微血流成像对类风湿性关节炎患者艾拉莫德治疗反应性的预测价值

曾鸽; 龙军; 蒋俊杰; 蔡慧; 李巧凤

doi:10.16766/j.cnki.issn.1674-4152.003559

微血流成像对类风湿性关节炎患者艾拉莫德治疗反应性的预测价值

doi: 10.16766/j.cnki.issn.1674-4152.003559

湖州市第三人民医院特检科，浙江温州 313000

基金项目:

浙江省医药卫生科技计划项目 2021KY1098

详细信息

通讯作者:
李巧凤，E-mail：53905938@qq.com

中图分类号: R593.22R979.5
计量
- 文章访问数: 37
- HTML全文浏览量: 13
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2023-12-15
- 网络出版日期: 2024-07-22

Predictive value of micro-blood flow imaging in the response of patients with rheumatoid arthritis to lguratimod therapy

Department of Special Inspection Department, Huzhou Third Municipal Hospital, Wenzhou, Zhejiang 313000, China

摘要

摘要: 目的类风湿性关节炎(RA)患者对艾拉莫德的治疗反应与患者预后显著相关，早期识别对艾拉莫德治疗不应答的患者并及时调整治疗方案，对改善预后具有重要意义。基于此，本研究将微血流成像应用于艾拉莫德治疗的RA患者中，并探讨其对RA患者艾拉莫德治疗反应性的预测价值。方法纳入湖州市第三人民医院2022年1月—2023年1月接受艾拉莫德治疗的139例RA患者，根据治疗反应将患者分为应答组(88例)和无应答组(51例)。收集患者治疗前微血流成像资料，采用多因素logistic回归分析筛选RA艾拉莫德治疗不应答的影响因素，构建预测模型，并分析模型预测价值。结果多因素logistic回归分析显示，动脉阻力指数(OR=0.362)、血流分级0级(OR=0.440)、Ⅰ级(OR=0.314)、Ⅱ级(OR=0.120)、红细胞沉降率(ESR，OR=1.117)、疾病活动指数(DAS，OR=1.367)、晨僵时间(OR=1.159)均是RA艾拉莫德治疗不应答的影响因素(P < 0.05)。基于上述5项指标构建的预测模型预测RA患者对艾拉莫德治疗不应答的C-index为0.816，AUC为0.816(95% CI: 0.808~0.938)。结论动脉阻力指数、血流分级、ESR、DAS、晨僵时间是RA患者艾拉莫德治疗反应的影响因素，基于此构建的预测模型可用于RA患者艾拉莫德治疗不应答发生风险的预测。
- 类风湿性关节炎 /
- 微血流成像 /
- 艾拉莫德 /
- 预测价值
Abstract: Objective The response of patients with rheumatoid arthritis (RA) to Elamod therapy is significantly related to their prognosis. It is of great significance to identify patients who do not respond to treatment with Elamod at an early stage and adjust the treatment plan in time. Based on this, this study applied micro-blood flow imaging technology to the patients with RA treated by Elamod, and discussed its predictive value for the curative effect of Erasmus in the treatment of RA patients. Methods A total of 139 patients with RA who received Elamod in the Huzhou Third Municipal Hospital from January 2022 to January 2023 were divided into response group (n=88) and non-response group (n=51) according to their treatment response. The data of micro-blood flow imaging before treatment were collected, and the factors influencing non-response to RA Elamod treatment were screened by multivariate logistic regression, and a prediction model was constructed to analyze the predictive value of the model. Results Multivariate logistic regression analysis showed that high arterial resistance index (OR=0.362), blood flow grade 0 (OR=0.440), grade Ⅰ (OR=0.314), and grade Ⅱ (OR=0.120) were the protective factors for the non-response of RA' Elamod (P < 0.05). High ESR (OR=1.117), high DAS score (OR=1.367), and long-term morning stiffness (OR=1.159) were the risk factors (P < 0.05). Based on the prediction model of the above five indexes, the C-index and AUC of RA patients who did not respond to Elamod treatment were 0.816 and 0.816 (95% CI: 0.808-0.938), respectively. Conclusion Pulse resistance index, blood flow classification, ESR, DAS score, and morning stiffness time are the factors influencing treatment response of patients with RA, and the prediction model on them can be used to predict the risk of non-response of RA patients to treatment.
- Rheumatoid arthritis /
- Microfluidic imaging /
- Elamod /
- Predicted value

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图 1 预测RA患者对艾拉莫德治疗不应答发生风险的列线图

Figure 1. A nomogram predicting the risk of RA patients not responding to eramode therapy

