Skip to main content

Advertisement

SpringerLink
Log in

Prediction of imminent fracture risk in Canadian women and men aged 45 years or older: external validation of the Fracture Risk Evaluation Model (FREM)

  • Original Article
  • Published:
Osteoporosis International Aims and scope Submit manuscript

Abstract

Summary

The Fracture Risk Evaluation Model (FREM) identifies individuals at high imminent risk of major osteoporotic fractures. We validated FREM on 74,828 individuals from Manitoba, Canada, and found significant fracture risk stratification for all FREM scores. FREM performed better than age alone but not as well as FRAX® with BMD.

Introduction

The FREM is a tool developed from Danish public health registers (hospital diagnoses) to identify individuals over age 45 years at high imminent risk of major osteoporotic fractures (MOF) and hip fracture (HF). In this study, our aim was to examine the ability of FREM to identify individuals at high imminent fracture risk in women and men from Manitoba, Canada.

Methods

We used the population-based Manitoba Bone Mineral Density (BMD) Program registry, and identified women and men aged 45 years or older undergoing baseline BMD assessment with 2 years of follow-up data. From linked population-based data sources, we constructed FREM scores using up to 10 years of prior healthcare information.

Results

The study population comprised 74,828 subjects, and during the 2 years of observation, 1612 incident MOF and 299 incident HF occurred. We found significant fracture risk stratification for all FREM scores, with AUC estimates of 0.63–0.66 for MOF for both sexes and 0.84 for women and 0.65–0.67 for men for HF. FREM performed better than age alone but not as well as FRAX® with BMD. The inclusion of physician claims data gave slightly better performance than hospitalization data alone. Overall calibration for 1-year MOF prediction was reasonable, but HF prediction was overestimated.

Conclusion

In conclusion, the FREM algorithm shows significant fracture risk stratification when applied to an independent clinical population from Manitoba, Canada. Overall calibration for MOF prediction was good, but hip fracture risk was systematically overestimated indicating the need for recalibration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Data availability

Data sharing is not permitted under the Researcher Agreement with Manitoba Health, Seniors and Active Living (MHSAL). However, researchers may apply for data access through the Health Research Ethics Board of the University of Manitoba and the Health Information and Privacy Committee of MHSAL.

References

  1. Kanis JA, Cooper C, Rizzoli R et al (2017) Identification and management of patients at increased risk of osteoporotic fracture: outcomes of an ESCEO expert consensus meeting. Osteoporos Int 28:2023–2034

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kanis J, Norton N, Harvey N, Jacobson T, Johansson H, Lorentzon M, McCloskey E, Willers C, Borgström F (2021) SCOPE 2020: a new scorecard for osteoporosis in Europe. Arch Osteoporos

  3. Kanis JA, Norton N, Harvey NC, Jacobson T, Johansson H, Lorentzon M, McCloskey EV, Willers C, Borgström F (2021) SCOPE 2021: a new scorecard for osteoporosis in Europe. Arch Osteoporos 16:82

    Article  PubMed  PubMed Central  Google Scholar 

  4. Johansson H, Siggeirsdóttir K, Harvey NC, Odén A, Gudnason V, McCloskey E, Sigurdsson G, Kanis JA (2017) Imminent risk of fracture after fracture. Osteoporos Int 28:775–780

    Article  CAS  PubMed  Google Scholar 

  5. Roux C, Briot K (2017) Imminent fracture risk. Osteoporos Int 28:1765–1769

    Article  CAS  PubMed  Google Scholar 

  6. Adachi JD, Berger C, Barron R et al (2019) Predictors of imminent non-vertebral fracture in elderly women with osteoporosis, low bone mass, or a history of fracture, based on data from the population-based Canadian Multicentre Osteoporosis Study (CaMos). Arch Osteoporos 14:53

    Article  PubMed  Google Scholar 

  7. Rubin KH, Friis-Holmberg T, Hermann AP, Abrahamsen B, Brixen K (2013) Risk assessment tools to identify women with increased risk of osteoporotic fracture: complexity or simplicity? A systematic review. J Bone Miner Res 28:1701–1717

