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Osteoporosis International

, Volume 30, Issue 4, pp 721–740 | Cite as

Performance of predictive tools to identify individuals at risk of non-traumatic fracture: a systematic review, meta-analysis, and meta-regression

  • C. BeaudoinEmail author
  • L. Moore
  • M. Gagné
  • L. Bessette
  • L. G. Ste-Marie
  • J. P. Brown
  • S. Jean
Review Article
  • 442 Downloads

Abstract

Summary

There is no consensus on which tool is the most accurate to assess fracture risk. The results of this systematic review suggest that QFracture, Fracture Risk Assessment Tool (FRAX) with BMD, and Garvan with BMD are the tools with the best discriminative ability. More studies assessing the comparative performance of current tools are needed.

Introduction

Many tools exist to assess fracture risk. This review aims to determine which tools have the best predictive accuracy to identify individuals at high risk of non-traumatic fracture.

Methods

Studies assessing the accuracy of tools for prediction of fracture were searched in MEDLINE, EMBASE, Evidence-Based Medicine Reviews, and Global Health. Studies were eligible if discrimination was assessed in a population independent of the derivation cohort. Meta-analyses and meta-regressions were performed on areas under the ROC curve (AUCs). Gender, mean age, age range, and study quality were used as adjustment variables.

Results

We identified 53 validation studies assessing the discriminative ability of 14 tools. Given the small number of studies on some tools, only FRAX, Garvan, and QFracture were compared using meta-regression models. In the unadjusted analyses, QFracture had the best discriminative ability to predict hip fracture (AUC = 0.88). In the adjusted analysis, FRAX with BMD (AUC = 0.81) and Garvan with BMD (AUC = 0.79) had the highest AUCs. For prediction of major osteoporotic fracture, QFracture had the best discriminative ability (AUC = 0.77). For prediction of osteoporotic or any fracture, FRAX with BMD and Garvan with BMD had higher discriminative ability than their versions without BMD (FRAX: AUC = 0.72 vs 0.69, Garvan: AUC = 0.72 vs 0.65). A significant amount of heterogeneity was present in the analyses.

Conclusions

QFracture, FRAX with BMD, and Garvan with BMD have the highest discriminative performance for predicting fracture. Additional studies in which the performance of current tools is assessed in the same individuals may be performed to confirm this conclusion.

Keywords

Discrimination Fracture Osteoporosis Risk assessment Systematic review Validation 

Notes

Acknowledgements

We gratefully aknowledge Vicky Tessier who has reviewed the search strategy.

Funding

C Beaudoin has received a scholarship from the CHU de Québec and the Fonds de recherche du Québec-Santé (FRQS).

Compliance with ethical standards

Competing interests

C Beaudoin, S Jean, L Moore, and M Gagné have no conflict of interest to disclose.

L Bessette has received grant/research support from Amgen Inc., BMS, Janssen, UCB, AbbVie, Pfizer, Sanofi, Eli Lilly, and Novartis; has consulted for Amgen Inc., BMS, Janssen, Roche, UCB, AbbVie, Pfizer, Merck, Celgene, Sanofi, Eli Lilly, and Novartis; and is a member of the Speakers’ Bureau for Amgen Inc., BMS, Janssen, Roche, UCB, AbbVie, Pfizer, Merck, Celgene, Sanofi, Eli Lilly, and Novartis.

LG Ste-Marie has received grant/research support from Amgen Inc., has been a member of the advisory board of Amgen Inc. and Eli Lilly, and received other financial supports from AstraZeneca.

JP Brown has received grant/research support from Amgen Inc. and Eli Lilly; has consulted for Amgen Inc., Eli Lilly, and Merck; and is a member of the Speakers’ Bureau for Amgen Inc. and Eli Lilly.

