risk of Osteoporosis Under-Diagnosis and Under-Treatment in Patients at High Fracture Risk: 45 and up a Prospective Population Based-Study

Background: Management of osteoporosis following fracture is suboptimal. Multimorbidity adds to clinical management complexity in the elderly but its contribution to the osteoporosis treatment gap has never been investigated. Objectives: To determine the impact of multimorbidity on fracture risk and on osteoporosis investigation and treatment in patients at high fracture risk. Design and Setting: The 45 and Up Study is a prospective population-based cohort study in NSW, Australia with questionnaire data linked to hospital records by the Centre for Health Record Linkage (CHeReL) and the Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Scheme (MBS) data provided by Department of Human Services. Fractures identified from hospital records, comorbidities from questionnaires, hospital and PBS records. Bone mineral density (BMD) investigation obtained from MBS and treatment for osteoporosis from PBS. Participants: 16191 women and 9089 men with incident low-trauma fracture (2000 - 2017) classified in a high and low-risk group based on 10-year fracture risk threshold of 20% from the Garvan Fracture Risk Calculator (age, gender, prior fracture and falls). Main Outcome Measurements: Association of Charlson comorbidity index (CCI) with fracture. Likelihood of BMD investigation and treatment initiation. Outcomes ascertained by logistic regression and re-fracture risk by Cox models. Results: Individuals at high fracture risk were significantly older [women (mean age ±SD) 77 ± 10 vs 57 ±4 for high- vs low risk and men 86±5 vs 65±8 for high vs low risk] and had a higher morbidity burden [women, CCI ≥ 2 40% vs 12% for high- vs low-risk and men 53% vs 26% for high vs low risk]. Being in the high-risk group as well as a higher CCI were independently associated with > 2-fold higher risk of re-fracture. However, in the high-risk group, only 28% (48% women and 17% men) had a BMD investigation and 31% (24% women and 14% men) received anti-osteoporosis medication post-fracture. A higher CCI was associated with a lower probability of both BMD investigation [CCI 2–3 vs 0–1, RR 0.73 (0.65–0.82) for women, and 0.50 (0.40–0.64) for men and CCI ≥4 vs 0–1, RR 0.50 (0.41- 0.62) for women and 0.36 (0.25–0.52) for men] and treatment initiation [CCI 2–3 vs 0–1, RR 0.88 (0.77–0.98) for women and 0.75 (0.60–0.95) for men and CCI ≥4 vs 0–1, RR 0.75 (0.59- 0.95) for women and 0.35 (0.23–0.53) for men]. Conclusion: Multimorbidity, despite being associated with the highest fracture risk, significantly lowers the likelihood of osteoporosis investigation and treatment. These findings suggest that fracture risk is either under-estimated or under-prioritized in the context of multimorbidity. Our findings highlight the need for improved delivery of fracture preventive care in this setting. More generally, they also point out the need for a better understanding of how problems are prioritized in complex clinical situations.

Fracture Risk Assessment: An Update.

Our ability to accurately identify high fracture risk in individuals has improved as the volume of clinical data has expanded and fracture risk assessment tools have been developed. Given its accessibility, affordability, and low radiation exposure, dual x-ray absorptiometry (DXA) remains the standard for osteoporosis screening and monitoring response to treatment. The trabecular bone score (TBS) is a DXA software add-on that uses lumbar spine DXA imaging to produce an output that correlates with bone microarchitecture. It has been identified as an independent fracture risk factor and may prove useful in further stratifying fracture risk among those with a bone mineral density (BMD) in the osteopenic range (-1.0 to -2.4 standard deviations), in those with low-energy fractures but normal or only mildly low BMD, or in those with conditions known to impair bone microarchitecture. Fracture risk assessment tools, including the Fracture Risk Assessment Tool (FRAX), Garvan fracture risk calculator, and QFracture, evaluate the impact of multiple clinical factors on fracture risk, even in the absence of BMD data. Each produces an absolute fracture risk output over a defined interval of time. When used appropriately, these enhance our ability to identify high-risk patients and allow us to differentiate fracture risk among patients who present with similar BMDs. For challenging clinical cases, a combined approach is likely to improve accuracy in the identification of high-risk patients who would benefit from the available osteoporosis therapies.

