In the longitudinal, retrospective study, the ability of the FRAX, Garvan, and POL-RISK algorithms to predict osteoporotic fractures was compared in a group of 457 women. Using the rigid threshold of 10% showed a significant discrepancy in sensitivity and specificity of all tools. New thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds allow for improving the diagnostic accuracy of all three calculators.
BACKGROUND: The aim of the longitudinal, retrospective study was to compare three tools designed to assess fracture risk: FRAX, Garvan, and POL-RISK in their prediction of fracture incidence.
METHODS: The study group consisted of 457 postmenopausal women with a mean age of 64.21 ± 5.94 years from the Gliwice Osteoporosis (GO) Study. Comprehensive data on clinical factors related to fractures were collected for all participants. Bone densitometry was performed at the proximal femur using the Prodigy device (GE, USA). Fracture risk was established using the FRAX, Garvan, and POL-RISK algorithms. Data on the incidence of osteoporotic fractures were collected over the last 10 years.
RESULTS: During the period of observation 72, osteoporotic fractures occurred in 63 subjects. For a preliminary comparison of the predictive value of analyzed diagnostic tools, the fracture risk threshold of 10% was used. For FRAX, the fracture probability exceeding 10% was observed only in 11 subjects who experienced fractures; thus, the fracture was properly predicted only in 22.9% of women. For Garvan, the respective value was 90.5%, and for POL-RISK, it was 98.4%. That gave a very low true positive value for FRAX and a very high false positive value for Garvan and POL-RISK. Based on ROC curves, new thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds improve the diagnostic accuracy of all compared fracture prediction tools.
CONCLUSIONS: The current study showed that different fracture risk assessment tools, although having similar clinical purposes, require different cut-off thresholds for making therapeutic decisions. Better identification of patients requiring therapy based on such an approach may help reduce the number of new fractures.

Only previous glucocorticoid use and rheumatoid arthritis were predictors of an early fracture (< 2 years after inclusion). A shorter \'time to first fracture\' was not an independent clinical risk factor for imminent fractures.
Risk factors for fragility fractures independent of BMD were assessed in several prediction models. However, predictors of a shorter \'time to first fracture\' and its impact on imminent fractures are unknown.
We studied the concept of \'time to first fracture\' in the FRISBEE (\"Fracture RIsk Brussels Epidemiological Enquiry\") cohort (3560 postmenopausal women). Validated fractures were divided into 3 groups: first fracture < 2 years, 2-5 years, and > 5 years after inclusion. Factors associated with first fracture risk were evaluated with uni- and multivariate analyses using Cox modeling. We examined \'time to first fracture\' as a risk factor for imminent fractures in untreated subjects and in those receiving pharmacological treatment.
Classical risk factors (age, prior fracture, fall history and low BMD) were associated with first fracture in all groups. Previous glucocorticoids and rheumatoid arthritis (RA) were predictors for fracture < 2 years. Imminent fractures were similar in subjects with or without osteoporosis treatment, despite a higher estimated 10-year risk of fragility fracture in those treated, suggesting that treatment is efficient. \'Time to first fracture\' was not an independent risk factor for imminent fractures.
Among the risk factors considered, previous glucocorticoid use and RA were predictors for early fracture, consistent with the concept of very high risk. The \'time to first validated fracture\' was not an independent risk factor for imminent fractures. Patients with a first osteoporotic fracture should thus be considered at very high risk for re-fracture, independent of the \'time to first fracture\'.

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

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.
BACKGROUND: 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.

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.

