Pain is a common symptom of multiple sclerosis (MS). The reliability of outcome measures for pain and the accuracy of screening tools are essential for treatment purposes.
Objectives:
This study investigated the test–retest reliability of Neuropathic Pain Scale (NPS), Neuropathic Pain Symptom Inventory (NPSI), Brief Pain Inventory-Short Form (BPI-SF), Nordic Musculoskeletal Questionnaire (NMQ), Douleur Neuropathique 4 (DN4), and painDETECT, and the accuracy of DN4 and painDETECT.
Methods:
110 persons with MS were included (mean age: 45.35 ± 12.73; Expanded Disability Status Scale (EDSS): 3 (Interquartile range, IQR: 2.6)). The reliability (internal consistency, test–retest, measurement error) and accuracy (area under the curve (AUC), specificity, sensitivity, positive predictive value, and negative predictive value) were analyzed.
Results:
All included pain assessment tools showed good test–retest reliability (Intraclass correlation coefficient, ICC, (95% confidence interval, CI): 0.85 (0.79–0.89)–0.89 (0.84–0.92)) and good-to-excellent internal consistency (Cronbach’s α: 0.73–0.93). The test–retest reliability of NMQ was moderate to substantial (Cohen’s κ = 0.54–0.87). The overall accuracy of DN4 compared to the neurologist’s diagnosis was acceptable (AUC = 0.762), while that of painDETECT was poor (AUC = 0.682) and demonstrated inadequate predictive ability.
Conclusion:
All pain outcome measures were reliable in patients with MS (pwMS). DN4 can be used to screen neuropathic pain in MS.
O’ConnorABSchwidSRHerrmannDN, et al. Pain associated with multiple sclerosis: Systematic review and proposed classification. Pain2008; 137(1): 96–111.
2.
TruiniAGaleottiFLa CesaS, et al. Mechanisms of pain in multiple sclerosis: A combined clinical and neurophysiological study. Pain2012; 153(10): 2048–2054.
TruiniABarbantiPPozzilliC, et al. A mechanism-based classification of pain in multiple sclerosis. J Neurol2013; 260(2): 351–367.
5.
FoleyPLVesterinenHMLairdBJ, et al. Prevalence and natural history of pain in adults with multiple sclerosis: Systematic review and meta-analysis. Pain2013; 154(5): 632–642.
6.
RodriguesPda SilvaBTrevisanG. A systematic review and meta-analysis of neuropathic pain in multiple sclerosis: Prevalence, clinical types, sex dimorphism, and increased depression and anxiety symptoms. Neurosci Biobehav Rev2023; 154: 105401.
7.
YilmazerCLamersISolaroC, et al. Clinical perspective on pain in multiple sclerosis. Mult Scler2022; 28(4): 502–511.
8.
TysonSFBrownP. How to measure pain in neurological conditions? A systematic review of psychometric properties and clinical utility of measurement tools. Clin Rehabil2014; 28(7): 669–686.
9.
GalerBSJensenMP. Development and preliminary validation of a pain measure specific to neuropathic pain: The Neuropathic Pain Scale. Neurology1997; 48(2): 332–338.
10.
BouhassiraDAttalNFermanianJ, et al. Development and validation of the neuropathic pain symptom inventory. Pain2004; 108: 248–257.
11.
CleelandCSRyanK. The brief pain inventory. Pain Res Group1991; 20: 143–147.
12.
MichalskiDLiebigSThomaeE, et al. Pain in patients with multiple sclerosis: A complex assessment including quantitative and qualitative measurements provides for a disease-related biopsychosocial pain model. J Pain Res2011; 4: 219–225.
13.
KahramanTGençAGözE. The Nordic Musculoskeletal Questionnaire: Cross-cultural adaptation into Turkish assessing its psychometric properties. Disabil Rehabil2016; 38(21): 2153–2160.
14.
ShayestehAzarMKariminasabMHSaraviMS, et al. A survey of severity and distribution of musculoskeletal pain in multiple sclerosis patients; a cross-sectional study. Arch Bone Jt Surg2015; 3(2): 114–118.