Fracture risk prediction and the decision to treat low bone density

In patients without a prevalent fracture, guidelines recommend initiating therapy based on a calculation of absolute fracture risk. Two common calculators are used in Australia - FRAX (Australia) and Garvan Fracture Risk Calculator (Garvan). The aim of this article is to examine whether the decision to treat with bone-preserving medication would be different depending on which calculator was used. Data were entered into each calculator for hypothetical male and female patients, aged 50-85 years, with femoral neck t-scores from +3.0 to -3.0. Garvan consistently predicted a higher absolute fracture risk than FRAX (Australia). The discrepancy increased with increasing age and decreasing bonemineral density, and was most pronounced in the prediction of any fracture, but less so for hip fracture. The decision to prescribe osteoporosis medications for a patient on the basis of fracture risk may depend on which risk calculator is used. Differences in the calculator methods contribute to the discrepancy between them.

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

SummaryThe study project was designed to assess the concordance of clinical results in the assessment of 5-year fracture risk of any fracture, carried out by two methods: the Garvan algorithm and the POL-RISK model. The study group included 389 postmenopausal women of Caucasian race. The concordance of results, obtained by those two models, turned out to be moderate, and the threshold for high fracture risk group was 11% in the POL-RISK model.PurposeThe goal of the study was to evaluate the concordance of results in fracture risk assessments between the Garvan Fracture Risk Calculator and POL-RISK, a new Polish algorithm, and to define an optimal threshold for intervention.MethodsThe study was a part of the Silesia Osteo Active Study. A group of 389 postmenopausal women, aged 65.2±6.9 years (mean ± SD), was randomly selected from the general population of Zabrze, Poland. All the participants had bone densitometry examination to assess the bone mineral density of the femoral neck. The mean femoral neck T-score was (−0.99) ± 1.05 SD. 6.4% of the women revealed osteoporosis. Five-year risk of any fracture was assessed, using the Garvan and POL-RISK calculators. The performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC).ResultsThe median 5-year risk of any fracture was 7% (range 1–54%) in the Garvan model and 8.8% (range 1.1–45.5%) in the POL-RISK algorithm. There was a significant correlation between the results obtained by both methods (r=0.6, p<0.005). For the thresholds, assumed at 8% and 13% (according to recommendation derived from Garvan tool), the rates of concordance of results between both calculators were 76% and 84%, respectively. In ROC analysis for the POL-RISK method, performed with reference to the Garvan method at two different cut-offs, assumed to be high fracture risk indicators (8% and 13%), the AUC values were 0.865 and 0.884, respectively. The optimal threshold for high fracture risk in the POL-RISK algorithm was ≥ 11%, which yielded a sensitivity of 0.94 and a specificity of 0.71.ConclusionThe obtained data demonstrate a moderate concordance of results between the POL-RISK algorithm and the Garvan model, illustrated by low and high fracture risk cut-offs, established in ROC analysis. In addition, the threshold of 11% in the POL-RISK method was the optimal level for “high risk”.

Fracture risk assessment in postmenopausal elderly women of an urban area using fracture prediction tools: A cross-sectional study

