Literature DB >> 18324342

Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks.

N D Nguyen1, S A Frost, J R Center, J A Eisman, T V Nguyen.   

Abstract

UNLABELLED: We have developed clinical nomograms for predicting 5-year and 10-year fracture risks for any elderly man or woman. The nomograms used age and information concerning fracture history, fall history, and BMD T-score or body weight.
INTRODUCTION: Although many fracture risk factors have been identified, the translation of these risk factors into a prognostic model that can be used in primary care setting has not been well realized. The present study sought to develop a nomogram that incorporates non-invasive risk factors to predict 5-year and 10-year absolute fracture risks for an individual man and woman.
METHODS: The Dubbo Osteoporosis Epidemiology Study was designed as a community-based prospective study, with 1358 women and 858 men aged 60+ years as at 1989. Baseline measurements included femoral neck bone mineral density (FNBMD), prior fracture, a history of falls and body weight. Between 1989 and 2004, 426 women and 149 men had sustained a low-trauma fracture (not including morphometric vertebral fractures). Two prognostic models based on the Cox's proportional hazards analysis were considered: model I included age, BMD, prior fracture and falls; and model II included age, weight, prior fracture and fall.
RESULTS: Analysis of the area under the receiver operating characteristic curve (AUC) suggested that model I (AUC = 0.75 for both sexes) performed better than model II (AUC = 0.72 for women and 0.74 for men). Using the models' estimates, we constructed various nomograms for individualizing the risk of fracture for men and women. If the 5-year risk of 10% or greater is considered "high risk", then virtually all 80-year-old men with BMD T-scores < -1.0 or 80-year-old women with T-scores < -2.0 were predicted to be in the high risk group. A 60-year-old woman's risk was considered high risk only if her BMD T-scores < or = -2.5 and with a prior fracture; however, no 60-year-old men would be in the high risk regardless of their BMD and risk profile.
CONCLUSION: These data suggest that the assessment of fracture risk for an individual cannot be based on BMD alone, since there are clearly various combinations of factors that could substantially elevate an individual's risk of fracture. The nomograms presented here can be useful for individualizing the short- and intermediate-term risk of fracture and identifying high-risk individuals for intervention to reduce the burden of fracture in the general population.

Entities:  

Mesh:

Year:  2008        PMID: 18324342     DOI: 10.1007/s00198-008-0588-0

Source DB:  PubMed          Journal:  Osteoporos Int        ISSN: 0937-941X            Impact factor:   4.507


  44 in total

Review 1.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Authors:  F E Harrell; K L Lee; D B Mark
Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

2.  Indices of discrimination or diagnostic accuracy: their ROCs and implied models.

Authors:  J A Swets
Journal:  Psychol Bull       Date:  1986-01       Impact factor: 17.737

Review 3.  Form of empirical ROCs in discrimination and diagnostic tasks: implications for theory and measurement of performance.

Authors:  J A Swets
Journal:  Psychol Bull       Date:  1986-03       Impact factor: 17.737

4.  Risk factors for proximal humerus, forearm, and wrist fractures in elderly men and women: the Dubbo Osteoporosis Epidemiology Study.

Authors:  T V Nguyen; J R Center; P N Sambrook; J A Eisman
Journal:  Am J Epidemiol       Date:  2001-03-15       Impact factor: 4.897

5.  An assessment tool for predicting fracture risk in postmenopausal women.

Authors:  D M Black; M Steinbuch; L Palermo; P Dargent-Molina; R Lindsay; M S Hoseyni; O Johnell
Journal:  Osteoporos Int       Date:  2001       Impact factor: 4.507

Review 6.  Comparisons of nomograms and urologists' predictions in prostate cancer.