    Article  PubMed  Google Scholar 

  8. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E (2008) FRAXTM and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19:385–397

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Hippisley-Cox J, Coupland C (2009) Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores. BMJ 339:b4229

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV (2007) Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int 18:1109–1117

    Article  CAS  PubMed  Google Scholar 

  11. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV (2008) Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporos Int 19:1431–1444

    Article  CAS  PubMed  Google Scholar 

  12. Rubin KH, Möller S, Holmberg T, Bliddal M, Søndergaard J, Abrahamsen B (2018) A New Fracture Risk Assessment Tool (FREM) based on public health registries. J Bone Miner Res 33:1967–1979

    Article  PubMed  Google Scholar 

  13. Skjodt MK, Moller S, Hyldig N, Clausen A, Bliddal M, Sondergaard J, Abrahamsen B, Rubin KH (2021) Validation of the Fracture Risk Evaluation Model (FREM) in predicting major osteoporotic fractures and hip fractures using administrative health data. Bone 147:115934

    Article  PubMed  Google Scholar 

  14. Leslie WD, Metge C (2003) Establishing a regional bone density program: lessons from the Manitoba experience. J Clin Densitom 6:275–282

    Article  PubMed  Google Scholar 

  15. Leslie WD, Caetano PA, Macwilliam LR, Finlayson GS (2005) Construction and validation of a population-based bone densitometry database. J Clin Densitom 8:25–30

    Article  PubMed  Google Scholar 

  16. Lix L, Yogendran M, Shaw S, Burchill C, Metge C, Bond R (2008) Population-based data sources for chronic disease surveillance. Chronic Dis Can 29:31–38

    Article  CAS  PubMed  Google Scholar 

  17. Duerksen DR, Lix LM, Leslie WD (2019) Development of an administrative definition for celiac disease. BMC Res Notes 12:661

    Article  PubMed  PubMed Central  Google Scholar 

  18. Blanchard JF, Ludwig S, Wajda A, Dean H, Anderson K, Kendall O, Depew N (1996) Incidence and prevalence of diabetes in Manitoba, 1986–1991. Diabetes Care 19:807–811

    Article  CAS  PubMed  Google Scholar 

  19. Leslie WD, Schousboe JT, Morin SN, Martineau P, Lix LM, Johansson H, McCloskey EV, Harvey NC, Kanis JA (2020) Measured height loss predicts incident clinical fractures independently from FRAX: a registry-based cohort study. Osteoporos Int 31:1079–1087

    Article  CAS  PubMed  Google Scholar 

  20. Leslie WD, Lix LM, Langsetmo L et al (2011) Construction of a FRAX(R) model for the assessment of fracture probability in Canada and implications for treatment. Osteoporos Int 22:817–827

    Article  CAS  PubMed  Google Scholar 

  21. Fraser LA, Langsetmo L, Berger C et al (2011) Fracture prediction and calibration of a Canadian FRAX® tool: a population-based report from CaMos. Osteoporos Int 22:829–837

    Article  PubMed  Google Scholar 

  22. Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, Kanis JA (2010) Independent clinical validation of a Canadian FRAX tool: fracture prediction and model calibration. J Bone Miner Res 25:2350–2358

    Article  PubMed  Google Scholar 

  23. Rubin KH, Abrahamsen B, Friis-Holmberg T, Hjelmborg JV, Bech M, Hermann AP, Barkmann R, Glüer CC, Brixen K (2013) Comparison of different screening tools (FRAX®, OST, ORAI, OSIRIS, SCORE and age alone) to identify women with increased risk of fracture A population-based prospective study. Bone 56:16–22

    Article  PubMed  Google Scholar 

  24. Lix LM, Azimaee M, Osman BA, Caetano P, Morin S, Metge C, Goltzman D, Kreiger N, Prior J, Leslie WD (2012) Osteoporosis-related fracture case definitions for population-based administrative data. BMC Public Health 12:301

    Article  PubMed  PubMed Central  Google Scholar 

  25. Epp R, Alhrbi M, Ward L, Leslie WD (2018) Radiological validation of fracture definitions from administrative data. J Bone Miner Res 33:S275

