Evidence map›Paper›PMID 37132542›Full record

ArticleJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research2023

A Fracture Risk Assessment Tool for High Resolution Peripheral Quantitative Computed Tomography.

Danielle E Whittier, Elizabeth J Samelson, Marian T Hannan, Lauren A Burt, David A Hanley, Emmanuel Biver, Pawel Szulc, Elisabeth Sornay-Rendu, Blandine Merle, Roland Chapurlat and 8 more

Abstract read
In one paragraph

Article in Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. External validation of a novel HR-pQCT based fracture risk assessment tool (μFRAC) in a male cohort: the osteoporotic fractures in men (MrOS) study.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026
    Article
  6. Contributions of Bone Microarchitecture to Skeletal Fragility in Adults with Longstanding Type 1 Diabetes.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026
    Article
  7. Longitudinal associations between changes in bone mechanical strength and fracture risk estimated by μFRAC.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026
    Article
  8. Observational
  9. Review
  10. Localized bone deterioration predicts declining failure load and increased fracture risk.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
    Article
  11. Review
  12. Article
  13. Article
  14. Emerging applications of feature selection in osteoporosis research: from biomarker discovery to clinical decision support.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025
    Review
  15. Article
  16. Osteoporosis screening in the mammography setting via digital wrist tomosynthesis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025
    Article
  17. Prediction of hip fracture by high-resolution peripheral quantitative computed tomography in older Swedish women.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Danielle E WhittierMcCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Elizabeth J SamelsonHinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-1086-1560
Marian T HannanHinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-9586-6928
Lauren A BurtMcCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.ORCID 0000-0002-0348-5643
David A HanleyMcCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Emmanuel BiverDivision of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.ORCID 0000-0001-6174-1951
Pawel SzulcINSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France.ORCID 0000-0003-4168-6697
Elisabeth Sornay-RenduINSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France.ORCID 0000-0002-2573-5316
Blandine MerleINSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France.
Roland ChapurlatINSERM UMR1033, Université de Lyon, Hôpital Edouard Herriot, Lyon, France.ORCID 0000-0001-8214-6385
Eric LespessaillesRegional Hospital of Orleans, PRIMMO and EA 4708-I3MTO, University of Orleans, Orleans, France.
Andy Kin On WongJoint Department of Medical Imaging, University Health Network, Dalla Lana School of Public Health, University of Toronto, Toronto, CA, USA.
David GoltzmanDepartment of Medicine, McGill University and McGill University Health Centre, Montreal, QC, Canada.ORCID 0000-0003-3421-7147
Sundeep KhoslaKogod Center on Aging and Division of Endocrinology, Mayo Clinic, Rochester, MN, USA.ORCID 0000-0002-2936-4372
Serge FerrariDivision of Bone Diseases, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.ORCID 0000-0002-1372-4417
Mary L BouxseinCenter for Advanced Orthopedic Studies, BIDMC, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-7027-7414
Douglas P KielHinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-8474-0310
Steven K BoydMcCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.ORCID 0000-0002-2930-5997

Funding

RISK FACTORS FOR VERTEBRAL FRACTURE AND BONE LOSSR01AR041398 · NIAMS · RHODE ISLAND HOSPITAL (PROVIDENCE, RI) · PI KIEL, DOUGLAS P. · 1991 to 2025
$16.1M
EPIDEMIOLOGY OF AGE-RELATED BONE LOSS, &FRACTURESR01AR027065 · NIAMS · MAYO CLINIC ROCHESTER · PI AMIN, SHREYASEE, KHOSLA, SUNDEEP · 1986 to 2020
$14.9M
The Gut Microbiome and Bone MicroarchitectureR01AR061445 · NIAMS · HEBREW REHABILITATION CENTER FOR AGED · PI KIEL, DOUGLAS P. · 2012 to 2021
$8.9M
THE FRAMINGHAM HEART STUDY-N01HC25195-268025195-268025195N01HC025195 · HC · TRUSTEES OF BOSTON UNIVERSITY · PI WOLF, PHILIP A · 2002 to 2006
–
NHLBI NIH HHS HHSN268201500001CNHLBI NIH HHS HHSN268201500001INHLBI NIH HHS N01 HC025195NIAMS NIH HHS R01 AR027065NIAMS NIH HHS R01 AR041398NIAMS NIH HHS R01 AR061445
6 · The paper itself

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).

Indexed as

Osteoporotic FracturesAdultAgedBone DensityHumansProspective StudiesRisk AssessmentTomography, X-Ray ComputedBONE MICROARCHITECTUREFRACTURE PREDICTIONHIGH RESOLUTION PERIPHERAL QUANTITATIVE COMPUTED TOMOGRAPHYMACHINE LEARNINGOSTEOPOROSISRISK ASSESSMENT

Identifiers

PMID37132542
PMCPMC10523935

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.