Authors:  Phillip L Ross; Claudia Gerigk; Mithat Gonen; Ofer Yossepowitch; Ilias Cagiannos; Pramod C Sogani; Peter T Scardino; Michael W Kattan
Journal:  Semin Urol Oncol       Date:  2002-05

7.  Identification of high-risk individuals for hip fracture: a 14-year prospective study.

Authors:  Nguyen D Nguyen; Chatlert Pongchaiyakul; Jacqueline R Center; John A Eisman; Tuan V Nguyen
Journal:  J Bone Miner Res       Date:  2005-05-31       Impact factor: 6.741

8.  Anti-hip fracture efficacy of biophosphonates: a Bayesian analysis of clinical trials.

Authors:  Nguyen D Nguyen; John A Eisman; Tuan V Nguyen
Journal:  J Bone Miner Res       Date:  2006-02       Impact factor: 6.741

9.  A family history of fracture and fracture risk: a meta-analysis.

Authors:  J A Kanis; H Johansson; A Oden; O Johnell; C De Laet; J A Eisman; E V McCloskey; D Mellstrom; L J Melton; H A P Pols; J Reeve; A J Silman; A Tenenhouse
Journal:  Bone       Date:  2004-11       Impact factor: 4.398

10.  A simple clinical score for estimating the long-term risk of fracture in post-menopausal women.

Authors:  T P van Staa; P Geusens; J A Kanis; H G M Leufkens; S Gehlbach; C Cooper
Journal:  QJM       Date:  2006-09-23
View more
  120 in total

Review 1.  Assessment of fracture risk.

Authors:  Sanford Baim; William D Leslie
Journal:  Curr Osteoporos Rep       Date:  2012-03       Impact factor: 5.096

2.  Competing Risks of Fracture and Death in Older Adults with Chronic Kidney Disease.

Authors:  Rasheeda K Hall; Richard Sloane; Carl Pieper; Courtney Van Houtven; Joanne LaFleur; Robert Adler; Cathleen Colón-Emeric
Journal:  J Am Geriatr Soc       Date:  2018-01-10       Impact factor: 5.562

3.  Differing perceptions of intervention thresholds for fracture risk: a survey of patients and doctors.

Authors:  F Douglas; K J Petrie; T Cundy; A Horne; G Gamble; A Grey
Journal:  Osteoporos Int       Date:  2011-11-08       Impact factor: 4.507

4.  The Ability of a Single BMD and Fracture History Assessment to Predict Fracture Over 25 Years in Postmenopausal Women: The Study of Osteoporotic Fractures.

Authors:  Dennis M Black; Jane A Cauley; Rachel Wagman; Kristine Ensrud; Howard A Fink; Teresa A Hillier; Li-Yung Lui; Steven R Cummings; John T Schousboe; Nicola Napoli
Journal:  J Bone Miner Res       Date:  2017-07-18       Impact factor: 6.741

5.  The Predictive Value of Sarcopenia and Falls for 2-Year Major Osteoporotic Fractures in Community-Dwelling Older Adults.

Authors:  Yi Su; Freddy M H Lam; Jason Leung; Wing-Hoi Cheung; Suzanne C Ho; Timothy Kwok
Journal:  Calcif Tissue Int       Date:  2020-05-30       Impact factor: 4.333

Review 6.  How to predict the risk of fracture in HIV?

Authors:  Michael T Yin; Julian Falutz
Journal:  Curr Opin HIV AIDS       Date:  2016-05       Impact factor: 4.283

7.  A FRAX® model for the assessment of fracture probability in Belgium.

Authors:  H Johansson; J A Kanis; E V McCloskey; A Odén; J-P Devogelaer; J-M Kaufman; A Neuprez; M Hiligsmann; O Bruyere; J-Y Reginster
Journal:  Osteoporos Int       Date:  2010-03-30       Impact factor: 4.507

Review 8.  Genetic profiling and individualized assessment of fracture risk.

Authors:  Tuan V Nguyen; John A Eisman
Journal:  Nat Rev Endocrinol       Date:  2013-02-05       Impact factor: 43.330

9.  Is Partial or Total Thyroidectomy Associated with Risk of Long-Term Osteoporosis: A Nationwide Population-Based Study.

Authors:  Chien-Ling Hung; Chih-Ching Yeh; Pi-Shan Sung; Chung-Jye Hung; Chih-Hsin Muo; Fung-Chang Sung; I-Ming Jou; Kuen-Jer Tsai
Journal:  World J Surg       Date:  2018-09       Impact factor: 3.352

10.  Fracture prediction from self-reported falls in routine clinical practice: a registry-based cohort study.

Authors:  W D Leslie; S N Morin; L M Lix; P Martineau; M Bryanton; E V McCloskey; H Johansson; N C Harvey; J A Kanis
Journal:  Osteoporos Int       Date:  2019-08-02       Impact factor: 4.507

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.