Center for Outcomes Research (G.F., D.W.H., F.A.A., F.H.H., S.H.G.) University of Massachusetts Medical School, Worcester, Massachusetts 01605; University of Cambridge School of Clinical Medicine (J.E.C.), Addenbrooke's Hospital, Cambridge CB2 0QQ, United Kingdom; Division of Rheumatology (R.D.C.), INSERM Unité Mixte de Recherche 1033, Université de Lyon, and Department of Orthopedics and Rheumatology, Hôpital E. Herriot, 69003 Lyon, France; Department of Internal Medicine III (J.P.), Alfried Krupp Krankenhaus, Essen D-45276, Germany; Medical Research Council Lifecourse Epidemiology Unit (C.C.), University of Southampton, Southampton General Hospital, Southampton SO16 6YD, United Kingdom; National Institute for Health Research Musculoskeletal Biomedical Research Unit (C.C.), University of Oxford, Oxford OX1 3QX, United Kingdom; St Joseph's Hospital (J.D.A.), McMaster University, Hamilton, Ontario, Canada L8N 1Y2; Hospital del Mar-IMIM-Autonomous University of Barcelona (A.D.-P.), 08035 Barcelona, Spain; Research Network on Aging and Frailty, Le Fonds européen de développement régional (European Regional Development Fund), Instituto de Salud Carlos III, 28029 Madrid, Spain; University of Pittsburgh (S.L.G.), Pittsburgh, Pennsylvania 15213; Department of Endocrinology (J.C.N.), VU University Medical Centre, Amsterdam 2131PM Hoofddorp, The Netherlands; Helen Hayes Hospital and Columbia University (J.W.N.), West Haverstraw, New York 10993; Section of Rheumatology (M.R.), Department of Medicine, University of Verona, 37129 Verona, Italy; Mercy Health (N.B.W.), Cincinnati, Ohio 45236; Fred Hutchinson Cancer Research Center (A.Z.L.), Seattle, Washington 98109; University of Sydney Institute of Bone and Joint Research and Department of Rheumatology (L.M.), Royal North Shore Hospital, 2006 Sydney, Australia; Paris Descartes University and Cochin Hospital (C.R.), 75014 Paris, France; Division of Clinical Immunology and Rheumatology (K.G.S.), University of Alabama at Bir
J Clin Endocrinol Metab. 2014 Mar;99(3):817-26. doi: 10.1210/jc.2013-3468. Epub 2014 Jan 1.
Several fracture prediction models that combine fractures at different sites into a composite outcome are in current use. However, to the extent individual fracture sites have differing risk factor profiles, model discrimination is impaired.
The objective of the study was to improve model discrimination by developing a 5-year composite fracture prediction model for fracture sites that display similar risk profiles.
This was a prospective, observational cohort study.
The study was conducted at primary care practices in 10 countries.
Women aged 55 years or older participated in the study.
Self-administered questionnaires collected data on patient characteristics, fracture risk factors, and previous fractures.
The main outcome is time to first clinical fracture of hip, pelvis, upper leg, clavicle, or spine, each of which exhibits a strong association with advanced age.
Of four composite fracture models considered, model discrimination (c index) is highest for an age-related fracture model (c index of 0.75, 47 066 women), and lowest for Fracture Risk Assessment Tool (FRAX) major fracture and a 10-site model (c indices of 0.67 and 0.65). The unadjusted increase in fracture risk for an additional 10 years of age ranges from 80% to 180% for the individual bones in the age-associated model. Five other fracture sites not considered for the age-associated model (upper arm/shoulder, rib, wrist, lower leg, and ankle) have age associations for an additional 10 years of age from a 10% decrease to a 60% increase.
After examining results for 10 different bone fracture sites, advanced age appeared the single best possibility for uniting several different sites, resulting in an empirically based composite fracture risk model.
目前有几种将不同部位的骨折组合成一个复合结果的骨折预测模型在使用。然而,由于各个骨折部位的风险因素特征不同,模型的区分度会受到影响。
本研究旨在通过开发一个针对具有相似风险特征的骨折部位的 5 年复合骨折预测模型来提高模型的区分度。
这是一项前瞻性、观察性队列研究。
研究在 10 个国家的初级保健诊所进行。
年龄在 55 岁及以上的女性参加了这项研究。
通过自填式问卷收集患者特征、骨折风险因素和既往骨折的相关数据。
主要结局是首次出现髋部、骨盆、大腿、锁骨或脊柱的临床骨折的时间,这些部位的骨折均与高龄密切相关。
在所考虑的四种复合骨折模型中,年龄相关骨折模型的区分度(c 指数)最高(c 指数为 0.75,47066 名女性),其次是骨折风险评估工具(FRAX)的主要骨折和 10 部位模型(c 指数分别为 0.67 和 0.65)。在年龄相关模型中,每个单独的骨骼部位,年龄每增加 10 年,骨折风险的未调整增幅从 80%到 180%不等。对于年龄相关模型未考虑的另外 5 个骨折部位(上臂/肩部、肋骨、手腕、小腿和脚踝),年龄每增加 10 年,骨折风险的额外增幅从 10%下降到 60%不等。
在对 10 个不同的骨骼骨折部位进行研究后,高龄似乎是将几个不同部位结合起来的最佳选择,从而得出了一个基于经验的复合骨折风险模型。