Selection for treatment of patients at high risk of fracture by the short versus long term prediction models - data from the Belgian FRISBEE cohort.

Our imminent model was less sensitive but more selective than FRAX® in the choice of treatment to prevent imminent fractures. This new model decreased NNT by 30%, which could reduce the treatment costs. In the Belgian FRISBEE cohort, the effect of recency further decreased the selectivity of FRAX®. PURPOSE: We analyzed the selection for treatment of patients at high risk of fracture by the Belgian FRISBEE imminent model and the FRAX® tool. METHODS: We identified in the FRISBEE cohort subjects who sustained an incident MOF (mean age 76.5 ± 6.8 years). We calculated their estimated 10-year risk of fracture using FRAX® before and after adjustment for recency and the 2-year probability of fracture using the FRISBEE model. RESULTS: After 6.8 years of follow-up, we validated 480 incident and 54 imminent MOFs. Of the subjects who had an imminent fracture, 94.0% had a fracture risk estimated above 20% by the FRAX® before correction for recency and 98.1% after adjustment, with a specificity of 20.2% and 5.9%, respectively. The sensitivity and specificity of the FRISBEE model at 2 years were 72.2% and 55.4%, respectively, for a threshold of 10%. For these thresholds, 47.3% of the patients were identified at high risk in both models before the correction, and 17.2% of them had an imminent MOF. The adjustment for recency did not change this selection. Before the correction, 34.2% of patients were selected for treatment by FRAX® only, and 18.8% would have had an imminent MOF. This percentage increased to 47% after the adjustment for recency, but only 6% of those would suffer a MOF within 2 years. CONCLUSION: In our Belgian FRISBEE cohort, the imminent model was less sensitive but more selective in the selection of subjects in whom an imminent fracture should be prevented, resulting in a lower NNT. The correction for recency in this elderly population further decreased the selectivity of FRAX®. These data should be validated in additional cohorts before using them in everyday practice.

The effect of fracture recency on observed 5-year fracture probability: A study based on the FRISBEE cohort.

Introduction: Prediction models, especially the FRAX®, are largely used to estimate the fracture risk at ten years, but the current algorithm does not take into account the time elapsed after a fracture. Kanis et al. recently proposed correction factors allowing to adjust the FRAX® score for fracture recency. The objective of this work was to analyze the effect of fracture recency in the FRISBEE cohort. Methods: We identified in the FRISBEE cohort subjects who sustained a validated fracture during the first 5 years following an incident MOF. We calculated their estimated 5-year risk of fracture using FRAX® uncorrected, adjusted for recency and further adjusted for the MOF/hip ratios calibration factors previously derived for the Belgian FRAX®. We compared the fracture risk estimated by FRAX® before and after these corrections to the observed incidence of validated fractures in our cohort. Results: In our ongoing cohort, 376 subjects had a first non-traumatic incident validated MOF after inclusion; 81 had a secondary fracture during the 5 years follow-up period after this index fracture. The FRAX® score significantly under-evaluated the observed incidence of fractures in our cohort by 54.7 % (fracture rate of 9.7 %; 95 % CI, 6.8-12.9 %) if uncorrected (p < 0.001) and by 32.6 % after correction for recency (14.5 %; 95 % CI, 11.1-18.2 %) (p = 0.01). The calibration for MOF/hip ratios improved the prediction (17.5 %; 95 % CI: 13.7-21.4 %) (p = 0.2). After correcting for recency and for calibration, the predicted value was over-evaluated by 22 % (fracture rate of 26.1 %; 95 % CI, 21.6-30.5 %) but this over-evaluation was not significant (p = 0.1). Conclusion: Our data indicate that the correction of the FRAX® score for fracture recency improves fracture prediction. However, correction for calibration and recency tends to overestimate fracture risk in this population of elderly women.

Are there specific clinical risk factors for the occurrence of multiple fractures? The FRISBEE study.

