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Whittier DE, Samelson EJ, Hannan MT, Burt LA, Hanley DA, Biver E, Szulc P, Sornay-Rendu E, Merle B, Chapurlat R, Lespessailles E, Wong AKO, Goltzman D, Khosla S, Ferrari S, Bouxsein ML, Kiel DP, Boyd SK. A Fracture Risk Assessment Tool for High Resolution Peripheral Quantitative Computed Tomography. J Bone Miner Res 2023; 38:1234-1244. [PMID: 37132542 PMCID: PMC10523935 DOI: 10.1002/jbmr.4808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 02/10/2023] [Accepted: 03/21/2023] [Indexed: 05/04/2023]
Abstract
Most fracture risk assessment tools use clinical risk factors combined with bone mineral density (BMD) to improve assessment of osteoporosis; however, stratifying fracture risk remains challenging. This study developed a fracture risk assessment tool that uses information about volumetric bone density and three-dimensional structure, obtained using high-resolution peripheral quantitative compute tomography (HR-pQCT), to provide an alternative approach for patient-specific assessment of fracture risk. Using an international prospective cohort of older adults (n = 6802) we developed a tool to predict osteoporotic fracture risk, called μFRAC. The model was constructed using random survival forests, and input predictors included HR-pQCT parameters summarizing BMD and microarchitecture alongside clinical risk factors (sex, age, height, weight, and prior adulthood fracture) and femoral neck areal BMD (FN aBMD). The performance of μFRAC was compared to the Fracture Risk Assessment Tool (FRAX) and a reference model built using FN aBMD and clinical covariates. μFRAC was predictive of osteoporotic fracture (c-index = 0.673, p < 0.001), modestly outperforming FRAX and FN aBMD models (c-index = 0.617 and 0.636, respectively). Removal of FN aBMD and all clinical risk factors, except age, from μFRAC did not significantly impact its performance when estimating 5-year and 10-year fracture risk. The performance of μFRAC improved when only major osteoporotic fractures were considered (c-index = 0.733, p < 0.001). We developed a personalized fracture risk assessment tool based on HR-pQCT that may provide an alternative approach to current clinical methods by leveraging direct measures of bone density and structure. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Danielle E Whittier
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elizabeth J Samelson
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Marian T Hannan
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Lauren A Burt
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - David A Hanley
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Emmanuel Biver
- Division of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pawel Szulc
- INSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France
| | | | - Blandine Merle
- INSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France
| | - Roland Chapurlat
- INSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France
| | - Eric Lespessailles
- Regional Hospital of Orleans, PRIMMO and EA 4708-I3MTO, University of Orleans, Orleans, France
| | - Andy Kin On Wong
- Joint Department of Medical Imaging, University Health Network, Dalla Lana School of Public Health, University of Toronto, Toronto, CA, USA
- Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, CA, USA
| | - David Goltzman
- Department of Medicine, McGill University and McGill University Health Centre, Montreal, QC, Canada
| | - Sundeep Khosla
- Kogod Center on Aging and Division of Endocrinology, Mayo Clinic, Rochester, MN, USA
| | - Serge Ferrari
- Division of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Mary L Bouxsein
- Center for Advanced Orthopedic Studies, BIDMC, Harvard Medical School, Boston, MA, USA
- Endocrine Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Orthopedic Surgery, Harvard Medical School, Boston, MA, USA
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Steven K Boyd
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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