References

  1. 1.
    NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy (2001) Osteoporosis prevention, diagnosis, and therapy. JAMA. 285(6):785–795Google Scholar
  2. 2.
    Osteoporosis Canada. Men and Osteoporosis [Available from: https://www.osteoporosis.ca/osteoporosis-and-you/men-and-osteoporosis/. Accessed 08 Mar 2019
  3. 3.
    Adachi JD, Loannidis G, Berger C, Joseph L, Papaioannou A, Pickard L, Papadimitropoulos EA, Hopman W, Poliquin S, Prior JC, Hanley DA, Olszynski WP, Anastassiades T, Brown JP, Murray T, Jackson SA, Tenenhouse A, Canadian Multicentre Osteoporosis Study (CaMos) Research Group (2001) The influence of osteoporotic fractures on health-related quality of life in community-dwelling men and women across Canada. Osteoporos Int 12(11):903–908PubMedGoogle Scholar
  4. 4.
    Brenneman SK, Barrett-Connor E, Sajjan S, Markson LE, Siris ES (2006) Impact of recent fracture on health-related quality of life in postmenopausal women. J Bone Miner Res 21(6):809–816PubMedGoogle Scholar
  5. 5.
    Center JR, Nguyen TV, Schneider D, Sambrook PN, Eisman JA (1999) Mortality after all major types of osteoporotic fracture in men and women: an observational study. Lancet. 353(9156):878–882PubMedGoogle Scholar
  6. 6.
    Ioannidis G, Papaioannou A, Hopman WM, Akhtar-Danesh N, Anastassiades T, Pickard L, Kennedy CC, Prior JC, Olszynski WP, Davison KS, Goltzman D, Thabane L, Gafni A, Papadimitropoulos EA, Brown JP, Josse RG, Hanley DA, Adachi JD (2009) Relation between fractures and mortality: results from the Canadian multicentre osteoporosis study. CMAJ. 181(5):265–271PubMedPubMedCentralGoogle Scholar
  7. 7.
    Haentjens P, Magaziner J, Colon-Emeric CS, Vanderschueren D, Milisen K, Velkeniers B et al (2010) Meta-analysis: excess mortality after hip fracture among older women and men. Ann Intern Med 152(6):380–390PubMedPubMedCentralGoogle Scholar
  8. 8.
    Hopkins RB, Burke N, Von Keyserlingk C, Leslie WD, Morin SN, Adachi JD et al (2016) The current economic burden of illness of osteoporosis in Canada. Osteoporos Int 27(10):3023–3032PubMedPubMedCentralGoogle Scholar
  9. 9.
    Cranney A, Jamal SA, Tsang JF, Josse RG, Leslie WD (2007) Low bone mineral density and fracture burden in postmenopausal women. CMAJ. 177(6):575–580PubMedPubMedCentralGoogle Scholar
  10. 10.
    Compston J, Cooper A, Cooper C, Gittoes N, Gregson C, Harvey N et al (2017) UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos 12(1):43PubMedPubMedCentralGoogle Scholar
  11. 11.
    Rabar S, Lau R, O’Flynn N, Li L, Barry P (2012) Risk assessment of fragility fractures: summary of NICE guidance. BMJ. 345:e3698PubMedGoogle Scholar
  12. 12.
    Papaioannou A, Morin S, Cheung AM, Atkinson S, Brown JP, Feldman S, Hanley DA, Hodsman A, Jamal SA, Kaiser SM, Kvern B, Siminoski K, Leslie WD, for the Scientific Advisory Council of Osteoporosis Canada (2010) 2010 clinical practice guidelines for the diagnosis and management of osteoporosis in Canada: summary. CMAJ. 182(17):1864–1873PubMedPubMedCentralGoogle Scholar
  13. 13.
    Marques A, Ferreira RJO, Santos E, Loza E, Carmona L, da Silva JAP (2015) The accuracy of osteoporotic fracture risk prediction tools: a systematic review and meta-analysis. Ann Rheum Dis 74(11):1958–1967PubMedGoogle Scholar
  14. 14.
    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(8):1701–1717PubMedGoogle Scholar
  15. 15.
    Nayak S, Edwards DL, Saleh AA, Greenspan SL (2014) Performance of risk assessment instruments for predicting osteoporotic fracture risk: a systematic review. Osteoporos Int 25(1):23–49PubMedGoogle Scholar