Context: Osteoporosis is a commonplace metabolic disorder affecting millions of women worldwide. This study highlights the prevalence of various risk factors associated with the development of osteoporosis in postmenopausal women in the Indian population and the determination of risk of fractures in the same using questionnaire-based tools. Aims: The aim of the study is to estimate the prevalence of risk factors for fractures and determine the 10-year probability of developing major osteoporotic fracture and major hip fractures using QFracture score and Garvan Fracture risk calculator in postmenopausal women. Settings and Design: This was a cross-sectional analytical study. Subjects and Methods: A total of 384 patients were evaluated to estimate the prevalence of risk factors for fractures and the 10-year probability of developing major osteoporotic fracture and major hip fractures along with comparison of clinical risk factors between patients with history of prior falls and patients without history of prior falls using the QFracture score and Garvan Fracture risk calculator. Statistical Analysis Used: Mann–Whitney U-test for estimating the overall 10-year risk of developing major osteoporotic and hip fractures using the two scoring systems. Results: The age range of our study population was 50–86 years, while the mean age was 59.86 ± 8.86 years. The most prevalent risk factor for osteoporotic fractures in our study was diabetes mellitus (n = 98 [25.5%]), and the least prevalent risk factor was chronic kidney disease (n = 4 [1.04%]). We also estimated the overall 10-year risk of developing major osteoporotic and hip fractures in two groups: Group 1 (with a history of falls) and Group 2 (without any history of falls). Conclusion: QFracture score and Garvan tool are easily administrable, internationally validated questionnaires which provide definitive information related to risk factors for development of osteoporosis.

Case finding with GARVAN fracture risk calculator in primary prevention of fragility fractures in older people

BackgroundFracture risk calculators (FRC) with DXA can guide osteoporosis (OP) management in the absence of dual X-ray absorptiometry (DXA). There is little information of the role of FRC without DXA. ObjectivesDetermine the accuracy of age-stratified Garvan FRC thresholds without DXA to manage OP. MethodsCross-sectional study of 531 participants, ≥70 years old who underwent DXA and had Garvan FRC scores with and without DXA calculated. Age-stratified Garvan scores without DXA, generated low (no action), moderate (order DXA) or high (treat without DXA) risk thresholds of OP. Accuracy of our thresholds were assessed against DXA confirmed OP. ResultsAge-specific GARVAN thresholds resulted in the correct decision in 85–88% of cases; “over-treated” OP in 7–8%; and, missed OP in 5–8%. 256 (48%) DXAs were unnecessary. Compared to recommended guidelines, Garvan HF and MOF thresholds improved accuracy of clinical decisions by 31% and 12%, respectively. ConclusionsAge-specific FRC score thresholds successfully identified who required treatment or DXA, with potential to reduce unnecessary DXA.

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.

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.

Beyond mobility assessment: Timed up and go test and its relationship to osteoporosis and fracture risk

BackgroundFracture determinants are falls, bone fragility, imbalance, and decreased lower limb strength. The timed up and go (TUG) test assesses most of the fracture determinants. AimTo assess the relationship between mobility status using TUG test, bone mineral density (BMD), and different fracture risks predicted by different tools. MethodsA case (TUG time > 20 seconds)–control (TUG ≤ 20 seconds) study comprised 66 patients and 72 controls. Participants were assessed for falls, fracture history, and BMD using dual energy X-ray absorptiometry; the estimated 10-year fracture risk was also calculated using both the World Health Organization fracture risk assessment tool and Garvan fracture risk calculator. ResultsPatients had a lower femoral BMD (p = 0.009), T score (p = 0.003), and Z score (p = 0.001). Femur neck osteoporosis had a higher number of patients (p < 0.001). Patients also had lower lumbar BMD (p = 0.02), T-score (p = 0.02), and Z-score (p = 0.005). The estimated 10-year fracture risk for hip and other osteoporotic fractures were higher among the patients using both fracture risk assessment tool and Garvan calculators. ConclusionPoor TUG test results are associated with lower BMD and higher estimated 10 year fracture risk.

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

BackgroundAbsolute 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.MethodsThe 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.ResultsThe 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.ConclusionsThe 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.

