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调整后的多生物标志物疾病活动评分作为类风湿关节炎影像学进展预后指标的验证:多项研究的联合分析

Validation of the adjusted multi-biomarker disease activity score as a prognostic test for radiographic progression in rheumatoid arthritis: a combined analysis of multiple studies.

作者信息

Curtis Jeffrey R, Weinblatt Michael E, Shadick Nancy A, Brahe Cecilie H, Østergaard Mikkel, Hetland Merete Lund, Saevarsdottir Saedis, Horton Megan, Mabey Brent, Flake Darl D, Ben-Shachar Rotem, Sasso Eric H, Huizinga T W

机构信息

University of Alabama at Birmingham, 510 20th Street S, Birmingham, AL, USA.

Divison of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA.

出版信息

Arthritis Res Ther. 2021 Jan 4;23(1):1. doi: 10.1186/s13075-020-02389-4.

DOI:10.1186/s13075-020-02389-4
PMID:33397438
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7784276/
Abstract

BACKGROUND

The multi-biomarker disease activity (MBDA) test measures 12 serum protein biomarkers to quantify disease activity in RA patients. A newer version of the MBDA score, adjusted for age, sex, and adiposity, has been validated in two cohorts (OPERA and BRASS) for predicting risk for radiographic progression. We now extend these findings with additional cohorts to further validate the adjusted MBDA score as a predictor of radiographic progression risk and compare its performance with that of other risk factors.

METHODS

Four cohorts were analyzed: the BRASS and Leiden registries and the OPERA and SWEFOT studies (total N = 953). Treatments included conventional DMARDs and anti-TNFs. Associations of radiographic progression (ΔTSS) per year with the adjusted MBDA score, seropositivity, and clinical measures were evaluated using linear and logistic regression. The adjusted MBDA score was (1) validated in Leiden and SWEFOT, (2) compared with other measures in all four cohorts, and (3) used to generate curves for predicting risk of radiographic progression.

RESULTS

Univariable and bivariable analyses validated the adjusted MBDA score and found it to be the strongest, independent predicator of radiographic progression (ΔTSS > 5) compared with seropositivity (rheumatoid factor and/or anti-CCP), baseline TSS, DAS28-CRP, CRP SJC, or CDAI. Neither DAS28-CRP, CDAI, SJC, nor CRP added significant information to the adjusted MBDA score as a predictor, and the frequency of radiographic progression agreed with the adjusted MBDA score when it was discordant with these measures. The rate of progression (ΔTSS > 5) increased from < 2% in the low (1-29) adjusted MBDA category to 16% in the high (45-100) category. A modeled risk curve indicated that risk increased continuously, exceeding 40% for the highest adjusted MBDA scores.

CONCLUSION

The adjusted MBDA score was validated as an RA disease activity measure that is prognostic for radiographic progression. The adjusted MBDA score was a stronger predictor of radiographic progression than conventional risk factors, including seropositivity, and its prognostic ability was not significantly improved by the addition of DAS28-CRP, CRP, SJC, or CDAI.

摘要

背景

多生物标志物疾病活动度(MBDA)检测可测量12种血清蛋白生物标志物,以量化类风湿关节炎(RA)患者的疾病活动度。一种针对年龄、性别和肥胖进行调整的新版MBDA评分,已在两个队列(OPERA和BRASS)中得到验证,可用于预测影像学进展风险。我们现在通过更多队列扩展这些研究结果,以进一步验证调整后的MBDA评分作为影像学进展风险预测指标的有效性,并将其性能与其他风险因素进行比较。

方法

分析了四个队列:BRASS和莱顿登记处以及OPERA和SWEFOT研究(总N = 953)。治疗方法包括传统的改善病情抗风湿药(DMARDs)和抗肿瘤坏死因子(TNF)药物。使用线性和逻辑回归评估每年影像学进展(ΔTSS)与调整后的MBDA评分、血清学阳性以及临床指标之间的关联。调整后的MBDA评分:(1)在莱顿和SWEFOT队列中进行验证;(2)在所有四个队列中与其他指标进行比较;(3)用于生成预测影像学进展风险的曲线。

结果

单变量和双变量分析验证了调整后的MBDA评分,并发现与血清学阳性(类风湿因子和/或抗环瓜氨酸肽抗体)、基线TSS、DAS28-CRP、CRP关节肿胀计数(SJC)或临床疾病活动指数(CDAI)相比,它是影像学进展(ΔTSS > 5)最强的独立预测指标。作为预测指标,DAS28-CRP、CDAI、SJC或CRP均未为调整后的MBDA评分增加显著信息,当与这些指标不一致时,影像学进展频率与调整后的MBDA评分一致。进展率(ΔTSS > 5)从调整后MBDA低类别(1 - 29)的< 2%增加到高类别(45 - 100)的16%。一个建模风险曲线表明风险持续增加,调整后MBDA评分最高时超过40%。

结论

调整后的MBDA评分被验证为一种可预测影像学进展的RA疾病活动度测量指标。调整后的MBDA评分是比包括血清学阳性在内的传统风险因素更强的影像学进展预测指标,并且添加DAS28-CRP、CRP、SJC或CDAI并未显著改善其预后能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/04feb2294bf8/13075_2020_2389_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/2f995a211a99/13075_2020_2389_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/c2a8d4845c18/13075_2020_2389_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/42b7e862780b/13075_2020_2389_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/04feb2294bf8/13075_2020_2389_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/2f995a211a99/13075_2020_2389_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/c2a8d4845c18/13075_2020_2389_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/42b7e862780b/13075_2020_2389_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32f2/7784276/04feb2294bf8/13075_2020_2389_Fig4_HTML.jpg

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