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复合定量膝关节结构指标预测膝关节骨关节炎的加速发展:来自骨关节炎倡议的数据。

Composite quantitative knee structure metrics predict the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative.

机构信息

Division of Rheumatology, Allergy, & Immunology, Tufts Medical Center, 800 Washington Street, Box 406, Boston, MA, 02111, USA.

Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.

出版信息

BMC Musculoskelet Disord. 2020 May 13;21(1):299. doi: 10.1186/s12891-020-03338-7.

DOI:10.1186/s12891-020-03338-7
PMID:32404099
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7222452/
Abstract

BACKGROUND

We aimed to determine if composite structural measures of knee osteoarthritis (KOA) progression on magnetic resonance (MR) imaging can predict the radiographic onset of accelerated knee osteoarthritis.

METHODS

We used data from a nested case-control study among participants from the Osteoarthritis Initiative without radiographic KOA at baseline. Participants were separated into three groups based on radiographic disease progression over 4 years: 1) accelerated (Kellgren-Lawrence grades [KL] 0/1 to 3/4), 2) typical (increase in KL, excluding accelerated osteoarthritis), or 3) no KOA (no change in KL). We assessed tibiofemoral cartilage damage (four regions: medial/lateral tibia/femur), bone marrow lesion (BML) volume (four regions: medial/lateral tibia/femur), and whole knee effusion-synovitis volume on 3 T MR images with semi-automated programs. We calculated two MR-based composite scores. Cumulative damage was the sum of standardized cartilage damage. Disease activity was the sum of standardized volumes of effusion-synovitis and BMLs. We focused on annual images from 2 years before to 2 years after radiographic onset (or a matched time for those without knee osteoarthritis). To determine between group differences in the composite metrics at all time points, we used generalized linear mixed models with group (3 levels) and time (up to 5 levels). For our prognostic analysis, we used multinomial logistic regression models to determine if one-year worsening in each composite metric change associated with future accelerated knee osteoarthritis (odds ratios [OR] based on units of 1 standard deviation of change).

RESULTS

Prior to disease onset, the accelerated KOA group had greater average disease activity compared to the typical and no KOA groups and this persisted up to 2 years after disease onset. During a pre-radiographic disease period, the odds of developing accelerated KOA were greater in people with worsening disease activity [versus typical KOA OR (95% confidence interval [CI]): 1.58 (1.08 to 2.33); versus no KOA: 2.39 (1.55 to 3.71)] or cumulative damage [versus typical KOA: 1.69 (1.14 to 2.51); versus no KOA: 2.11 (1.41 to 3.16)].

CONCLUSIONS

MR-based disease activity and cumulative damage metrics may be prognostic markers to help identify people at risk for accelerated onset and progression of knee osteoarthritis.

摘要

背景

我们旨在确定磁共振成像(MR)上膝关节骨关节炎(KOA)进展的综合结构指标是否可以预测放射学上加速的膝关节骨关节炎的发生。

方法

我们使用了来自骨关节炎倡议中没有基线放射学 KOA 的参与者的嵌套病例对照研究的数据。根据 4 年内的放射学疾病进展,参与者分为三组:1)加速组(Kellgren-Lawrence 分级[KL] 0/1 至 3/4),2)典型组(KL 增加,不包括加速性骨关节炎),或 3)无 KOA 组(KL 无变化)。我们使用半自动化程序评估了 3T MR 图像上的胫骨股骨软骨损伤(四个区域:内侧/外侧胫骨/股骨)、骨髓病变(BML)体积(四个区域:内侧/外侧胫骨/股骨)和整个膝关节积液-滑膜炎体积。我们计算了两个基于磁共振的综合评分。累积损伤是标准化软骨损伤的总和。疾病活动度是标准化积液-滑膜炎和 BML 体积之和。我们专注于从放射学发病前 2 年到发病后 2 年(或没有膝关节骨关节炎的情况下匹配的时间)的每年图像。为了确定所有时间点复合指标在组间的差异,我们使用了具有组(3 个水平)和时间(多达 5 个水平)的广义线性混合模型。对于我们的预后分析,我们使用多变量逻辑回归模型来确定每个复合指标变化的一年恶化是否与未来的加速膝关节骨关节炎相关(基于单位为 1 标准差的变化的比值比[OR])。

结果

在疾病发病前,与典型 KOA 组和无 KOA 组相比,加速 KOA 组的平均疾病活动度更高,并且这种情况一直持续到发病后 2 年。在放射学前疾病期间,疾病活动度恶化的人发展为加速 KOA 的可能性更大[与典型 KOA 相比:1.58(1.08 至 2.33);与无 KOA 相比:2.39(1.55 至 3.71)]或累积损伤[与典型 KOA 相比:1.69(1.14 至 2.51);与无 KOA 相比:2.11(1.41 至 3.16)]。

结论

基于磁共振的疾病活动度和累积损伤指标可能是预测标志物,有助于识别有加速膝关节骨关节炎发病和进展风险的人群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1b1/7222452/f225671b084c/12891_2020_3338_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1b1/7222452/bf671a07e54c/12891_2020_3338_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1b1/7222452/dd76413dd726/12891_2020_3338_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1b1/7222452/f225671b084c/12891_2020_3338_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1b1/7222452/bf671a07e54c/12891_2020_3338_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1b1/7222452/dd76413dd726/12891_2020_3338_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c1b1/7222452/f225671b084c/12891_2020_3338_Fig3_HTML.jpg

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