This study showed additional clinical risk factors for the occurrence of multiple fractures with regards to a single fracture, with often higher hazard ratios. It would be important to include the risk of the occurrence of multiple fractures in future prediction models. PURPOSE: To identify clinical risk factors (CRFs) which would specifically increase the risk of multiple fractures. METHODS: Data of the 3560 postmenopausal women of the FRISBEE study were analysed. The CRFs and the fractures are collected annually. The cohort was divided into three groups: those who had no incident fracture, those who had a single incident fracture and those who had 2 two or more incident fractures (i.e. multiple fractures). Statistical analyses were performed using Cox proportional hazards models. RESULTS: Among the 3560 subjects (followed for 9.1 (7.2-10.6) years), 261 subjects had two or more validated fractures during follow-up (146 were major osteoporotic fractures (MOFs)), 628 had one fracture (435 MOFs), 2671 had no fracture (2979 had no MOF); 157 subjects had two or more central fractures, 389 had only one and 3014 had none. The risk factors for those with multiple fractures at any site were age, history of fracture, history of fall, total hip bone mineral density (BMD), spine BMD and rheumatoid arthritis. For those with multiple MOFs, significant CRFs were age, history of fracture, parental hip fracture, total hip BMD and rheumatoid arthritis. CONCLUSION: We found in a prospective cohort study that there were more CRFs and higher hazard ratios for the occurrence of multiple fractures than for a single fracture.

Distribution of Fracture Sites in Postmenopausal Overweight and Obese Women: The FRISBEE Study.

The association between obesity and fracture sites in postmenopausal women has been little studied. We examined the most common types of fractures in obese and overweight postmenopausal women compared to subjects with a normal BMI in the FRISBEE study, a cohort of postmenopausal women followed since 9.1 (7.2-10.6) years. Chi-squared tests and logistic regressions were used to compare the percentages of fracture sites in overweight/obese subjects to subjects with a normal BMI. Their mean (± SD) age was 76.7 ± 6.9 years and their mean BMI was 26.4 ± 4.4. Seven hundred seventy-seven subjects suffered at least one validated fragility fracture with a total of 964 fractures in the whole cohort. Subjects with a BMI higher than 25 had significantly more ankle fractures and less pelvic fractures than subjects with a normal BMI (OR 1.63, 95% CI 1.02-2.56, P = 0.04 and OR 0.55, 95% CI 0.34-0.89, P = 0.01, respectively). There were no significant differences between overweight and obese subjects. Among those older than 75, there were significantly fewer pelvic fractures in overweight/obese subjects (OR 0.49, 95% CI 0.27-0.87, P = 0.01), but before 75, ankle fractures were significantly more frequent in overweight/obese subjects than in subjects with a normal BMI (OR 1.89, 95% CI 1.01-3.57, P = 0.04). In conclusion, the proportion of ankle and pelvic fractures in obese and overweight subjects differs from that in subjects with a normal BMI, but these differences are age dependent. Fracture prevention strategies should take into account the differential effects of excess weight according to age and the site of fracture.

Impact of non-hip fractures in elderly women: a narrative review.

The association of hip fractures with adverse outcomes is well established, but for non-hip fractures this association still needs to be further investigated. The objective of this narrative review is to describe the state of the art with regards to the health impact of clinically relevant non-hip fracture locations in postmenopausal women. PubMed and Scopus databases were searched from January 2010 until December 2020. Studies were included when the crude rates and/or relative risk of 1-year subsequent fractures and/or mortality were reported as well as the precise fracture site. Twenty-three studies met the inclusion criteria. Regarding mortality rates, there was a high variability between studies, with higher rates for vertebral, proximal humerus and pelvic fractures. There was a small or no impact of wrist, ankle or tibia fractures. The mortality rate increased with age after vertebral, proximal humerus and wrist fractures. Moreover, proximal humerus and vertebral fractures were associated with a higher mortality risk. This narrative review indicates that, besides fractures of the hip, fractures of the vertebrae, proximal humerus or pelvis deserve more attention when trying to prevent adverse outcomes of osteoporosis. More studies on the topic of non-hip fractures are urgently needed.

MOF/Hip Fracture Ratio in a Belgian Cohort of Post-menopausal Women (FRISBEE): Potential Impact on the FRAX® Score.

