본문 바로가기
KERD
논문KCI 등재

Discriminatory Accuracy of Fracture Risk Assessment Tool in Asian Populations: A Systematic Review and Meta-Analysis

Yumie Rhee|Dheeraj Jha|Manju Chandran|Namki Hong|Seungjin Baek|Stephen J. Ferguson|Benedikt Helgason|Anitha D. Praveen
대한골대사학회 (The Korean Society of Bone Metabolism)|2024.11|대한골대사학회지 (Journal of Bone Metabolism)|제31권 제4호|pp. 296-315|DOI 10.11005/jbm.24.781

2

조회

0

다운로드

1

인용

0

참고문헌

키워드

OsteoporosisboneRisk assessmentMeta-analysis as topicFracturesSystematic reviews as topic
주제분류정형외과학

초록

Background: This review explores the discriminative ability of fracture risk assessment tool (FRAX) in major osteoporotic fracture (MOF) and hip fracture (HF) risk prediction and the densitometric diagnosis of osteoporosis in Asian populations. Methods: We systematically searched the EMBASE, Cochrane, and PubMed databases from the earliest indexing date to January 2024. Studies were included if FRAX was used to identify future osteoporotic fractures or a densitometric diagnosis of osteoporosis in an Asian population and reported the area under the curve (AUC) values. Meta-analyses were conducted after quality assessment for AUC with 95% confidence intervals across the following categories: standard FRAX without/with bone mineral density (BMD), adjusted FRAX, and BMD alone for fracture prediction, as well as standard FRAX for densitometric diagnosis of osteoporosis. Results: A total of 42 studies were included. The AUC values for predicting fracture risk using FRAX-MOF with BMD (0.73 [0.70–0.77]) was highest compared to FRAX-MOF without BMD (0.72 [0.66–0.77]), and adjusted FRAX-MOF (0.71 [0.65–0.77]). The AUC values for predicting fracture risk using FRAX-HF with BMD (0.77 [0.71–0.83]) was highest compared to FRAX-HF without BMD (0.72 [0.65–0.80]), and adjusted FRAXHF (0.75 [0.63–0.86]). The AUC values for BMD alone (0.68 [0.62–0.73]) was lowest for fracture prediction. The AUC values for identifying a densitometric diagnosis of osteoporosis was 0.77 [0.70–0.84] and 0.76 [0.67-0.86] using FRAX-MOF and FRAX-HF, respectively. Conclusions: FRAX with BMD tends to perform more reliably in predicting HF compared to MOF in Asia. However, its accuracy in predicting fracture risk in Asian populations can be improved through region-specific, long-term epidemiological data.

Abstract

Background: This review explores the discriminative ability of fracture risk assessment tool (FRAX) in major osteoporotic fracture (MOF) and hip fracture (HF) risk prediction and the densitometric diagnosis of osteoporosis in Asian populations. Methods: We systematically searched the EMBASE, Cochrane, and PubMed databases from the earliest indexing date to January 2024. Studies were included if FRAX was used to identify future osteoporotic fractures or a densitometric diagnosis of osteoporosis in an Asian population and reported the area under the curve (AUC) values. Meta-analyses were conducted after quality assessment for AUC with 95% confidence intervals across the following categories: standard FRAX without/with bone mineral density (BMD), adjusted FRAX, and BMD alone for fracture prediction, as well as standard FRAX for densitometric diagnosis of osteoporosis. Results: A total of 42 studies were included. The AUC values for predicting fracture risk using FRAX-MOF with BMD (0.73 [0.70–0.77]) was highest compared to FRAX-MOF without BMD (0.72 [0.66–0.77]), and adjusted FRAX-MOF (0.71 [0.65–0.77]). The AUC values for predicting fracture risk using FRAX-HF with BMD (0.77 [0.71–0.83]) was highest compared to FRAX-HF without BMD (0.72 [0.65–0.80]), and adjusted FRAXHF (0.75 [0.63–0.86]). The AUC values for BMD alone (0.68 [0.62–0.73]) was lowest for fracture prediction. The AUC values for identifying a densitometric diagnosis of osteoporosis was 0.77 [0.70–0.84] and 0.76 [0.67-0.86] using FRAX-MOF and FRAX-HF, respectively. Conclusions: FRAX with BMD tends to perform more reliably in predicting HF compared to MOF in Asia. However, its accuracy in predicting fracture risk in Asian populations can be improved through region-specific, long-term epidemiological data.