15.
FinnerupNBKunerRJensenTS. Neuropathic pain: From mechanisms to treatment. Physiol Rev2021; 101(1): 259–301.
16.
BouhassiraDAttalNAlchaarH, et al. Comparison of pain syndromes associated with nervous or somatic lesions and development of a new neuropathic pain diagnostic questionnaire (DN4). Pain2005; 114(1–2): 29–36.
17.
FreynhagenRBaronRGockelU, et al. PainDETECT: A new screening questionnaire to identify neuropathic components in patients with back pain. Curr Med Res Opin2006; 22(10): 1911–1920.
18.
MonticoneMGaleotoGBerardiA, et al. Psychometric properties of assessment tools. In: GaleotoGBerardiATofaniM, et al. (eds) Measuring spinal cord injury: A practical guide of outcome measures. Cham: Springer International Publishing, 2021, pp. 7–15.
19.
DunkerØGrotleMKvaløyMB, et al. Accuracy of neuropathic pain measurements in patients with symptoms of polyneuropathy: Validation of painDETECT, self-completed Leeds assessment of neuropathic symptoms and signs, and douleur neuropathique 4. Pain2023; 164: 991–1001.
20.
EppingRVerhagenAPHoebinkE, et al. The diagnostic accuracy and test–retest reliability of the Dutch PainDETECT and the DN4 screening tools for neuropathic pain in patients with suspected cervical or lumbar radiculopathy. Musculoskelet Sci Pract2017; 30: 72–79.
21.
TzamakouEPetrouATefaL, et al. Detection of neuropathic pain in end-stage cancer patients: Diagnostic accuracy of two questionnaires. Pain Pract2018; 18(6): 768–776.
22.
AhoTMustonenLKalsoE, et al. Douleur neuropathique 4 (DN4) stratifies possible and definite neuropathic pain after surgical peripheral nerve lesion. Eur J Pain2020; 24(2): 413–422.
23.
Ríos-LeónMTaylorJSegura-FragosoA, et al. Usefulness of the DN4, S-LANSS, and painDETECT screening questionnaires to detect the neuropathic pain components in people with acute whiplash-associated disorders: A cross-sectional study. Pain Med2023; 25(5): 344–351.
24.
TimmermanHSteegersMAHHuygenFJPM, et al. Investigating the validity of the DN4 in a consecutive population of patients with chronic pain. PLoS ONE2017; 12(11): e0187961.
25.
ThompsonAJBanwellBLBarkhofF, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol2018; 17: 162–173.
26.
KuorinkaIJonssonBKilbomA, et al. Standardised Nordic questionnaires for the analysis of musculoskeletal symptoms. Appl Ergon1987; 18(3): 233–237.
27.
FischerJSRudickRACutterGR, et al. The Multiple Sclerosis Functional Composite measure (MSFC): An integrated approach to MS clinical outcome assessment. Mult Scler1999; 5(4): 244–250.
28.
DemeurisseGDemolORobayeE. Motor evaluation in vascular hemiplegia. Eur Neurol1980; 19: 38219800101–389.
29.
FeysPLamersIFrancisG, et al. The Nine-Hole Peg Test as a manual dexterity performance measure for multiple sclerosis. Mult Scler2017; 23(5): 711–720.
30.
BohannonRWSmithMB. Interrater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther1987; 67: 206–207.
31.
LarsonRD. Psychometric properties of the modified fatigue impact scale. Int J MS Care2013; 15: 15–20.
32.
BenedictRHDeLucaJPhillipsG, et al. Validity of the Symbol Digit Modalities Test as a cognition performance outcome measure for multiple sclerosis. Mult Scler2017; 23(5): 721–733.
33.
ZigmondASSnaithRP. The hospital anxiety and depression scale. Acta Psychiatr Scand1983; 67: 361–370.
34.
CohenSKamarckTMermelsteinR. A global measure of perceived stress. J Health Soc Behav1983; 24: 385–396.
35.
BuysseDJReynoldsCF3rdMonkTH, et al. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res1989; 28(2): 193–213.