  16. 16.
    Leslie WD, Lix LM (2014) Comparison between various fracture risk assessment tools. Osteoporos Int 25(1):1–21PubMedGoogle Scholar
  17. 17.
    Iki M, Fujita Y, Tamaki J, Kouda K, Yura A, Sato Y, Moon JS, Winzenrieth R, Okamoto N, Kurumatani N (2015) Trabecular bone score may improve FRAX prediction accuracy for major osteoporotic fractures in elderly Japanese men: the Fujiwara-kyo osteoporosis risk in men (FORMEN) cohort study. Osteoporos Int 26(6):1841–1848PubMedGoogle Scholar
  18. 18.
    Lundin H, Torabi F, Saaf M, Strender LE, Nyren S, Johansson SE et al (2015) Laser-supported dual energy X-ray absorptiometry (DXL) compared to conventional absorptiometry (DXA) and to FRAX as tools for fracture risk assessments. PLoS One 10(9):e0137535PubMedPubMedCentralGoogle Scholar
  19. 19.
    Azagra R, Zwart M, Encabo G, Aguye A, Martin-Sanchez JC, Puchol-Ruiz N et al (2016) Rationale of the Spanish FRAX model in decision-making for predicting osteoporotic fractures: an update of FRIDEX cohort of Spanish women. BMC Musculoskelet Disord 17(1):262PubMedPubMedCentralGoogle Scholar
  20. 20.
    Goldshtein I, Gerber Y, Ish-Shalom S, Leshno M (2016) Validation of fracture risk assesment tool using real-world data. Pharmacoepidemiol Drug Saf 25:250–251Google Scholar
  21. 21.
    Holloway KL, Mohebbi M, Hans D, Brennan-Olsen SL, Kotowicz MA, Pasco JA (2016) Prediction of hip fractures in Australian men using FRAX scores adjusted with trabecular bone score. Osteoporos Int 27(1 SUPPL. 1):S351Google Scholar
  22. 22.
    Klop C, De Vries F, Bijlsma JWJ, Leufkens HGM, Welsing PMJ (2016) Predicting the 10-year risk of hip and major osteoporotic fracture in rheumatoid arthritis and in the general population: an independent validation and update of UK FRAX without bone mineral density. Ann Rheum Dis 75(12):2095–2100PubMedPubMedCentralGoogle Scholar
  23. 23.
    Sundh V, Jonasson G (2016) FRAX and mandibular sparse trabeculation as fracture predictors: a longitudinal study 1980–2002. Osteoporos Int 27(1 SUPPL. 1):S257Google Scholar
  24. 24.
    Dagan N, Cohen-Stavi C, Leventer-Roberts M, Balicer RD (2017) External validation and comparison of three prediction tools for risk of osteoporotic fractures using data from population based electronic health records: retrospective cohort study. BMJ (Online) 356:i6755Google Scholar
  25. 25.
    Francesco L, Elisa B, Raffaella M, Alessandro P, Iacopo C, Giampiero M, et al. (2017) Assessing risk of osteoporotic fractures in primary care: development and validation of the FRA-HS algorithm. Calcif Tissue Int 1–13Google Scholar
  26. 26.
    Hoff M, Meyer HE, Skurtveit S, Langhammer A, Sogaard AJ, Syversen U et al (2017) Validation of FRAX and the impact of self-reported falls among elderly in a general population: the HUNT study. Norway Osteoporos Int:1–10Google Scholar
  27. 27.
    Olmos JM, Hernandez JL, Gonzalez JL, Martinez J, Pariente E, Sierra I et al (2017) Predictive and discriminatory capacity of the frax tool in Spanish postmenopathic women: a preliminary study. Calcif Tissue Int 100(1 Supplement 1):S86Google Scholar
  28. 28.
    Orwoll ES, Lapidus J, Wang PY, Vandenput L, Hoffman A, Fink HA, Laughlin GA, Nethander M, Ljunggren Ö, Kindmark A, Lorentzon M, Karlsson MK, Mellström D, Kwok A, Khosla S, Kwok T, Ohlsson C, for the Osteoporotic Fractures in Men (MrOS) Study Research Group (2017) The limited clinical utility of testosterone, estradiol, and sex hormone binding globulin measurements in the prediction of fracture risk and bone loss in older men. J Bone Miner Res 32(3):633–640PubMedGoogle Scholar