참고문헌 (76)

  1. Ensrud KE, "Epidemiology of fracture risk with advancing age", J Gerontol A Biol Sci Med Sci, 68, pp.1236~1242 (2013)[DOI]
  2. Cheung CL, "An updated hip fracture projection in Asia : The Asian Federation of Osteoporosis Societies study", Osteoporos Sarcopenia, 4, pp.16~21 (2018)[DOI]
  3. Chandran M, "Cost-effectiveness of FRAX®-based intervention thresholds for management of osteoporosis in Singaporean women", Osteoporos Int, 32, pp.133~144 (2021)[DOI]
  4. Haentjens P, "Meta-analysis : Excess mortality after hip fracture among older women and men", Ann Intern Med, 152, pp.380~390 (2010)[DOI]
  5. Kanis JA, "Diagnosis of osteoporosis and assessment of fracture risk", Lancet, 359, pp.1929~1936 (2002)[DOI]
  6. Mithal A, "The Asia-pacific regional audit-epidemiology, costs, and burden of osteoporosis in India 2013 : A report of International osteoporosis foundation", Indian J Endocrinol Metab, 18, pp.449~454 (2014)[DOI]
  7. Cheung EY, "Discriminative value of FRAX for fracture prediction in a cohort of Chinese postmenopausal women", Osteoporos Int, 23, pp.871~878 (2012)[DOI]
  9. Beaudoin C, "Performance of predictive tools to identify individuals at risk of non-traumatic fracture: A systematic review, meta-analysis, and metaregression", Osteoporos Int, 30, pp.721~740 (2019)[DOI]
  10. Marques A, "The accuracy of osteoporotic fracture risk prediction tools : A systematic review and meta-analysis", Ann Rheum Dis, 74, pp.1958~1967 (2015)[DOI]
  11. Adami G, "A systematic review on the performance of fracture risk assessment tools : FRAX, De-FRA, FRA-HS", J Endocrinol Invest, 46, pp.2287~2297 (2023)[DOI]
  12. Fan Z, "Comparison of OSTA, FRAX and BMI for predicting postmenopausal osteoporosis in a Han population in Beijing : A cross sectional study", Clin Interv Aging, 15, pp.1171~1180 (2020)[DOI]
  13. Nagendra L, "FRAX-based osteoporosis treatment guidelines for resource-poor settings in India", Arch Osteoporos, 16, pp.69~ (2021)[DOI]
  14. Lekamwasam S, "Application of FRAX model to Sri Lankan postmenopausal women", J Clin Densitom, 13, pp.51~55 (2010)[DOI]
  15. Cherian KE, "Utility of FRAX(fracture risk assessment tool)in primary care and family practice setting in India", J Family Med Prim Care, 8, pp.1824~1827 (2019)[DOI]
  16. Chin KY, "A review on the performance of osteoporosis selfassessment tool for Asians in determining osteoporosis and fracture risk", Postgrad Med, 129, pp.734~746 (2017)[DOI]
  17. Kanis JA, "FRAX and the assessment of fracture probability in men and women from the UK", Osteoporos Int, 19, pp.385~397 (2008)[DOI]
  18. Page MJ, "The PRISMA 2020statement : An updated guideline for reporting systematic reviews", BMJ, 372, pp.n71~ (2021)[DOI]
  19. Ouzzani M, "Rayyan-a web and mobile app for systematic reviews", Syst Rev, 5, pp.210~ (2016)[DOI]
  20. Hanley JA, "The meaning and use of the area under a receiver operating characteristic(ROC)curve", Radiology, 143, pp.29~36 (1982)[DOI]
  21. Whiting PF, "QUADAS-2 : A revised tool for the quality assessment of diagnostic accuracy studies", Ann Intern Med, 155, pp.529~536 (2011)[DOI]
  22. Bach M, "The study of under-and over-sampling methods’ utility in analysis of highly imbalanced data on osteoporosis", Inf Sci, 384, pp.174~190 (2017)[DOI]
  23. Viechtbauer W, "Outlier and influence diagnostics for meta-analysis", Res Synth Methods, 1, pp.112~125 (2010)[DOI]
  24. Cheung MW, "Modeling dependent effect sizes with threelevel meta-analyses : A structural equation modeling approach", Psychol Methods, 19, pp.211~229 (2014)[DOI]
  25. Pustejovsky JE, "Meta-analysis with robust variance estimation : Expanding the range of working models", Prev Sci, 23, pp.425~438 (2022)[DOI]
  26. An N, "Beijing friendship hospital osteoporosis self-assessment tool for elderly male(BFH-OSTM)vs fracture risk assessment tool(FRAX)for identifying painful new osteoporotic vertebral fractures in older Chinese men : A cross-sectional study", BMC Musculoskelet Disord, 22, pp.596~ (2021)[DOI]
  27. Chang AJ, "Evaluation of three risk assessment tools in discriminating fracture status among Chinese patients undergoing hemodialysis", Osteoporos Int, 27, pp.3599~3606 (2016)[DOI]
  28. Chuan F, "A simple fragility fracture risk score for type 2 diabetes patients : A derivation, validation, comparison, and risk stratification study", Eur J Endocrinol, 189, pp.508~516 (2023)[DOI]