The ratio between major osteoporotic fractures (MOFs) and hip fractures in the Belgian FRAX® tool to predict fractures is currently based on Swedish data. We determined these ratios in a prospective cohort of Belgian postmenopausal women. 3560 women, aged 60-85 years (70.1 ± 6.4 years), were included in a prospective study from 2007 to 2013 and surveyed yearly (FRISBEE). We analyzed the number of validated incident fractures until October 2020 by age and sites and compared the MOFs/hip ratios in this cohort with those from the Swedish databases. We registered 1336 fractures (mean follow-up of 9.1 years). The MOFs/hip ratios extracted from the FRISBEE cohort were 10.7 [95% CI: (5.6-20.5)], 6.4 [4.7-8.7], and 5.0 [3.9-6.5] for women of 60-69, 70-79, and 80-89 years old, respectively. These ratios were 1.7-1.8 times higher for all age groups than those from the Swedish data, which decreased from 6.5 (60-64 years group) down to 1.8 (85-89 age group). The overall MOFs/hip ratio in Frisbee was 6.0 [5.9-6.1], which was higher than any Swedish ratio between 65 and 85 years. Nevertheless, the decrease of the ratios with age paralleled that observed in Sweden. In this Brussels prospective cohort, MOFs/hip ratios were 1.7-1.8 times those observed in Sweden currently used for MOFs prediction in the Belgian FRAX® version. This discrepancy can greatly modify the estimation of the risk of MOFs, which is among the main criteria used to recommend a pharmacological treatment for osteoporosis in several countries.

Risk factors for imminent fractures: a substudy of the FRISBEE cohort.

Multiple factors increase the risk of an imminent fracture, including a recent fracture, older age, osteoporosis, comorbidities, and the fracture site. These findings could be a first step in the development of a model to predict an imminent fracture and select patients most at need of immediate treatment. INTRODUCTION: The risk of a recurrent fragility fracture is maximal during the first 2 years following an incident fracture. In this prospective cohort study, we looked at the incidence of recurrent fractures within 2 years after a first incident fracture and we assessed independent clinical risk factors (CRFs) increasing this imminent fracture risk. METHODS: A total of 3560 postmenopausal women recruited from 2007 to 2013 were surveyed yearly for the occurrence of fragility fractures. We identified patients who sustained a fracture during the first 2 years following a first incident fragility fracture. We quantified the risk of a new fracture and assessed independent CRFs, associated with an imminent fracture at various sites. RESULTS: A recent fracture was a significant CRF for an imminent fracture (OR (95% CI): 3.7 (2.4-5.7) [p < 0.0001]). The incidence of an imminent fracture was higher in subjects above 80 years (p < 0.001). Other CRFs highly predictive in a multivariate analysis were osteoporosis diagnosis (p < 0.01), a central fracture as the index fracture (p < 0.01), and the presence of comorbidities (p < 0.05), with likelihood ratios of 1.9, 1.9, and 2.2, respectively. An imminent fracture was better predicted by a central fracture (p < 0.01) than by a major osteoporotic fracture. The hazard ratio was the highest for a central fracture. CONCLUSION: In patients with a recent fracture, older age, osteoporosis, comorbidities, and fracture site were associated with an imminent fracture risk. These findings could be a first step in the development of a model to predict an imminent fracture and select patients most at need of immediate and most appropriate treatment.

Discriminating value of HR-pQCT for fractures in women with similar FRAX scores: A substudy of the FRISBEE cohort.

Areal bone mineral density (aBMD) has a low sensitivity to identify women at high fracture risk. The FRAX algorithm, by combining several clinical risk factors, might improve fracture prediction compared to aBMD alone. Several micro-architectural and biomechanical parameters which can be measured by high-resolution peripheral quantitative computed tomography (HR-pQCT) are associated with fracture risk. HR-pQCT in combination or not with finite element analysis (FEA) may be used to improve bone strength prediction. Our aim was to assess whether HR-pQCT measurements (densities, cortical and trabecular microarchitecture, biomechanical proprieties assessed by FEA) had an added value in predicting fractures in a subgroup of women belonging to the Belgian FRISBEE cohort. One hundred nineteen women who sustained a fracture (aged 60 to 85 years) during the initial follow-up of our cohort had a radius and tibia examination by HR-pQCT and were compared with controls matched for their FRAX score at baseline. We found that low distal radius total (OR = 1.41 [1.07-1.86] per SD, p < 0.05) and trabecular densities (OR = 1.45 [1.10-1.90], p < 0.01), trabecular number (OR = 1.32 [1.01-1.72], p < 0.05), intra individual distribution of separation (OR = 0.73 [0.54-0.99], p < 0.05) as several FEA parameters were significantly associated with fractures. At the distal tibia, impaired cortical density (OR = 1.32 [1.03-1.70] per SD, p < 0.05) and thickness (OR = 1.29 [1.01-1.63], p < 0.05) and apparent modulus (OR = 1.30 [1.01-1.66], p < 0.05) were significantly correlated with fractures. A low ultra distal radial aBMD (UDR) measured at the time of HR-pQCT was significantly associated with fractures (OR = 1.67 [1.22-2.28], p < 0.01). Women from both groups were followed further after the realization of the HR-pQCT and 46 new fractures were registered. In this second part of the study, low UDR aBMD (OR = 1.66 [1.18-2.35], p < 0.01), total (OR = 1.48 [1.08-2.03], p < 0.05), cortical (OR = 1.40 [1.04-1.87], p < 0.05) and trabecular (OR = 1.37 [1.01-1.85], p < 0.05) densities or apparent modulus (OR = 1.49 [1.07-2.05], p < 0.05) at the radius were associated with a significant increase of fracture risk. At the tibia, only the cortical density was significantly associated with the fracture risk (OR = 1.34 [1.02-2.76], p < 0.05). These results confirm the interest of HR-pQCT measurements for the evaluation of fracture risk, also in women matched for their baseline FRAX score. They also highlight that UDR aBMD contains pertinent information.