  29. 29.
    Reyes Dominguez AI, Sosa Cabrera N, Saavedra Santana P, de Tejada Romero MJG, Jodar Gimeno E, Sosa HM (2017) Assessment of the predictive capacity of the garvan calculator of 10 year risk of fracture in a Spanish population. Revista de Osteoporosis y Metabolismo Mineral 9(2):55–61Google Scholar
  30. 30.
    Su Y, Leung J, Hans D, Lamy O, Kwok T (2017) The added value of trabecular bone score to FRAX to predict major osteoporotic fractures for clinical use in Chinese older people: the Mr. OS and Ms. OS cohort study in Hong Kong. Osteoporos Int 28(1):111–117PubMedGoogle Scholar
  31. 31.
    Deeks JJ, Bossuyt PM, Gatsonis C (editors) (2013). Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy Version 1.0.0 [updated September 2013]: The Cochrane Collaboration. Available from: http://srdta.cochrane.org/. Accessed 17 Mar 2014
  32. 32.
    Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 339:b2535PubMedPubMedCentralGoogle Scholar
  33. 33.
    Toll DB, Janssen KJ, Vergouwe Y, Moons KG (2008) Validation, updating and impact of clinical prediction rules: a review. J Clin Epidemiol 61(11):1085–1094PubMedGoogle Scholar
  34. 34.
    Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 143(1):29–36PubMedGoogle Scholar
  35. 35.
    Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM, QUADAS-2 Group (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536PubMedGoogle Scholar
  36. 36.
    Bennett DA (2001) How can I deal with missing data in my study? Aust N Z J Public Health 25(5):464–469PubMedGoogle Scholar
  37. 37.
    Peduzzi P, Concato J, Feinstein AR, Holford TR (1995) Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates. J Clin Epidemiol 48(12):1503–1510PubMedGoogle Scholar
  38. 38.
    Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996) A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 49(12):1373–1379PubMedGoogle Scholar
  39. 39.
    Viechtbauer W (2010) Conducting meta-analyses in R with the metafor Package. J Stat Softw 36(3):48Google Scholar
  40. 40.
    Hosmer DW, Lemeshow S (1989) Applied logistic regression, vol xiii. Wiley, New York, p 307Google Scholar
  41. 41.
    Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]: The Cochrane Collaboration; 2011. Available from: http://handbook.cochrane.org/. Accessed 02 Mar 2014
  42. 42.
    Pressman AR, Lo JC, Chandra M, Ettinger B (2011) Methods for assessing fracture risk prediction models: experience with FRAX in a large integrated health care delivery system. J Clin Densitom 14(4):407–415PubMedGoogle Scholar
  43. 43.
    Lo JC, Pressman AR, Chandra M, Ettinger B (2011) Fracture risk tool validation in an integrated healthcare delivery system. Am J Manag Care 17(3):188–194PubMedGoogle Scholar
  44. 44.
    Ettinger B, Liu H, Blackwell T, Hoffman AR, Ensrud KE, Orwoll ES, Osteoporotic Fracture in Men (MrOS) Research Group (2012) Validation of FRC, a fracture risk assessment tool, in a cohort of older men: the osteoporotic fractures in men (MrOS) study. J Clin Densitom 15(3):334–342PubMedPubMedCentralGoogle Scholar
  45. 45.
    Ettinger B, Ensrud KE, Blackwell T, Curtis JR, Lapidus JA, Orwoll ES et al (2013) Performance of FRAX in a cohort of community-dwelling, ambulatory older men: the osteoporotic fractures in men (MrOS) study. Osteoporos Int 24(4):1185–1193PubMedGoogle Scholar
  46. 46.
    Yu R, Leung J, Woo J (2014) Sarcopenia combined with FRAX probabilities improves fracture risk prediction in older Chinese men. J Am Med Dir Assoc 15(12):918–923PubMedGoogle Scholar