  29. Guo S, "Comparison of four tools to identify painful new osteoporotic vertebral fractures in the postmenopausal population in Beijing", Front Endocrinol(Lausanne), 13, pp.1013755~ (2022)[DOI]
  30. Huang M, "Performance of HR-pQCT, DXA, and FRAX in the discrimination of asymptomatic vertebral fracture in postmenopausal Chinese women", Arch Osteoporos, 16, pp.125~ (2021)[DOI]
  31. Ji L, "Osteoporosis, fracture and survival : application of machine learning in breast cancer prediction models", Front Oncol, 12, pp.973307~ (2022)[DOI]
  32. Kong XK, "Major osteoporosis fracture prediction in type 2 diabetes : A derivation and comparison study", Osteoporos Int, 33, pp.1957~1967 (2022)[DOI]
  33. Lin J, "Validation of three tools for identifying painful new osteoporotic vertebral fractures in older Chinese men: Bone mineral density, Osteoporosis Self-Assessment Tool for Asians, and fracture risk assessment tool", Clin Interv Aging, 11, pp.461~469 (2016)[DOI]
  34. Yu R, "Sarcopenia combined with FRAX probabilities improves fracture risk prediction in older Chinese men", J Am Med Dir Assoc, 15, pp.918~923 (2014)[DOI]
  35. Jia H, "Assessment for bone health in patients with differentiated thyroid carcinoma after postoperative thyroid-stimulating hormone suppression therapy : A new fracture risk assessment algorithm", Front Endocrinol(Lausanne), 14, pp.1286947~ (2023)[DOI]
  36. Liu S, "Setting the new FRAX reference threshold without bone mineral density in Chinese postmenopausal women", J Endocrinol Invest, 44, pp.347~352 (2021)[DOI]
  37. Zhang X, "Comparison of three tools for predicting primary osteoporosis in an elderly male population in Beijing : A cross-sectional study", Clin Interv Aging, 13, pp.201~209 (2018)[DOI]
  38. Chen SJ, "Comparisons of different screening tools for identifying fracture", osteoporosis risk among community-dwelling older people, 2016, pp.95, e3415~
  39. Liu IT, "Validation of the Taiwan FRAX®calculator for the prediction of fracture risk", Arch Osteoporos, 17, pp.27~ (2022)[DOI]
  40. Yang JJ, "Ten existing osteoporosis prediction tools for the successful application of National Health Insurance-reimbursed anti-osteoporosis medications in long-term care residents in Taiwan", J Formos Med Assoc, 122, pp.139~147 (2023)[DOI]
  41. Yu SF, "Establishment of a preliminary FRAX®-based intervention threshold for rheumatoid arthritis-associated fragility fracture : A 3-year longitudinal, observational, cohort study", Ther Adv Chronic Dis, 13, pp.20406223221078089~ (2022)[DOI]
  42. Sheng YH, "Real world fracture prediction of fracture risk assessment tool(FRAX), osteoporosis self-assessment tool for Asians(OSTA)and one-minute osteoporosis risk test : An 11-year longitudinal study", Bone Rep, 20, pp.101742~ (2024)[DOI]
  43. Lam MT, "Development and validation of a risk score to predict the first hip fracture in the oldest old : A retrospective cohort study", J Gerontol A Biol Sci Med Sci, 75, pp.980~986 (2020)[DOI]
  44. Su Y, "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, pp.111~117 (2017)[DOI]
  45. Su Y, "The role of previous falls in major osteoporotic fracture prediction in conjunction with FRAX in older Chinese men and women : The Mr. OS and Ms. OS cohort study in Hong Kong", Osteoporos Int, 29, pp.355~363 (2018)[DOI]
  46. Iki M, "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, pp.1841~1848 (2015)[DOI]
  47. Tamaki J, "Fracture risk prediction using FRAX® : A 10-year follow-up survey of the Japanese Population-Based Osteoporosis(JPOS)cohort study", Osteoporos Int, 22, pp.3037~3045 (2011)[DOI]
  48. Tamaki J, "Does trabecular bone score(TBS)improve the predictive ability of FRAX(®)for major osteoporotic fractures according to the Japanese Population-Based Osteoporosis(JPOS)cohort study?", J Bone Miner Metab, 37, pp.161~170 (2019)[DOI]
  49. Fujimaki H, "Accuracy of the fracture risk assessment tool for judging pharmacotherapy initiation for primary osteoporosis", J Bone Miner Metab, 40, pp.860~868 (2022)[DOI]
  50. Kim D, "Association between trabecular bone score and risk factors for fractures in Korean female patients with rheumatoid arthritis", Mod Rheumatol, 26, pp.540~545 (2016)[DOI]
  51. Kong SH, "A novel fracture prediction model using machine learning in a community-based cohort", JBMR Plus, 4, pp.e10337~ (2020)[DOI]
  52. Lee HS, "Usefulness of fracture risk assessment tool using lumbar bone mineral density in prediction of osteoporotic vertebral fracture", J Korean Neurosurg Soc, 58, pp.346~349 (2015)[DOI]