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图 2 预测模型的内部验证曲线

Figure 2. The internal validation curve of the prediction model

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图 3 预测模型的ROC曲线

Figure 3. ROC curve of prediction model

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图 4 预测模型的决策曲线

Figure 4. The decision curve of the prediction model

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表 1 2组RA患者临床资料比较

Table 1. Comparison of clinical data between two groups of patients

指标	应答组(n=88)	无应答组(n=51)	统计量	P值
年龄(x ± s, 岁)	45.32±10.25	42.79±12.3	1.235^a	0.220
性别[例(%)]			0.739^b	0.390
男性	6(6.82)	1(1.96)
女性	82(93.18)	50(98.04)
吸烟[例(%)]	11(12.50)	8(15.69)	0.278^b	0.598
饮酒[例(%)]	9(10.23)	3(5.88)	0.320^b	0.572
病程(x ± s, 月)	9.12±2.07	10.01±3.24	1.764^a	0.082
BMI(x ± s)	26.34±2.56	25.69±3.07	1.277^a	0.205
动脉阻力指数(x ± s, IU/mL)	0.72±0.10	0.62±0.08	6.467^a	< 0.001
血流分级			61.901^c	< 0.001
0级	13(14.77)	1(1.96)
Ⅰ级	48(54.55)	3(5.88)
Ⅱ级	24(27.27)	22(43.14)
Ⅲ级	3(3.41)	25(49.02)
RF(x ± s, U/mL)	104.41±33.25	112.84±32.86	1.451^a	0.150
CCP抗体(x ± s, U/mL)	469.91±116.80	443.56±99.40	1.411^a	0.161
ESR(x ± s, mm/h)	85.88±18.81	101.43±17.61	4.893^a	< 0.001
CRP(x ± s, mg/L)	76.30±17.43	86.00±23.49	2.568^a	0.012
DAS(x ± s, 分)	4.06±1.05	5.81±1.17	8.820^a	< 0.001
晨僵时间(x ± s, min)	58.42±13.46	69.27±16.19	4.044^a	< 0.001
疼痛评分(x ± s, 分)	4.32±1.07	4.56±1.36	1.081^a	0.283
健康评估问卷得分(x ± s, 分)	14.92±3.54	16.85±3.75	2.985^a	0.004
关节压痛数(x ± s, 个)	10.32±2.07	9.42±3.08	1.858^a	0.067
关节肿胀数(x ± s, 个)	9.41±2.11	8.85±2.16	1.486^a	0.140
注：^a为t值，^b为χ²值，^c为Z值。

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表 2 变量赋值情况

Table 2. Variable assignment

变量	赋值方法
对艾拉莫德的治疗反应	应答=0，不应答=1
动脉阻力指数	以实际值赋值
血流分级	0级=0，Ⅰ级=1，Ⅱ级=2，Ⅲ级=3
ESR	以实际值赋值
CRP	以实际值赋值
DAS分数	以实际值赋值
晨僵时间	以实际值赋值
健康评估问卷分数	以实际值赋值

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表 3 RA患者艾拉莫德治疗不应答影响因素的多因素logistic回归分析

Table 3. Multivariate logistic regression analysis of independent risk factors for RA patients not responding to treatment with Elamod

变量	B	SE	Waldχ²	P值	OR值	95% CI
动脉阻力指数	-1.184	0.803	10.767	< 0.001	0.362	0.187~0.501
血流分级			17.362	< 0.001
0级	-0.822	0.555	2.195	0.038	0.440	0.148~0.904
Ⅰ级	-1.157	0.521	4.928	0.026	0.314	0.113~0.873
Ⅱ级	-2.124	0.515	16.997	< 0.001	0.120	0.044~0.328
ESR	0.110	0.044	6.293	0.012	1.117	1.024~1.217
CRP	0.038	0.030	1.624	0.202	1.039	0.980~1.102
DAS	0.313	0.385	1.069	0.008	1.367	1.137~1.638
晨僵时间	0.148	0.054	7.352	0.007	1.159	1.042~1.290
健康评估问卷得分	0.316	0.193	2.678	0.102	1.372	0.939~2.003

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参考文献(20)