  47. 47.
    Tebe Cordomi C, Del Rio LM, Di Gregorio S, Casas L, Estrada M-D, Kotzeva A et al (2013) Validation of the FRAX predictive model for major osteoporotic fracture in a historical cohort of Spanish women. J Clin Densitom 16(2):231–237PubMedGoogle Scholar
  48. 48.
    Czerwinski E, Borowy P, Kumorek A, Amarowicz J, Gorkiewicz M, Milert A (2013) Fracture risk prediction in outpatients from Krakow region using FRAX tool versus fracture risk in 11-year follow-up. Ortopedia, traumatologia, rehabilitacja 15(6):617–628PubMedGoogle Scholar
  49. 49.
    Sornay-Rendu E, Munoz F, Delmas PD, Chapurlat RD (2010) The FRAX tool in French women: how well does it describe the real incidence of fracture in the OFELY cohort? J Bone Miner Res 25(10):2101–2107PubMedGoogle Scholar
  50. 50.
    Cheung EYN, Bow CH, Cheung CL, Soong C, Yeung S, Loong C, Kung A (2012) Discriminative value of FRAX for fracture prediction in a cohort of Chinese postmenopausal women. Osteoporos Int 23(3):871–878PubMedGoogle Scholar
  51. 51.
    Tremollieres FA, Pouilles J-M, Drewniak N, Laparra J, Ribot CA, Dargent-Molina P (2010) Fracture risk prediction using BMD and clinical risk factors in early postmenopausal women: sensitivity of the WHO FRAX tool. J Bone Miner Res 25(5):1002–1009PubMedPubMedCentralGoogle Scholar
  52. 52.
    Yun H, Delzell E, Ensrud KE, Kilgore ML, Becker D, Morrisey MA, Curtis JR (2010) Predicting hip and major osteoporotic fractures using administrative data. Arch Intern Med 170(21):1940–1942PubMedPubMedCentralGoogle Scholar
  53. 53.
    Gonzalez-Macias J, Marin F, Vila J, Diez-Perez A (2012) Probability of fractures predicted by FRAX and observed incidence in the Spanish ECOSAP study cohort. Bone. 50(1):373–377PubMedGoogle Scholar
  54. 54.
    Tamaki J, Iki M, Kadowaki E, Sato Y, Kajita E, Kagamimori S, Kagawa Y, Yoneshima H (2011) Fracture risk prediction using FRAX: a 10-year follow-up survey of the Japanese population-based osteoporosis (JPOS) cohort study. Osteoporos Int 22(12):3037–3045PubMedGoogle Scholar
  55. 55.
    Briot K, Paternotte S, Kolta S, Eastell R, Felsenberg D, Reid DM, Glüer CC, Roux C (2013) FRAX: prediction of major osteoporotic fractures in women from the general population: the OPUS study. PLoS One 8(12):e83436PubMedPubMedCentralGoogle Scholar
  56. 56.
    Ensrud KE, Lui L-Y, Taylor BC, Schousboe JT, Donaldson MG, Fink HA, Cauley JA, Hillier TA, Browner WS, Cummings SR, Study of Osteoporotic Fractures Research Group (2009) A comparison of prediction models for fractures in older women: is more better? Arch Intern Med 169(22):2087–2094PubMedPubMedCentralGoogle Scholar
  57. 57.
    Rubin KH, Abrahamsen B, Friis-Holmberg T, Hjelmborg JVB, 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(1):16–22PubMedGoogle Scholar
  58. 58.
    Langsetmo L, Nguyen TV, Nguyen ND, Kovacs CS, Prior JC, Center JR, Morin S, Josse RG, Adachi JD, Hanley DA, Eisman JA, the Canadian Multicentre Osteoporosis Study Research Group (2011) Independent external validation of nomograms for predicting risk of low-trauma fracture and hip fracture. CMAJ. 183(2):E107–E114PubMedPubMedCentralGoogle Scholar
  59. 59.
    Bolland MJ, Siu AT, Mason BH, Horne AM, Ames RW, Grey AB et al (2011) Evaluation of the FRAX and Garvan fracture risk calculators in older women. J Bone Miner Res 26(2):420–427PubMedGoogle Scholar
  60. 60.