  53. Lee KA, "Comparison of predictive value of FRAX, trabecular bone score, and bone mineral density for vertebral fractures in systemic sclerosis: A cross-sectional study", Medicine (Baltimore), 102, pp.e32580~ (2023)[DOI]
  54. Bansal B, "Judicious use of DXABMD in assessing fracture risk by using clinical risk factors in the Indian population", Arch Osteoporos, 13, pp.115~ (2018)[DOI]
  55. Rajan R, "FRAX® with or without BMD and TBS predicts fragility fractures in communitydwelling rural southern Indian postmenopausal women", Arch Osteoporos, 15, pp.82~ (2020)[DOI]
  56. Cherian KE, "Evaluation of different screening tools for predicting femoral neck osteoporosis in rural South Indian postmenopausal women", J Clin Densitom, 21, pp.119~124 (2018)[DOI]
  57. Ang SB, "A pilot screening study for low bone mass in Singaporean women using years since menopause and BMI", Climacteric, 25, pp.163~169 (2022)[DOI]
  58. Chandran M, "Comparison of the osteoporosis self-assessment tool for Asians and the fracture risk assessment tool-FRAX to identify densitometric defined osteoporosis : A discriminatory value analysis in a multi-ethnic female population in Southeast Asia", Osteoporos Sarcopenia, 6, pp.53~58 (2020)[DOI]
  59. Logan S, "Chronic joint pain and handgrip strength correlates with osteoporosis in mid-life women : A Singaporean cohort", Osteoporos Int, 28, pp.2633~2643 (2017)[DOI]
  60. Chanplakorn P, "Development of prediction model for osteoporotic vertebral compression fracture screening without using clinical risk factors, compared with FRAX and other previous models", Arch Osteoporos, 16, pp.84~ (2021)[DOI]
  61. Sribenjalak D, "An optimal intervention threshold of FRAX in postmenopausal Thai women", Arch Osteoporos, 17, pp.21~ (2022)[DOI]
  62. Lekamwasam S, "Ability of FRAX Sri Lanka adjusted for trabecular bone score to discriminate between postmenopausal women with a recent fracture and without a fracture", Osteoporos Sarcopenia, 6, pp.106~110 (2020)[DOI]
  63. Amininezhad F, "Evaluation of the validity of the FRAX algorithm for predicting risk of osteoporotic fracture in Iran", Osteologie, 24, pp.183~186 (2015)[DOI]
  64. Kharroubi A, "Evaluation of the validity of osteoporosis and fracture risk assessment tools(IOF One Minute Test, SCORE, and FRAX)in postmenopausal Palestinian women", Arch Osteoporos, 12, pp.6~ (2017)[DOI]
  65. Cauley JA, "Official positions for FRAX® clinical regarding international differences from Joint Official Positions Development Conference of the International Society for Clinical Densitometry and International Osteoporosis Foundation on FRAX®", J Clin Densitom, 14, pp.240~262 (2011)[DOI]
  66. Nielsen SP, "The fallacy of BMD : A critical review of the diagnostic use of dual X-ray absorptiometry", Clin Rheumatol, 19, pp.174~183 (2000)[DOI]
  67. Tosteson AN, "Costeffective osteoporosis treatment thresholds : The United States perspective", Osteoporos Int, 19, pp.437~447 (2008)[DOI]
  68. "Global status report on alcohol and health 2018" (2018)[URL]
  69. Pouresmaeili F, "A comprehensive overview on osteoporosis and its risk factors", Ther Clin Risk Manag, 14, pp.2029~2049 (2018)[DOI]
  70. Troy KL, "Exercise early and often : Effects of physical activity and exercise on women's bone health", Int J Environ Res Public Health, 15, pp.878~ (2018)[DOI]
  71. Chuang TL, "Comparison of trabecular bone score-adjusted fracture risk assessment(TBSFRAX)and FRAX tools for identification of high fracture risk among Taiwanese adults aged 50 to 90 years with or without prediabetes and diabetes", Medicina(Kaunas), 58, pp.1766~ (2022)[DOI]
  72. Martineau P, "The utility and limitations of using trabecular bone score with FRAX", Curr Opin Rheumatol, 30, pp.412~419 (2018)[DOI]
  73. Fleps I, "Finite element derived femoral strength is a better predictor of hip fracture risk than aBMD in the AGES Reykjavik study cohort", Bone, 154, pp.116219~ (2022)[DOI]
  74. Allaire BT, "Prediction of incident vertebral fracture using CT-based finite element analysis", Osteoporos Int, 30, pp.323~331 (2019)[DOI]
  75. Bjornsson PA, "Fast and robust femur segmentation from computed tomography images for patient-specific hip fracture risk screening", Comput Methods Biomech Biomed Eng Imaging Vis, 11, pp.253~265 (2023)[DOI]
  76. Grassi L, "3D finite element models reconstructed from 2D dual-energy X-ray absorptiometry(DXA)images improve hip fracture prediction compared to areal BMD in osteoporotic fractures in men(MrOS)Sweden cohort", J Bone Miner Res, 38, pp.1258~1267 (2023)[DOI]

