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Abstract Number: 338

Measurement Properties of Paindetect, a Neuropathic Pain Screening Tool, for Evaluating Pain Phenotype in Patients with Rheumatoid Arthritis: Developing Neuropathic Pain Scale As a Measure of Treatment Outcome By Applying Rasch Analysis

Yong Gil Hwang1, Lei Zhu2, Ajay Wasan3 and Larry W. Moreland1, 1Rheumatology & Clinical Immunology, University of Pittsburgh, Pittsburgh, PA, 2University of Pittsburgh, Pittsburgh, PA, 3Departments of Anesthesiology and Psychiatry, University of Pittsburgh, Pittsburgh, PA

Meeting: 2017 ACR/ARHP Annual Meeting

Date of first publication: September 18, 2017

Keywords: Outcome measures, pain and rheumatoid arthritis (RA)

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Session Information

Date: Sunday, November 5, 2017

Title: Patient Outcomes, Preferences, and Attitudes Poster I

Session Type: ACR Poster Session A

Session Time: 9:00AM-11:00AM

Background/Purpose: Ongoing pain state in rheumatoid arthritis (RA) often persists after the resolution of inflammation, indicating the transition between the acute inflammatory pain and post-inflammatory persistent pain phenotypes. PainDETECT (PDQ) was developed as a self-reported neuropathic pain screening tool. We conducted a Rasch analysis to investigate whether measurement properties of PDQ can be used as an outcome measure.

 Methods: For RA subjects enrolled in the University of Pittsburgh Rheumatoid Arthritis Comparative Effectiveness Registry (RACER), Rasch analysis was conducted for all RACER patients who completed PDQ and PROMIS29 short form. Unidimensionality, reliability, item difficulty, category functioning, and differential item function were examined using the partial credit model for polytomous items (WINSTEPS ver. 3.93.2). Differential item function (DIF) analyses for gender and age groups (<60,60-70,>70) were performed to examine item invariance for different groups.

Results:   For the 302 subjects analyzed, age was 63.8 ¡¾ 12.4 (mean ¡¾ SD) years with disease duration of 18.4¡¾11.9 years. Given misfit and high fit residuals, time course and radiating pain items were removed. Remaining 7-item PDQ fit the Rasch model (Table 1). Item residuals showed high correlation between the burning and slight pressure items and these items were treated as a testlet. Cold-or-heat item exhibited marginally disordered threshold but the rescoring did not substantially affect fit (Table 2). DIF analyses for gender and age groups showed uniform DIF. Person-item distribution showed that PDQ was reasonably targeted (Figure).

Conclusion:   Rasch analysis of 7-item PDQ suggests that PDQ may function as an outcome measure and may provide a useful tool to predict RA treatment outcome for neuropathic pain.

Table 1. Summary statistics for all analyses (*Log-likelihood chi-squared: The chi-square value is approximately = -2 * log-likelihood of the active data points, **probability: the probability that these data fit the Rasch model globally, df: degree of freedom, SE: Standard Error, RMSR: Root-Mean-Square Residual, MNSQ: Mean Square)

PainDETECT items

9 items

8 items

7 items

7 items (testlet)

Global statistics

Log_-liklihood chi-squard*

5521.44

4863.36

4194.34

3534.94

df

5553

4923

4257

3610

Probability**

0.62

0.72

0.75

0.811

Global RMSR with expected value

0.88 (0.89)

0.90 (0.89)

0.89 (0.88)

0.88 (0.87)

Person

Measure (location, logit)

-1.62

-1.66

-1.71

-1.76

SE (logit)

0.54

0.61

0.67

0.73

Infit MNSQ

1.02

1.04

1.06

1.04

Outfit MNSQ

1.06

1.03

1.04

1.03

Separation

1.44

1.32

1.29

1.17

Reliability

0.68

0.63

0.63

0.58

Item

Measure (location, logit)

0.00

0.00

0.00

0.00

SE (logit)

0.07

0.07

0.07

0.07

Infit MNSQ

0.98

1.02

1.03

1.03

Outfit MNSQ

1.08

1.03

1.04

1.03

Separation

5.15

5.05

5.40

5.74

Reliability

0.96

0.96

0.97

0.97

 

Table 2. Item difficulty, fit statistics and step difficulties (thresholds) represented with logit unit of PDQ 7 items with 6-level scaling (SE: Standard Error, RMSR: Root-Mean-Square Residual, MNSQ: Mean Square, *: disordering of the step difficulties)

Item

Difficulty (logit)

SE (logit)

Infit

MNSQ

Outfi

MNSQ

0

1

2

3

4

5

Burning

-0.21

0.06

0.92

0.90

-2.61

-1.47

-1.19

-0.73

0.11

1.26

Tingling

-0.08

0.06

0.88

0.89

-2.56

-1.40

-1.26

-0.46

0.20

1.27

Light touch

0.38

0.07

0.87

0.92

-2.41

-0.97

-0.92

-0.10

0.35

1.47

Sudden attack

-0.14

0.06

1.15

1.18

-2.54

-1.40

-1.10

-0.69

-0.26

1.57

Cold or heat

0.53

0.08

1.19

1.23

-2.35

-1.03

-0.56

-0.24*

-0.34*

1.40

Numbness

0.20

0.07

1.01

0.96

-2.42

-1.10

-0.92

-0.60

0.49

1.45

Slight pressure

-0.69

0.06

1.19

1.20

-2.91

-1.68

-1.47

-1.04

-0.35

0.63

 


Disclosure: Y. G. Hwang, Pfizer Inc, 2; L. Zhu, None; A. Wasan, None; L. W. Moreland, None.

To cite this abstract in AMA style:

Hwang YG, Zhu L, Wasan A, Moreland LW. Measurement Properties of Paindetect, a Neuropathic Pain Screening Tool, for Evaluating Pain Phenotype in Patients with Rheumatoid Arthritis: Developing Neuropathic Pain Scale As a Measure of Treatment Outcome By Applying Rasch Analysis [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/measurement-properties-of-paindetect-a-neuropathic-pain-screening-tool-for-evaluating-pain-phenotype-in-patients-with-rheumatoid-arthritis-developing-neuropathic-pain-scale-as-a-measure-of-treatmen/. Accessed .
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