    Google Scholar 

  26. Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839–843

    Article  CAS  PubMed  Google Scholar 

  27. Svedbom A, Hernlund E, Ivergård M, Compston J, Cooper C, Stenmark J, McCloskey EV, Jönsson B, Kanis JA (2013) Osteoporosis in the European Union: a compendium of country-specific reports. Arch Osteoporos 8:137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Sheer RL, Barron RL, Sudharshan L, Pasquale MK (2020) Validated prediction of imminent risk of fracture for older adults. Am J Manag Care 26:e91–e97

    Article  PubMed  Google Scholar 

  29. Weycker D, Edelsberg J, Barron R, Atwood M, Oster G, Crittenden DB, Grauer A (2017) Predictors of near-term fracture in osteoporotic women aged ≥65 years, based on data from the study of osteoporotic fractures. Osteoporos Int 28:2565–2571

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Reber KC, König HH, Becker C, Rapp K, Büchele G, Mächler S, Lindlbauer I (2018) Development of a risk assessment tool for osteoporotic fracture prevention: a claims data approach. Bone 110:170–176

    Article  PubMed  Google Scholar 

  31. Almog YA, Rai A, Zhang P et al (2020) Deep learning with electronic health records for short-term fracture risk identification: crystal bone algorithm development and validation. J Med Internet Res 22:e22550

    Article  PubMed  PubMed Central  Google Scholar 

  32. Kanis JA, Oden A, Johansson H, McCloskey E (2012) Pitfalls in the external validation of FRAX. Osteoporos Int 23:423–431

    Article  CAS  PubMed  Google Scholar 

  33. Khalid S, Pineda-Moncusi M, El-Hussein L, et al. (2021) Predicting imminent fractures in patients with a recent fracture or starting oral bisphosphonate therapy: development and international validation of prognostic models. J Bone Miner Res

  34. Kanis JA, Johansson H, Harvey NC et al (2021) The use of 2-, 5-, and 10-year probabilities to characterize fracture risk after a recent sentinel fracture. Osteoporos Int 32:47–54

    Article  CAS  PubMed  Google Scholar 

  35. Black DM, Rosen CJ (2016) Clinical practice Postmenopausal osteoporosis. N Engl J Med 374:254–262

    Article  CAS  PubMed  Google Scholar 

  36. The Lancet Diabetes E (2018) Osteoporosis: a roadmap to close the treatment gap. Lancet Diabetes Endocrinol 6:833

    Article  Google Scholar 

  37. Kanis JA, Svedbom A, Harvey N, McCloskey EV (2014) The osteoporosis treatment gap. J Bone Miner Res 29:1926–1928

    Article  PubMed  Google Scholar 

  38. Harvey NC, McCloskey EV, Mitchell PJ, Dawson-Hughes B, Pierroz DD, Reginster JY, Rizzoli R, Cooper C, Kanis JA (2017) Mind the (treatment) gap: a global perspective on current and future strategies for prevention of fragility fractures. Osteoporos Int 28:1507–1529

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Skjødt MK, Khalid S, Ernst M et al (2020) Secular trends in the initiation of therapy in secondary fracture prevention in Europe: a multi-national cohort study including data from Denmark, Catalonia, and the United Kingdom. Osteoporos Int 31:1535–1544

    Article  PubMed  PubMed Central  Google Scholar 

  40. Abrahamsen B, Skjødt MK, Vestergaard P (2019) Hip fracture rates and time trends in use of anti-osteoporosis medications in Denmark for the period 2005 to 2015: missed opportunities in fracture prevention. Bone 120:476–481

    Article  PubMed  Google Scholar 

  41. Leslie WD, Tsang JF, Caetano PA, Lix LM (2007) Effectiveness of bone density measurement for predicting osteoporotic fractures in clinical practice. J Clin Endocrinol Metab 92:77–81

    Article  CAS  PubMed  Google Scholar 

  42. Leslie WD, Berger C, Langsetmo L et al (2011) Construction and validation of a simplified fracture risk assessment tool for Canadian women and men: results from the CaMos and Manitoba cohorts. Osteoporos Int 22:1873–1883