관련 논문AI추천

A FRAX Experience in Korea: Fracture Risk Probabilities with a Country-specific Versus a Surrogate Model문영완, 민용기, 박윤수, 윤병구, 이동윤, 이영균, 임승재, 최두석대한골대사학회 · 대한골대사학회지2015 · 제22권 제3호Routine Application of the Korean FRAX Model in Women: a Single-center Study김태희, 박준식, 이수진, 이우석, 이해혁, 정수호대한골대사학회 · 대한골대사학회지2012 · 제19권 제1호Osteoporotic Fracture Risk Assessment Using Bone Mineral Density in Korean: A Community-based Cohort Study신찬수, 이영균, 장선미, 장은진, 조남한, 최형진, 하용찬대한골대사학회 · 대한골대사학회지2016 · 제23권 제1호고관절 골절환자에서 한국인 FRAX^® 모델의 효용성박민정, 원예연, 지형민대한골다공증학회 · OSTEOPOROSIS2011 · 제9권 제2호Development and Validation of a New Clinical Diagnostic Screening Model for Osteoporosis in Postmenopausal WomenJirapong Leeyaphan, Karn Rojjananukulpong, Piyapong Intarasompun, Yuthasak Peerakul대한골대사학회 · 대한골대사학회지2023 · 제30권 제2호FRAX를 이용한 한국인 폐경후 여성에서 당뇨병 유무에 따른 골절 위험률 비교: 국민건강 영양조사 2008-2009김대중, 정윤석, 최용준대한골다공증학회 · OSTEOPOROSIS2014 · 제12권 제1호

이 간행물의 다른 논문 (7건)

Incretin-Based Therapies: A Promising Approach for Modulating Oxidative Stress and Insulin Resistance in SarcopeniaRaniah I. Alnaser, Mohammed N. Abed, Fawaz A. Alassafpp. 251-263Clinical Utility of Bone Turnover Markers in Chronic Kidney DiseasePraopilad Srisuwarn, Syazrah Salam, Richard Eastellpp. 264-278Age- and Sex-Related Volumetric Density Differences in Trabecular and Cortical Bone of the Proximal Femur in Healthy PopulationSilvana Di Gregorio, María Lorena Brance, Fernando Daniel Saraví, Muriel M. Henríquez, María Silvia Larroudé, Jorge E. Jacobo, Sebastian Abalo Araujo, Vanesa Longobardi, María Bélen Zanchetta, María Rosa Ulla, Florencia Martos, Helena Salerni, Beatriz Oliveri, Marina Soledad Bonanno, Naraline Luna Meneses, Pilar Diaz Baclini, Luis A. Ramírez Stieben, Lucas R. Brunpp. 279-289Zoledronate Therapy in Osteogenesis Imperfecta: Perspectives in Indonesia Tertiary HospitalRob G. H. H. Nelissen, Dian Nurhayati, Tri Wahyu Martanto, Aliefio Japamadisawpp. 290-295Physical Performance Level and Patient-Reported Outcomes in Female Patients with Distal Radius FractureYoung Ho Shin, Rawdhah Kamil Kalantan, Byung Hun Oh, Jae Kwang Kimpp. 316-325
원문 보기