What is the validity of self-reported fractures?

We assessed the validity of self-reported fractures, over a median follow-up period of 6.2 years, in a well characterized population-based cohort of 3560 postmenopausal women, aged 60-85 years, from the Fracture Risk Brussels Epidemiological Enquiry (FRISBEE) study. Incident low-traumatic (falls from a standing height or less) or non-traumatic fractures, including peripheral fractures, were registered during each annual follow-up telephone interview. A self-reported fracture was considered as a true positive if it was validated by written reliable medical reports (radiographs, CT scans or surgical report). False positives fractures were considered to be those for which the radiology report indicated that there was no fracture at the reported site. Among self-reported fractures, false positive rates were 14.4% for all fractures. The rate of false positives of 11.2% (n = 48/429) was not negligible for the four classical major osteoporotic fractures (MOFs: hip, clinical spine, forearm or shoulder fractures). In terms of fracture site, we found the lowest false positive rate (4.4%) at the hip, and the highest (16.8%) at the spine, with the proximal humerus and the wrist in between, at about 10% each. The global rates of false positives were 12.5% (n = 22/176) for other major fractures and 22.3% (n = 49/220) for minor fractures. Younger subjects, individuals with fractures at sites other than the hip, with a lower education level, or with a higher BMI were more likely to report false positive fractures. Our data indicate that the inaccuracy of self-reported fractures is clinically relevant for several major fractures, which could influence any fracture risk prediction model.

Osteoporosis treatment gap in a prospective cohort of volunteer women.

Despite the availability of efficient drugs to prevent osteoporotic fractures, only a minority of women receives osteoporosis therapy after a fracture. The high treatment gap in our cohort consisted of unselected volunteer patients highlights the urgent need of additional education, especially for the medical profession, regarding the risk-benefit balance of treatment. INTRODUCTION: Despite the availability of efficient drugs to prevent osteoporotic fractures, only a minority of women receives osteoporosis therapy after a fracture, with a treatment gap around 80%. This can have dramatic consequences for patients and the healthcare systems. METHODS: In this study based on longitudinal data from the FRISBEE (Fracture RIsk Brussels Epidemiological Enquiry) cohort of 3560 volunteer women aged 60 to 85 years, we evaluated the 1-year treatment gap after a first major incident fragility fracture. RESULTS: There were 386 first validated fragility fractures, 285 major osteoporotic fractures (MOF) and 101 "other major" fractures. The rate of untreated patients was 85.0% (82.8% for MOF versus 91.0 % for "other major" fracture sites) (p = 0.04), with a lower rate for spine (70.5%) and hip (72.5%) versus shoulder (91.6%) and wrist (94.1%) (p < 0.0001). More specifically, the treatment gap for patients with osteoporosis, defined by a T-score < - 2.5 SD was 74.6% versus 76.5% for patients with osteoporosis defined by the presence of hip, shoulder, or spine fractures, independently of DXA results. When considering age groups, the rate of untreated women was 87.9% for women 60-70 years old, 88.2% between 70 and 80 years and 77.8% above 80 years (p = 0.03), with a greater difference between women who were younger or older than 80 years at inclusion: 88.1% versus 77.8% (p = 0.009). A diagnosis of osteoporosis (p = 0.01) and age (p = 0.03) were the only clinical risk factors (CRFs) significantly associated with treatment initiation. CONCLUSIONS: This study highlights the urgent need of additional education, especially for the medical profession, regarding the risk-benefit balance of treatment.

