S I Cappelle1, I Ramon2, C Dekelver2, S Rozenberg3, F Baleanu2, R Karmali2, M Rubinstein4, M Tondeur5, M Moreau6, M Paesmans6, P Bergmann7, J-J Body8. 1. Department of Internal Medicine, CHU Brugmann, Université Libre de Bruxelles (ULB), Brussels, Belgium. Electronic address: sylvie.cappelle@chu-brugmann.be. 2. Department of Internal Medicine, CHU Brugmann, Université Libre de Bruxelles (ULB), Brussels, Belgium. 3. Department of Gynecology, CHU St Pierre, Université Libre de Bruxelles (ULB), Brussels, Belgium. 4. Department of Nuclear Medicine, Ixelles Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium. 5. Department of Nuclear Medicine, CHU St Pierre, Université Libre de Bruxelles (ULB), Brussels, Belgium. 6. Data Centre, Bordet Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium. 7. Department of Nuclear Medicine, CHU-Brugmann, Université Libre de Bruxelles (ULB), Brussels, Belgium. 8. Department of Internal Medicine, CHU Brugmann, Université Libre de Bruxelles (ULB), Brussels, Belgium. Electronic address: jean-jacques.body@chu-brugmann.be.
Abstract
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.
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.
Authors: A Charles; A Mugisha; L Iconaru; F Baleanu; F Benoit; M Surquin; P Bergmann; J J Body Journal: Calcif Tissue Int Date: 2022-03-22 Impact factor: 4.000
Authors: A Mugisha; P Bergmann; V Kinnard; L Iconaru; F Baleanu; A Charles; M Surquin; S Rozenberg; F Benoit; J J Body Journal: Calcif Tissue Int Date: 2021-06-23 Impact factor: 4.333
Authors: L Iconaru; M Moreau; F Baleanu; V Kinnard; A Charles; A Mugisha; M Surquin; F Benoit; R Karmali; M Paesmans; J J Body; P Bergmann Journal: Osteoporos Int Date: 2021-01-07 Impact factor: 4.507
Authors: L Iconaru; C Smeys; F Baleanu; V Kinnard; M Moreau; S Cappelle; M Surquin; M Rubinstein; S Rozenberg; M Paesmans; R Karmali; P Bergmann; J J Body Journal: Osteoporos Int Date: 2020-03-03 Impact factor: 4.507