    Henry MJ, Pasco JA, Merriman EN, Zhang Y, Sanders KM, Kotowicz MA, Nicholson GC (2011) Fracture risk score and absolute risk of fracture. Radiology. 259(2):495–501PubMedGoogle Scholar
  61. 61.
    Zhang Y, Pasco JA, Kotowicz MA, Sanders KM, Nicholson GC, Henry MJ (2011) How well do the frax (AUS) and garvan calculators predict fractures from the Geelong Osteoporosis Study (GOS). IOF Regionals, 2nd Asia-Pacific Osteoporosis and Bone Meeting, ANZBMS Annual Scientific Meeting, with JSBMR Gold Coast, QLD Australia: Springer London. S548Google Scholar
  62. 62.
    Leslie WD, Lix LM, Johansson H, Oden A, McCloskey E, Kanis JA, Manitoba Bone Density Program (2010) Independent clinical validation of a Canadian FRAX tool: fracture prediction and model calibration. J Bone Miner Res 25(11):2350–2358PubMedGoogle Scholar
  63. 63.
    Morin S, Tsang JF, Leslie WD (2009) Weight and body mass index predict bone mineral density and fractures in women aged 40 to 59 years. Osteoporos Int 20(3):363–370PubMedGoogle Scholar
  64. 64.
    Hundrup YA, Jacobsen RK, Andreasen AH, Davidsen M, Obel EB, Abrahamsen B (2010) Validation of a 5-year risk score of hip fracture in postmenopausal women. The Danish nurse cohort study. Osteoporos Int 21(12):2135–2142PubMedGoogle Scholar
  65. 65.
    Hundrup YA, Jacobsen RK, Andreasen AH, Davidsen M, Obel EB, Abrahamsen B. Performance of BMD-independent risk scores adapted from the WHI and FRAX(R) collaborations in prediction of 5-year risk of hip fracture: The Danish nurses cohort. 36th European Symposium on Calcified Tissues; Vienna Austria: Elsevier Inc.; 2009. p. S397Google Scholar
  66. 66.
    Albaba M, editor (2014) Is HIP fracture risk assessment index (HFRAI), an electronic medical database derived tool, comparable to the world health organization fracture assessment tool (FRAX) in subjects without known femoral neck bone mineral density? 2014 Annual scientific meeting of the American Geriatrics Society. Orlando: Blackwell Publishing Inc.Google Scholar
  67. 67.
    Cauley J, LaCroix AZ, Wu C, Lewis B, Wactawski-Wende J, Masaki K et al (2010) FRAX: does fracture prediction differ by race/ethnicity? 32nd annual meeting of the American Society for Bone and Mineral Research. Wiley-Blackwell, Toronto, ON CanadaGoogle Scholar
  68. 68.
    Robbins J, Aragaki AK, Kooperberg C, Watts N, Wactawski-Wende J, Jackson RD, LeBoff MS, Lewis CE, Chen Z, Stefanick ML, Cauley J (2007) Factors associated with 5-year risk of hip fracture in postmenopausal women. JAMA. 298(20):2389–2398PubMedGoogle Scholar
  69. 69.
    Ahmed LA, Nguyen ND, Bjornerem A, Joakimsen RM, Jorgensen L, Stormer J et al (2014) External validation of the garvan nomograms for predicting absolute fracture risk: the tromso study. PLoS One 9:e107695PubMedPubMedCentralGoogle Scholar
  70. 70.
    Tanaka S, Yoshimura N, Kuroda T, Hosoi T, Saito M, Shiraki M (2010) The fracture and immobilization score (FRISC) for risk assessment of osteoporotic fracture and immobilization in postmenopausal women—a joint analysis of the Nagano, Miyama, and Taiji cohorts. Bone. 47(6):1064–1070PubMedGoogle Scholar
  71. 71.
    Hippisley-Cox J, Coupland C, Brindle P (2014) The performance of seven QPrediction risk scores in an independent external sample of patients from general practice: a validation study. BMJ Open 4(8):e005809PubMedPubMedCentralGoogle Scholar
  72. 72.
    Collins GS, Mallett S, Altman DG (2011) Predicting risk of osteoporotic and hip fracture in the United Kingdom: prospective independent and external validation of QFractureScores. BMJ (Clinical research ed) 342:d3651Google Scholar
  73. 73.
