Literature DB >> 15190881

Optimization of BMD measurements to identify high risk groups for treatment--a test analysis.

Helena Johansson1, Anders Oden, Olof Johnell, Bengt Jonsson, Chris de Laet, Alan Oglesby, Eugene V McCloskey, Karthik Kayan, Tarja Jalava, John A Kanis.   

Abstract

INTRODUCTION: The aim of this study was to develop a methodology to optimize the role of BMD measurements in a case finding strategy. We studied 2113 women > or = 75 years of age randomly selected from Sheffield, UK, and adjacent regions. Baseline assessment included hip BMD and clinical risk factors. Outcomes included death and fracture in women followed for 6723 person-years.
MATERIALS AND METHODS: Poisson models were used to identify significant risk factors for all fractures and for death with and without BMD and the hazard functions were used to compute fracture probabilities. Women were categorized by fracture probability with and without a BMD assessment. A 10-year fracture probability threshold of 35% was taken as an intervention threshold. Discordance in categorization of risk (i.e., above or below the threshold probability) between assessment with and without BMD was examined by logistic regression as probabilities of re-classification. Age, prior fracture, use of corticosteroids, and low body mass index were identified as significant clinical risk factors.
RESULTS: A total of 16.8% of women were classified as high risk based on these clinical risk factors. The average BMD in these patients was approximately 1 SD lower than in low-risk women; 21.5% of women were designated to be at high risk with the addition of BMD. Fifteen percent of all women were reclassified after adding BMD to clinical risk factors, most of whom lay near the intervention threshold. When a high probability of reclassification was accepted (without a BMD test) for high risk to low risk (p1< or = 0.8) and a low probability accepted for low to high risk (P2 < or = 0.2), BMD tests would be required in only 21% of the population.
CONCLUSION: We conclude that the use of clinical risk factors can identify elderly women at high fracture risk and that such patients have a low average BMD. BMD testing is required, however, in a minority of women--a fraction that depends on the probabilities accepted for classification and the thresholds of risk chosen. These findings need to be validated in other cohorts at different ages and from different regions of the world. Copyright 2004 American Society for Bone and Mineral Research

Entities:  

Mesh:

Year:  2004        PMID: 15190881     DOI: 10.1359/jbmr.2004.19.6.906

Source DB:  PubMed          Journal:  J Bone Miner Res        ISSN: 0884-0431            Impact factor:   6.741


  39 in total

Review 1.  The role of DXA bone density scans in the diagnosis and treatment of osteoporosis.

Authors:  Glen M Blake; Ignac Fogelman
Journal:  Postgrad Med J       Date:  2007-08       Impact factor: 2.401

2.  Requirements for DXA for the management of osteoporosis in Europe.

Authors:  J A Kanis; O Johnell
Journal:  Osteoporos Int       Date:  2004-12-24       Impact factor: 4.507

Review 3.  Assessment of fracture risk.

Authors:  John A Kanis; Frederik Borgstrom; Chris De Laet; Helena Johansson; Olof Johnell; Bengt Jonsson; Anders Oden; Niklas Zethraeus; Bruce Pfleger; Nikolai Khaltaev
Journal:  Osteoporos Int       Date:  2004-12-23       Impact factor: 4.507

4.  Amount of smoking, pulmonary function, and bone mineral density in middle-aged Korean men: KNHANES 2008-2011.

Authors:  Ji Hyun Lee; A Ram Hong; Jung Hee Kim; Kyoung Min Kim; Bo Kyung Koo; Chan Soo Shin; Sang Wan Kim
Journal:  J Bone Miner Metab       Date:  2017-01-31       Impact factor: 2.626

5.  Assessment of the 10-year risk of fracture in Italian postmenopausal women using FRAX®: a north Italian multicenter study.

Authors:  M Pedrazzoni; G Girasole; A Giusti; A Barone; G Pioli; G Passeri; E Palummeri; G Bianchi
Journal:  J Endocrinol Invest       Date:  2011-07-12       Impact factor: 4.256

6.  BMD, clinical risk factors and their combination for hip fracture prevention.

Authors:  H Johansson; J A Kanis; A Oden; O Johnell; E McCloskey
Journal:  Osteoporos Int       Date:  2009-03-17       Impact factor: 4.507

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

8.  FRAX-based intervention and assessment thresholds in seven Latin American countries.

Authors:  P Clark; E Denova-Gutiérrez; C Zerbini; A Sanchez; O Messina; J J Jaller; C Campusano; C H Orces; G Riera; H Johansson; J A Kanis
Journal:  Osteoporos Int       Date:  2017-12-23       Impact factor: 4.507

9.  FRAX-based intervention and assessment thresholds for osteoporosis in Iran.

Authors:  P Khashayar; A Keshtkar; A Ostovar; B Larijani; H Johansson; N C Harvey; M Lorentzon; E McCloskey; J A Kanis
Journal:  Osteoporos Int       Date:  2019-08-01       Impact factor: 4.507

Review 10.  A systematic review of intervention thresholds based on FRAX : A report prepared for the National Osteoporosis Guideline Group and the International Osteoporosis Foundation.

Authors:  John A Kanis; Nicholas C Harvey; Cyrus Cooper; Helena Johansson; Anders Odén; Eugene V McCloskey
Journal:  Arch Osteoporos       Date:  2016-07-27       Impact factor: 2.617

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