    Article  CAS  PubMed  Google Scholar 

  43. Kanis JA, Johansson H, Harvey NC, Gudnason V, Sigurdsson G, Siggeirsdottir K, Lorentzon M, Liu E, Vandenput L, McCloskey EV (2020) Adjusting conventional FRAX estimates of fracture probability according to the recency of sentinel fractures. Osteoporos Int 31:1817–1828

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the Manitoba Centre for Health Policy for use of data contained in the Population Health Research Data Repository (HIPC 2016/2017–29). The results and conclusions are those of the authors and no official endorsement by the Manitoba Centre for Health Policy, Manitoba Health, Seniors and Active Living, or other data providers is intended or should be inferred. This article has been reviewed and approved by the members of the Manitoba Bone Density Program Committee.

Funding

LML is supported by a Tier I Canada Research Chair.

Author information

Author notes

  1. Katrine Hass Rubin and William D. Leslie share last authorship.

Authors and Affiliations

  1. Open Patient data Explorative Network, Odense University Hospital, Odense, Denmark

    Sören Möller & Katrine Hass Rubin

  2. Research unit OPEN, Department of Clinical Research, University of Southern Denmark, Odense, Denmark

    Sören Möller, Michael K. Skjødt, Bo Abrahamsen & Katrine Hass Rubin

  3. Department of Medicine, Holbæk Hospital, Holbæk, Denmark

    Michael K. Skjødt & Bo Abrahamsen

  4. University of Manitoba, Winnipeg, Canada

    Lin Yan, Lisa M. Lix & William D. Leslie

  5. Centre for Metabolic Bone Diseases, University of Sheffield Medical School, Sheffield, UK

    Eugene V. McCloskey, Helena Johansson & John A. Kanis

  6. Centre for Integrated Research in Musculoskeletal Ageing (CIMA), Mellanby Centre for Bone Research, University of Sheffield, Sheffield, UK

    Eugene V. McCloskey

  7. Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia

    Helena Johansson & John A. Kanis

  8. MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK

    Nicholas C. Harvey

  9. NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, UK

    Nicholas C. Harvey

Authors
  1. Sören Möller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael K. Skjødt
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bo Abrahamsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lisa M. Lix
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Eugene V. McCloskey
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Helena Johansson
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nicholas C. Harvey
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. John A. Kanis
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Katrine Hass Rubin
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. William D. Leslie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sören Möller.

Ethics declarations

Ethics approval

The study was approved by the Health Research Ethics Board of the University of Manitoba.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflicts of interest

Michael K. Skjødt: educational grant, UCB; institutional research grant, UCB/Amgen. Eugene McCloskey: nothing to declare for the context of this paper, but numerous ad hoc consultancies/speaking honoraria and/or research funding from Amgen, Bayer, General Electric, GSK, Fresenius Kabi, Hologic, Lilly, Merck Research Labs, Novartis, Novo Nordisk, Nycomed, Ono, Pfizer, ProStrakan, Roche, Sanofi-Aventis, Servier, Tethys, UCB, and Warner-Chilcott. Nicholas Harvey: nothing to declare for the context of this paper, but has received consultancy/lecture fees/honoraria/grant funding from the Alliance for Better Bone Health, Amgen, MSD, Eli Lilly, Servier, Shire, UCB, Consilient Healthcare, Radius Health, Kyowa Kirin, and Internis Pharma. Bo Abrahamsen: institutional research contracts with Novartis, UCB, Kyowa-Kirin. Fees or honoraria from UCB, Amgen, Pharmacosmos. John Kanis: Professor Kanis led the team that developed FRAX as director of the WHO Collaborating Centre for Metabolic Bone Diseases; he has no financial interest in FRAX. William Leslie, Lisa Lix, Lin Yan, Helena Johansson, Sören Möller, and Katrine Hass Rubin: No conflicts of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Möller, S., Skjødt, M.K., Yan, L. et al. Prediction of imminent fracture risk in Canadian women and men aged 45 years or older: external validation of the Fracture Risk Evaluation Model (FREM). Osteoporos Int 33, 57–66 (2022). https://doi.org/10.1007/s00198-021-06165-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-021-06165-1

Keywords

Search

Navigation