Does the Prediction Accuracy of Osteoporotic Fractures by BMD and Clinical Risk Factors Vary With Fracture Site?

Several clinical risk factors (CRFs) have been shown to predict the risk of fragility fractures independently of BMD, but their accuracy in the prediction of a particular fracture site has not been extensively studied. In this study based on longitudinal data from the FRISBEE cohort (Fracture Risk Brussels Epidemiological Enquiry), we evaluated if CRFs are specific for sites of incident osteoporotic fractures during follow-up. We recruited 3560 postmenopausal women, aged 60 to 85 years, from 2007 to 2013, and surveyed yearly for the occurrence of fragility fractures during 6.2 years (median). We analyzed the association between CRFs included in the FRAX (fracture risk assessment tool) model or additional CRFs (falls, sedentary lifestyle, early untreated menopause, diabetes, use of selective serotonin reuptake inhibitors or proton pump inhibitors) and the first incident validated major osteoporotic fracture (MOF; n = 362; vertebra, hip, shoulder, and wrist) or other major fractures (n = 74; ankle, pelvis/sacrum, elbow, knee, long bones). Uni- and multivariate analyses using the Cox proportional hazards model were used. For MOFs considered together, the risk of fracture was highly associated in uni- and multivariate analyses (p<0.01) with osteoporosis (T-score < -2.5), prior fracture, age, BMD (assessed by DXA), and fall history (HR 2.34, 1.82,1.71, 1.38, and 1.32, respectively). For each site analyzed separately, prior OF, age, smoking, and total hip BMD remained independent predictors for hip fractures (HR 5.72, 3.98, 3.10, 2.32, and 1.92, respectively); osteoporosis, age, prior OF, glucocorticoids, and spine BMD for vertebral fracture (HR 2.08, 1.87, 1.78, 1.76, and 1.45, respectively); osteoporosis, prior OF, and femoral neck BMD (HR 1.83, 1.60, and 1.56, respectively) for wrist fracture; osteoporosis, prior OF, and spine BMD (HR 2.48, 1.78, and 1.31, respectively) for shoulder fracture; prior OF and diabetes (HR 2.62 and 2.03) for other major fractures. Thus, a prior fracture and BMD were the best predictors of fracture risk at any site. Other CRFs have a weaker predictive value, which is a function of the site of a future fracture. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

Distribution of clinical risk factors for fracture in a Brussels cohort of postmenopausal women: The FRISBEE study and comparison with other major cohort studies.

OBJECTIVES: The estimation of fracture risk using clinical risk factors (CRFs) is of primary concern in osteoporosis management, but only some risk factors have been thoroughly evaluated and incorporated in predictive models. We have launched a large prospective study, the 'Fracture Risk Brussels Epidemiological Enquiry' (FRISBEE), to develop a new predictive model for osteoporotic fractures. The aims of this report are to describe the methodology of the FRISBEE study and to compare the distribution of CRFs in our cohort with those reported in other large studies. STUDY DESIGN: FRISBEE is a new study that prospectively evaluates a cohort of 3560 post-menopausal women (aged 60-85 years) followed yearly for the occurrence of fragility fractures. Multiple validated CRFs, densitometry (DXA) values and intake of medication were systematically registered at baseline. The distribution of the FRISBEE CRFs has been compared with the distributions of CRFs in the cohorts used to develop the FRAX® model as well as in more recent cohorts. For these recent cohorts, we focused on CRFs not included in FRAX®. RESULTS: The most frequently encountered CRFs used in FRAX® were a prior fragility fracture (27.1%) and a parental history of hip fracture (13.4%). The prevalence of some CRFs not integrated in FRAX® was relatively high, such as the use of proton pump inhibitors (20.8%) and a history of fall(s) (19.7%). The prevalence of many CRFs was quite variable between cohorts; for example, the prevalence of 'personal prior fragility fracture' ranged from 9% to 51%. CONCLUSION: We found considerable heterogeneity in the prevalence of CRFs between cohort studies. The impact of these differences on the predictive value of a particular CRF is unknown. We will construct a predictive model calibrated to the Belgian population. More importantly, the FRISBEE study should allow us to determine the predictive value of newly recognized CRFs in addition to the FRAX® algorithm to reliably estimate fracture risk.