The utility of absolute risk prediction using FRAX® and Garvan Fracture Risk Calculator in daily practice

ObjectivesThere are two commonly used fracture risk prediction tools FRAX® and Garvan Fracture Risk Calculator (GARVAN-FRC). The objective of this study was to investigate the utility of these tools in daily practice. Study designA prospective population-based 5-year follow-up study was conducted in ten general practice centres in the Netherlands. For the analyses, the FRAX® and GARVAN-FRC 10-year absolute risks (FRAX® does not have 5-year risk prediction) for all fractures were used. ResultsAmong 506 postmenopausal women aged ≥60 years (mean age: 67.8±5.8 years), 48 (9.5%) sustained a fracture during follow-up. Both tools, using BMD values, distinguish between women who did and did not fracture (10.2% vs. 6.8%, respectively for FRAX® and 32.4% vs. 39.1%, respectively for GARVAN-FRC, p<0.0001) at group level. However, only 8.9% of those who sustained a fracture had an estimated fracture risk ≥20% using FRAX® compared with 53.3% using GARVAN-FRC. Although both underestimated the observed fracture risk, the GARVAN-FRC performed significantly better for women who sustained a fracture (higher sensitivity) and FRAX® for women who did not sustain a fracture (higher specificity). Similar results were obtained using age related cut off points. ConclusionsThe discriminant value of both models is at least as good as models used in other medical conditions; hence they can be used to communicate the fracture risk to patients. However, given differences in the estimated risks between FRAX® and GARVAN-FRC, the significance of the absolute risk must be related to country-specific recommended intervention thresholds to inform the patient.

Effect of additional sensory information on functional mobility and static balance of fallers elderly

risk based guidelines were used as compared to using the current Danish guidelines. Methods.– Female patients attending a falls clinic and evaluated by bone mineral density bone densitometry and referred for bone densitometry were studied. 10-year fracture risks were calculated using the Garvan Fracture Risk Calculator and FRAXrax (WHOFracture Risk Assessment Tool). tools. PercentagePercentages of patients eligible for treatment wereas determined using fourusing four guidelines: Danish Bone Society (DBS), the FRAX based age-dependent UK National Osteoporosis Guideline Group (NOGG), the US National Osteoporosis Foundation FRAX based 3% (hip)/20% (any) cut) cut-off guidelinesoff guidelines (US-FRAX) and the Australian Garvan derived 20% cut-off guidelines (Garvan). Results.– Median age [IQR] of patients (n=85) was 81 years ([IQR=75–85]). Mean 10-year % risk (SD) of major/any osteoporotic fractures wererisk (SD) ofmajor/any osteoporotic fractures was: DK-FRAX 29.8% (12.8), UK-FRAX 19.9% (9.5), US-FRAX 21.8% (10.3), Garvan 50.2% (23.8). Proportion of patients (95%CI) eligible for treatment were:wasere; DBS 55.3% (44.7–65.9), NOGG 51.2% (40.5–61.9), US-FRAX 87.1% (80.0–94.2), Garvan 90.6% (84.4–96.8). Conclusion.– These data show that the choice of guideline has a major impact on treatment decisions in this group of older Danish women with falls. Adherence to the US-FRAX and Garvan algorithms would result in extremely discrepant treatment rates compared to adherence with the DBS and UK-NOGG guidelines.

Individualized fracture risk assessment

Fragility fracture is a major public health burden, because it is associated with a substantial morbidity and mortality. Risk prediction models, including the Fracture Risk Assessment Tool (FRAX) and Garvan Fracture Risk Calculator (GFRC), have been developed to provide a useful clinical framework for communicating the risk of fracture. The present review examines the validation of risk prediction models in osteoporosis and identifies some major challenges. Recent validation studies suggested that the area under the ROC curve in fracture discrimination ranged from 0.61 to 0.83 for FRAX, and from 0.63 to 0.88 for GFRC, with hip fracture having a better discrimination than fragility fractures as a group. FRAX substantially underestimated the risk of fracture, whereas the predicted risk by GFRC was close to or slightly higher than the actual risk. Results of post-hoc analyses of clinical trials indicated the antifracture efficacy of alendronate, coronate, bazedoxifene, and denosumab was greater in patients with higher predicted risk of fracture. However, there was no correlation between antifracture efficacy and predicted fracture risk among patients on raloxifene and strontium ranelate. The prognostic performance of FRAX and GFRC for fracture prediction is not perfect, but these predictive models can aid patients and doctors to 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 the risk of fracture and its severity outcomes.

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.

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.

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.