36.
CraigCLMarshallALSjöströmM, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc2003; 35: 1381–1395.
37.
MokkinkLBTerweeCBPatrickDL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol2010; 63(7): 737–745.
38.
TerweeCBPrinsenCACChiarottoA, et al. COSMIN methodology for evaluating the content validity of patient-reported outcome measures: A Delphi study. Qual Life Res2018; 27(5): 1159–1170.
39.
MokkinkLBde VetHCWPrinsenCAC, et al. COSMIN risk of bias checklist for systematic reviews of patient-reported outcome measures. Qual Life Res2018; 27(5): 1171–1179.
40.
PricePCJhangianiRChiangI, et al. Research methods in psychology. 3rd American ed. Washington, DC: Press Books Publications, 2017.
41.
McHughML. Interrater reliability: The kappa statistic. Biochem Med2012; 22: 276–282.
42.
KooTKLiMY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med2016; 15: 155–163.
43.
TerweeCBBotSDde BoerMR, et al. Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol2007; 60(1): 34–42.
44.
WeirJP. Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J Strength Cond Res2005; 19(1): 231–240.
45.
de VetHCTerweeCBKnolDL, et al. When to use agreement versus reliability measures. J Clin Epidemiol2006; 59(10): 1033–1039.
46.
RanganathanPAggarwalR. Common pitfalls in statistical analysis: Understanding the properties of diagnostic tests–part 1. Perspect Clin Res2018; 9(1): 40–43.
47.
RanganathanPAggarwalR. Understanding the properties of diagnostic tests–part 2: Likelihood ratios. Perspect Clin Res2018; 9(2): 99–102.
48.
McGeeS. Simplifying likelihood ratios. J Gen Intern Med2002; 17(8): 646–649.
49.
HosmerDWJrLemeshowSSturdivantRX. Applied logistic regression. New York: John Wiley & Sons, 2013.
50.
MathiesonSMaherCGTerweeCB, et al. Neuropathic pain screening questionnaires have limited measurement properties. A systematic review. J Clin Epidemiol2015; 68(8): 957–966.
51.
FagbohunT. Systematic review on the psychometric, reliability and validity properties of translated neuropathic pain screening tools (DN4, LANSS and PDQ) 1 January 2005–19 July 2019. Int J Med Res2021; 7: 51–67.
52.
WongMLFlemingLRobayoLE, et al. Utility of the neuropathic pain symptom inventory in people with spinal cord injury. Spinal Cord2020; 58(1): 35–42.
53.
RogDJNurmikkoTJFriedeT, et al. Validation and reliability of the Neuropathic Pain Scale (NPS) in multiple sclerosis. Clin J Pain2007; 23(6): 473–481.
54.
OsborneTLRaichleKAJensenMP, et al. The reliability and validity of pain interference measures in persons with multiple sclerosis. J Pain Symptom Manage2006; 32(3): 217–229.
55.
RaichleKAOsborneTLJensenMP, et al. The reliability and validity of pain interference measures in persons with spinal cord injury. J Pain2006; 7(3): 179–186.
56.
SpalloneVMorgantiRD’AmatoC, et al. Validation of DN4 as a screening tool for neuropathic pain in painful diabetic polyneuropathy. Diabet Med2012; 29(5): 578–585.
57.
FinnerupNBHaroutounianSKamermanP, et al. Neuropathic pain: An updated grading system for research and clinical practice. Pain2016; 157(8): 1599–1606.
58.
HallströmHNorrbrinkC. Screening tools for neuropathic pain: Can they be of use in individuals with spinal cord injury?Pain2011; 152(4): 772–779.
59.
GudalaKGhaiBBansalD. Usefulness of four commonly used neuropathic pain screening questionnaires in patients with chronic low back pain: A cross-sectional study. Korean J Pain2017; 30(1): 51–58.
60.
AlkanHArdicFErdoganC, et al. Turkish version of the painDETECT questionnaire in the assessment of neuropathic pain: A validity and reliability study. Pain Med2013; 14(12): 1933–1943.
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