[1]	RADU A F, BUNGAU S G. Management of rheumatoid arthritis: an overview[J]. Cells, 2021, 10(11): 2857. DOI: 10.3390/cells10112857.
[2]	何伟珍, 张会昌, 莫入, 等. 艾拉莫德联合昆仙胶囊治疗难治性狼疮性肾炎一例[J]. 新医学, 2020, 51(1): 71-73. doi: 10.3969/j.issn.0253-9802.2020.01.016 HE W Z, ZHANG H C, MO R, et al. Iguratimod combined with Kunxian capsule in treatment of refractory lupus nephritis: a case report[J]. Journal of New Medicine, 2020, 51(1): 71-73. doi: 10.3969/j.issn.0253-9802.2020.01.016
[3]	许海艳, 况南珍, 张瑜娟, 等. 类风湿性关节炎治疗方法的研究进展[J]. 南昌大学学报(医学版), 2020, 60(5): 97-102. XU H Y, KUANG N Z, ZHANG Y J, et al. Research Progress in Treatment of Rheumatoid Arthritis[J]. Journal of Nanchang University (Medical Edition), 2020, 60(5): 97-102.
[4]	XIE S, LI S, TIAN J, et al. Iguratimod as a new drug for rheumatoid arthritis: current landscape[J]. Front Pharmacol, 2020, 11(1): 73. DOI: 10.3389/fphar.2020.00073.
[5]	NOZAKI Y, ORIBE M, TOMITA D, et al. Iguratimod versus salazosulfapyridine in rheumatoid arthritis patients with an inadequate response to methotrexate: adjusted with propensity score matching[J]. Mod Rheumatol, 2023, 33(3): 472-480. doi: 10.1093/mr/roac060
[6]	DENG L, YAO F, TIAN F, et al. Influence of iguratimod on bone metabolism in patients with rheumatoid arthritis: a meta-analysis[J]. Int J Clin Pract, 2022, 2022(1): 5684293. DOI: 10.1155/2022/5684293.
[7]	中华医学会风湿病学分会. 2018中国类风湿关节炎诊疗指南[J]. 临床医学研究与实践, 2018, 3(12): 242-251. Rheumatology Branch of Chinese Medical Association. 2018 Chinese guideline for the diagnosis and treatment of rheumatoid arthritis[J]. Chin J Intern Med, 2018, 3(12): 242-251.
[8]	MEZNERICS F A, KEMÉNY L V, GUNTHER E, et al. Multibiomarker disease activity score: an objective tool for monitoring rheumatoid arthritis? A systematic review and meta-analysis[J]. Rheumatology(Oxford), 2023, 62(6): 2048-2059. doi: 10.1093/rheumatology/keac715
[9]	FRAENKEL L, BATHON J M, ENGLAND B R, et al. 2021 American College of Rheumatology Guideline for the treatment of rheumatoid arthritis[J]. Arthritis Rheumatol, 2021, 73(7): 1108-1123. doi: 10.1002/art.41752
[10]	LIN Y J, ANZAGHE M, SCHVLKE S. Update on the pathomechanism, diagnosis, and treatment options for rheumatoid arthritis[J]. Cells, 2020, 9(4): 880. DOI: 10.3390/cells9040880.
[11]	CHAKRABORTY D, GUPTA K, BISWAS S. A mechanistic insight of phytoestrogens used for rheumatoid arthritis: an evidence-based review[J]. Biomed Pharmacother, 2021, 133(1): 111039. DOI: 10.1016/j.biopha.2020.111039.
[12]	NOZAKI Y. Iguratimod: novel molecular insights and a new csDMARD for rheumatoid arthritis, from Japan to the world[J]. Life(Basel), 2021, 11(5): 457. DOI: 10.3390/life11050457.
[13]	MATSUO H, IMAMURA A, SHIMIZU M, et al. Prediction of recurrence and remission using superb microvascular imaging in rheumatoid arthritis[J]. J Med Ultrason(2001), 2020, 47(1): 131-138. doi: 10.1007/s10396-019-00978-8
[14]	CUSH J J. Rheumatoid arthritis: early diagnosis and treatment[J]. Rheum Dis Clin North Am, 2022, 48(2): 537-547. doi: 10.1016/j.rdc.2022.02.010
[15]	DIAO X H, SHEN Y, CHEN L, et al. Superb microvascular imaging is as sensitive as contrast-enhanced ultrasound for detecting synovial vascularity in rheumatoid arthritis[J]. Quant Imaging Med Surg, 2022, 12(5): 2866-2876. doi: 10.21037/qims-21-859
[16]	SESKUTE G, DAPKUTE A, KAUSAITE D, et al. Multidisciplinary diagnostic dilemma in differentiating Madelung' s disease-the value of superb microvascular imaging technique: a case report[J]. World J Clin Cases, 2021, 9(21): 6145-6154. doi: 10.12998/wjcc.v9.i21.6145
[17]	TANG K, LIU M, ZHU Y, et al. The clinical application of ultrasonography with superb microvascular imaging-a review[J]. J Clin Ultrasound, 2022, 50(5): 721-732. doi: 10.1002/jcu.23210
[18]	李雪兰, 杨萌, 赵辰阳, 等. 超微血流成像在类风湿关节炎滑膜血流评估中的应用[J]. 中华医学超声杂志(电子版), 2023, 20(2): 232-237. LI X L, YANG M, ZHAO C Y, et al. Ultramicroangiography for evaluation of synovial blood flow in rheumatoid arthritis patients[J]. Chinese Journal of Medical Ultrasound(Electronic Edition), 2023, 20(2): 232-237.
[19]	MATSUO H, KAMADA M, IMAMURA A, et al. Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test[J]. Sci Rep, 2022, 12(1): 7224. DOI: 10.1038/s41598-022-11361-y.
[20]	SESKUTE G, JASOINYTE G, RUGIENE R, et al. The use of superb microvascular imaging in evaluating rheumatic diseases: a systematic review[J]. Medicina(Kaunas), 2023, 59(9): 1641. DOI: 10.3390/medicina59091641.