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疼痛检测问卷在类风湿关节炎、银屑病关节炎和脊柱关节炎中的心理测量学特性:拉施分析与重测信度

Psychometric properties of the painDETECT questionnaire in rheumatoid arthritis, psoriatic arthritis and spondyloarthritis: Rasch analysis and test-retest reliability.

作者信息

Rifbjerg-Madsen Signe, Wæhrens Eva Ejlersen, Danneskiold-Samsøe Bente, Amris Kirstine

机构信息

The Parker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, 2000, Frederiksberg, Denmark.

The Research Initiative for Activity Studies and Occupational Therapy, The Research Unit of General Practice, Department of Public Health, University of Southern Denmark, Odense, Denmark.

出版信息

Health Qual Life Outcomes. 2017 May 22;15(1):110. doi: 10.1186/s12955-017-0681-1.

Abstract

BACKGROUND

Pain is inherent in rheumatoid arthritis (RA), psoriatic arthritis (PsA) and spondyloarthritis (SpA) and traditionally considered to be of nociceptive origin. Emerging data suggest a potential role of augmented central pain mechanisms in subsets of patients, thus, valid instruments that can identify underlying pain mechanisms are needed. The painDETECT questionnaire (PDQ) was originally designed to differentiate between pain phenotypes. The objectives were to evaluate the psychometric properties of the PDQ in patients with inflammatory arthritis by applying Rasch analysis and to explore the reliability of pain classification by test-retest.

METHODS

For the Rasch analysis 900 questionnaires from patients with RA, PsA and SpA (300 per diagnosis) were extracted from 'the DANBIO painDETECT study'. The analysis was directed at the seven items assessing somatosensory symptoms and included: 1) the performance of the six-category Likert scale; 2) whether a unidimensional construct was defined; 3) the reliability and precision of estimates. Another group of 30 patients diagnosed with RA, PsA or SpA participated in a test-retest study. Intraclass Correlation Coefficients (ICC) and classification consistency were calculated.

RESULTS

The Rasch analysis revealed: (1) Acceptable psychometric rating scale properties; the frequency distribution peaked in category 0 except for item 5, threshold calibration >10 observations per category, no disorder in the category measures for all items, scale category outfit Mnsq <2.0, small distances (<1.4 logits) between thresholds for category 1, 2 and 3 for all items. (2) The principal component analysis supported unidimensionality; the standardized residuals showed that 53.7% of total variance was explained by the measure and the magnitude of first contrast had an eigenvalue of 1.5, no misfitting items, clinical insignificant different item hierarchies across diagnoses (DIF < 0.5 logits). (3) A targeted item-person map, person and item separation indices of 1.88(reliability = 0.78), and 13.04 (reliability = 0.99). The test-retest revealed: ICC: RA 0.86(0.56-0.96), PsA 0.96(0.74-0.99), SpA 0.93(0.76-98), overall 0.94(0.84-0.98). Classification consistency was: RA 70%, PsA 80%, SpA 90%, overall 80%.

CONCLUSION

The results support that the PDQ can be used as a classification instrument and assist identification of underlying pain-mechanisms in patients suffering from inflammatory arthritis.

摘要

背景

疼痛是类风湿关节炎(RA)、银屑病关节炎(PsA)和脊柱关节炎(SpA)的固有症状,传统上认为其源于伤害性刺激。新出现的数据表明,增强的中枢疼痛机制在部分患者中可能起作用,因此,需要有效的工具来识别潜在的疼痛机制。疼痛DETECT问卷(PDQ)最初旨在区分疼痛表型。目的是通过应用拉施分析评估PDQ在炎性关节炎患者中的心理测量特性,并通过重测探索疼痛分类的可靠性。

方法

为进行拉施分析,从“丹麦生物疼痛DETECT研究”中提取了900份来自RA、PsA和SpA患者的问卷(每种诊断300份)。分析针对评估躯体感觉症状的七个项目,包括:1)六类李克特量表的表现;2)是否定义了单维结构;3)估计值的可靠性和精确性。另一组30名诊断为RA、PsA或SpA的患者参加了重测研究。计算组内相关系数(ICC)和分类一致性。

结果

拉施分析显示:(1)心理测量量表特性可接受;除项目5外,频率分布在类别0达到峰值,每个类别阈值校准>10次观察,所有项目的类别测量无紊乱,量表类别装备Mnsq<2.0,所有项目类别1、2和3的阈值之间距离小(<1.4 logits)。(2)主成分分析支持单维性;标准化残差表明,总方差的53.7%由该测量解释,第一对比的大小特征值为1.5,无拟合不佳项目,各诊断间临床无显著差异的项目层次结构(差异<0.5 logits)。(3)目标项目-人图、人与项目分离指数分别为1.88(可靠性=0.78)和13.04(可靠性=0.99)。重测显示:ICC:RA为0.86(0.56 - 0.96),PsA为0.96(0.74 - 0.99),SpA为0.93(0.76 - 98),总体为0.94(0.84 - 0.98)。分类一致性为:RA 70%,PsA 80%,SpA 90%,总体80%。

结论

结果支持PDQ可作为一种分类工具,有助于识别炎性关节炎患者潜在的疼痛机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b6b/5440942/0940d5557251/12955_2017_681_Fig1_HTML.jpg

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