Human body height loss of 3-4 cm or more may be considered a simple indicator of increasing fracture risk, where the information is very similar to the results from fracture risk assessments by available online calculators, all of them based on a multiple variable approaches.
BACKGROUND: The aim of the study was to assess the relationship between body height loss (HL) and fracture risk in postmenopausal women from the Gliwice Osteoporosis (GO) Study.
METHODS: The study sample included 1735 postmenopausal women, aged over 55 years and recruited at the Osteoporotic Outpatient Clinic. The mean age of the study participants was 68.15 ± 8.16 years. Fracture risk was established, using the fracture risk assessment tool (FRAX) (10-year probability of major and hip fractures), the Garvan calculator (any and hip fractures, 5 and 10 years) and the Polish (POL-RISK) algorithm, available at www. fracture - risk .pl (any fractures, 5 years). Bone densitometry at the femoral neck was performed, using a Prodigy device (Lunar, GE, USA). Body heights were measured before bone densitometry, using a wall stadiometer and compared with the maximum body heights, measured in early adulthood and reported by the study participants themselves.
RESULTS: In 199 women, the body heights, measured during the study, did not change in comparison to their corresponding values in early adulthood, while being decreased in the other 1536 women. The mean height loss (HL) in the whole study group was 3.95 ± 3.24 cm. That HL correlated significantly with the calculated fracture risk (the r range from 0.13 to 0.39, p < 0.0001). In general, regarding the patients with fracture risk close to the recommended therapeutic thresholds, HL was around 3-4 cm, except of the values from the FRAX calculator for major fractures, where the commonly used therapeutic threshold (20%) was related to HL of approximately 6.5 cm. In subjects with HL between 3.5 and 4 cm (n = 208), the FRAX value for major fractures was 6.83 ± 3.74.
CONCLUSIONS: Body height measurements, carried out to establish HL, provide an important information for clinical practice, where HL of 3-4 cm or more may be considered a simple indicator of increasing fracture risk.

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

Whether fracture prediction tools developed for the management of osteoporosis can be used in chronic kidney disease (CKD) is poorly known. We aimed to compare the performance of fracture prediction tools in non-CKD and CKD. We analyzed CARTaGENE, a population-based survey of 40-year-old to 69-year-old individuals recruited between 2009 and 2010. Renal function was assessed using baseline creatinine and categorized according to Kidney Disease Improving Global Outcomes (KDIGO) guidelines (non-CKD, stage 2, stage 3). Individuals without creatinine measurements or with advanced CKD (stage 4 or 5; prevalence <0.25%) were excluded. Predicted 5-year fracture probabilities (using Fracture Risk Assessment Tool [FRAX], QFracture, and Garvan) were computed at baseline. Fracture incidence (major fracture [MOF] or any fracture) was evaluated in administrative databases from recruitment to March 2016. Discrimination (hazard ratios [HRs] per standard deviation [SD] increase in Cox models; c-statistics) and calibration (standardized incidence ratios [SIRs] before and after recalibration) were assessed in each CKD strata. We included 19,393 individuals (9522 non-CKD; 9114 stage 2; 757 stage 3). A total of 830 patients had any fracture during follow-up, including 352 MOF. FRAX (HR = 1.89 [1.63-2.20] non-CKD; 1.64 [1.41-1.91] stage 2; 1.76 [1.10-2.82] stage 3) and QFracture (HR = 1.90 [1.62-2.22] non-CKD; 1.57 [1.35-1.82] stage 2; 1.86 [1.19-2.91] stage 3) discriminated MOF similarly in non-CKD and CKD. In contrast, the discrimination of Garvan for any fracture tended to be lower in CKD stage 3 compared to non-CKD and CKD stage 2 (HR = 1.36 [1.22-1.52] non-CKD; 1.34 [1.20-1.50] stage 2; 1.11 [0.79-1.55] stage 3). Before recalibration, FRAX globally overestimated fracture risk while QFracture and Garvan globally underestimated fracture risk. After recalibration, FRAX and QFracture were adequately calibrated for MOF in all CKD strata whereas Garvan tended to underestimate any fracture risk in CKD stage 3 (SIR = 1.31 [0.95-1.81]). In conclusion, the discrimination and calibration of FRAX and QFracture is similar in non-CKD and CKD. Garvan may have a lower discrimination in CKD stage 3 and underestimate fracture risk in these patients. © 2020 American Society for Bone and Mineral Research.

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.
BACKGROUND: 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) (FRAXBMD, FRAXnoBMD, GarvanBMD, GarvannoBMD). 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 FRAXBMD and fragility fractures by GarvannoBMD, 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 GarvanBMD performed better than GarvannoBMD for prediction of fragility fractures.

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.