    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 (Clinical research ed) 339:b4229Google Scholar
  74. 74.
    van Geel TACM, Eisman JA, Geusens PP, van den Bergh JPW, Center JR, Dinant G-J (2014) The utility of absolute risk prediction using FRAX and Garvan fracture risk calculator in daily practice. Maturitas. 77(2):174–179PubMedGoogle Scholar
  75. 75.
    Friis-Holmberg T, Rubin KH, Brixen K, Tolstrup JS, Bech M (2014) Fracture risk prediction using phalangeal bone mineral density or FRAX()?-a Danish cohort study on men and women. J Clin Densitom 17(1):7–15PubMedGoogle Scholar
  76. 76.
    Forti P, Rietti E, Pisacane N, Olivelli V, Maltoni B, Ravaglia G (2012) A comparison of frailty indexes for prediction of adverse health outcomes in an elderly cohort. Arch Gerontol Geriatr 54(1):16–20PubMedGoogle Scholar
  77. 77.
    Tanaka S, Kuroda T, Saito M, Shiraki M (2011) Urinary pentosidine improves risk classification using fracture risk assessment tools for postmenopausal women. J Bone Miner Res 26(11):2778–2784PubMedGoogle Scholar
  78. 78.
    Sambrook PN, Flahive J, Hooven FH, Boonen S, Chapurlat R, Lindsay R, Nguyen TV, Díez-Perez A, Pfeilschifter J, Greenspan SL, Hosmer D, Netelenbos JC, Adachi JD, Watts NB, Cooper C, Roux C, Rossini M, Siris ES, Silverman S, Saag KG, Compston JE, LaCroix A, Gehlbach S (2011) Predicting fractures in an international cohort using risk factor algorithms without BMD. J Bone Miner Res 26(11):2770–2777PubMedPubMedCentralGoogle Scholar
  79. 79.
    Sund R, Honkanen R, Johansson H, Oden A, McCloskey E, Kanis J et al (2014) Evaluation of the FRAX model for hip fracture predictions in the population-based Kuopio osteoporosis risk factor and prevention study (OSTPRE). Calcif Tissue Int 95(1):39–45PubMedGoogle Scholar
  80. 80.
    Albertsson DM, Mellstrom D, Petersson C, Eggertsen R (2007) Validation of a 4-item score predicting hip fracture and mortality risk among elderly women. Ann Fam Med 5(1):48–56PubMedPubMedCentralGoogle Scholar
  81. 81.
    Beaudoin C, Jean S, Moore L, Gamache P, Bessette L, Ste-Marie LG et al (2018) Number, location, and time since prior fracture as predictors of future fracture in the elderly from the general population. J Bone Miner Res 33(11):1956–1966PubMedGoogle Scholar
  82. 82.
    Johansson H, Kanis JA, Oden A, McCloskey E, Chapurlat RD, Christiansen C et al (2014) A meta-analysis of the association of fracture risk and body mass index in women. J Bone Miner Res 29(1):223–233PubMedGoogle Scholar
  83. 83.
    Lau B, Cole SR, Gange SJ (2009) Competing risk regression models for epidemiologic data. Am J Epidemiol 170(2):244–256PubMedPubMedCentralGoogle Scholar
  84. 84.
    Leslie WD, Lix LM, Wu X (2013) Competing mortality and fracture risk assessment. Osteoporos Int 24(2):681–688PubMedGoogle Scholar
  85. 85.
    Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 148(3):839–843PubMedGoogle Scholar

Copyright information

© International Osteoporosis Foundation and National Osteoporosis Foundation 2019

Authors and Affiliations

  1. 1.Department of Social and Preventive Medicine, Medicine FacultyLaval UniversityQuebec CityCanada
  2. 2.CHU de Québec-Université Laval Research CenterQuébecCanada
  3. 3.Bureau d’information et d’études en santé des populationsInstitut National de Santé Publique du QuébecQuébecCanada
  4. 4.Department of Medicine, Medicine FacultyLaval UniversityQuebec CityCanada
  5. 5.Department of Medicine, Medicine FacultyUniversity of MontréalMontréalCanada

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