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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/6877

Title: Construction of an algorithm for quick detection of patients with low bone mineral density and its applicability in daily general practice
Authors: van der Voort, D.J.
Dinant, G.J.
Rinkens, P.E.
van der Voort-Duindam, C.J.
van Wersch, J.W.
GEUSENS, Piet
Issue Date: 2000
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Citation: Journal of clinical epidemiology, 53(11). p. 1095-1103
Abstract: Objective: To construct a quick algorithm to detect patients with low bone mineral density (BMD) and osteoporosis and determine its applicability in daily general practice. Design: Cross-sectional study in all 9107 postmenopausal women, aged 50-80, registered at 12 general practice centers. Subjects and measurements: All healthy women (5303) and 25% of the remaining group (943/3804) were invited to participate. Of 6246 invited women, 4725 (76%) participated. The women were questioned (state of health, medical history, family history, and food questionnaire) and examined [weight, height, body mass index (BMI), and BMD of the lumbar spine]. Statistics: Multivariable, stepwise backward and forward logistic regression analyses were performed, with BMD of the lumbar spine (L2-L4, cut-off points at 0.800 g/cm(2) for osteoporosis and 0.970 g/cm(2) for low BMD) as the dependent variable. An algorithm was constructed with those variables that correlated statistically significantly and clinically relevant with the presence of both osteoporosis and low BMD. Results: The prevalence of osteoporosis was 23%, that of low BMD was 65%. Only three variables (age, BMI, and fractures) were statistically significant and clinically relevant correlated with the presense of both osteoporosis and low BMD. Age (OR 2.70 for osteoporosis and OR 1.77 for low BMD) and fractures during the past five years (OR 3.60 for osteoporosis and OR 2.85 for low BMD) were found to be the key predictors. From the algorithm the absolute risks varied from 9% to 51% for osteoporosis and from 48% to 84% for low BMD. The corresponding relative risks varied from 1.0 to 5.7 and from 1.0 to 1.8. Conclusions: Using an algorithm with age, BMI, and fracture history subgroups at high risk could be identified. However, in whatever combination, many women with osteoporosis could not be identified. Despite the differences in methods, we found predictors for osteoporosis which were comparable with the results of other cross-sectional studies, meaning that the first selection of patients at high risk for low BMD can be done adequately by both specialists and general practitioners. (C) 2000 Elsevier Science Inc. All rights reserved.
URI: http://hdl.handle.net/1942/6877
DOI: 10.1016/S0895-4356(00)00226-2
ISI #: 000165471900003
ISSN: 0895-4356
Category: A1
Type: Journal Contribution
Appears in Collections: Research publications

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