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基于 Frax 和 Garvan 算法的最佳骨折预测阈值与治疗起始时间:RAC-OST-POL 研究中人群代表性女性队列的纵向观察。

Optimal fracture prediction thresholds for therapy onset, established from FRAX and Garvan algorithms: a longitudinal observation of the population representative female cohort from the RAC-OST-POL Study.

机构信息

Department and Clinic of Internal Diseases, Diabetology, and Nephrology, Metabolic Bone Diseases Unit, Faculty of Medical Sciences in Zabrze, Medical University of Silesia in Katowice, 3-Maja 13/15 Street, 41-800, Zabrze, Poland.

Department of Applied Informatics, Silesian University of Technology, 44-100, Gliwice, Poland.

出版信息

Arch Osteoporos. 2023 Nov 16;18(1):136. doi: 10.1007/s11657-023-01346-3.

DOI:10.1007/s11657-023-01346-3
PMID:37973685
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10654207/
Abstract

UNLABELLED

The study shows that the use of unified cutoff thresholds to identify high fracture risks by two popular calculators-FRAX and Garvan-leads to a significant discrepancy between the prediction of fractures and their actual prevalence over the period of 10 years. On the basis of the ROC analyses, a proposal of differentiated thresholds is presented. They were established at 6% for FRAX major fracture risk, 1.4% for FRAX hip fracture risk, 14.4% for Garvan any fracture risk, and 8.8% for Garvan hip fracture risk.

PURPOSE/INTRODUCTION: The aim of the study was to verify how much were the tools, designed to predict fracture risks, precise vs. the actual fracture incidence values over a prospective observation.

METHODS

The study group consisted of a population-based postmenopausal sample from the RAC-OST-POL Study. At baseline, there were 978 subjects at the mean age of 66.4 ± 7.8 years and, after a 10-year follow-up, 640 women remained at the mean age of 75.0 ± 6.95 years. At baseline, the fracture risk was established by the FRAX and Garvan tools.

RESULTS

During the observation period, 190 osteoporotic fractures were identified in 129 subjects. When high-risk fracture cutoff thresholds (of 10% for major/any and 3% for hip fractures) were employed, only 19.59% of major fractures and 50% of hip fractures were identified in the high-risk group. For the Garvan tool, the percentage of correctly predicted fractures for any and hip fractures was 86.05% and 71.43%, respectively. Nevertheless, the fracture prediction by the Garvan tool was associated with the qualification of numerous subjects to the high-risk group, who subsequently did not experience a fracture in the 10-year follow-up period (false-positive prediction). Based on the ROC analyses, new high-risk thresholds were proposed individually for each calculator, improving the sensitivity, specificity, and diagnostic accuracy of these tools. They were established at 6% for FRAX major fracture risk, 1.4% for FRAX hip fracture risk, 14.4% for Garvan any fracture risk, and 8.8% for Garvan hip fracture risk.

CONCLUSIONS

The current prospective study enabled to establish new, optimal thresholds for therapy initiation. Such a modified approach may enable a more accurate identification of treatment requiring patients and, in consequence, reduce the number of new fractures.

摘要

目的/引言:本研究旨在验证预测骨折风险的工具在前瞻性观察中与实际骨折发生率相比的精确程度。

方法

研究组由来自 RAC-OST-POL 研究的基于人群的绝经后样本组成。在基线时,共有 978 名受试者,平均年龄为 66.4±7.8 岁,经过 10 年随访后,640 名女性的平均年龄为 75.0±6.95 岁。在基线时,使用 FRAX 和 Garvan 工具确定骨折风险。

结果

在观察期间,129 名受试者中有 190 例骨质疏松性骨折。当使用高风险骨折截断阈值(主要/任何骨折为 10%,髋部骨折为 3%)时,仅在高风险组中识别出 19.59%的主要骨折和 50%的髋部骨折。对于 Garvan 工具,任何和髋部骨折的正确预测骨折比例分别为 86.05%和 71.43%。然而,Garvan 工具的骨折预测与许多受试者被归类为高风险组有关,这些受试者随后在 10 年随访期间并未经历骨折(假阳性预测)。基于 ROC 分析,为每个计算器分别提出了新的高风险阈值,提高了这些工具的敏感性、特异性和诊断准确性。它们分别设定为 FRAX 主要骨折风险为 6%、FRAX 髋部骨折风险为 1.4%、Garvan 任何骨折风险为 14.4%和 Garvan 髋部骨折风险为 8.8%。

结论

本前瞻性研究能够为治疗启动确定新的最佳阈值。这种改进的方法可以更准确地识别需要治疗的患者,从而减少新发骨折的数量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bce2/10654207/6b2ecee2361c/11657_2023_1346_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bce2/10654207/c9c5391a4624/11657_2023_1346_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bce2/10654207/6b2ecee2361c/11657_2023_1346_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bce2/10654207/c9c5391a4624/11657_2023_1346_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bce2/10654207/6b2ecee2361c/11657_2023_1346_Fig2_HTML.jpg

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