Rating Scales for Pain in Parkinson's Disease: Critique and Recommendations


Background: We aimed at critically appraising the clinimetric properties of existing pain scales or questionnaires and to give recommendations for their use in Parkinson's disease (PD). Methods: Clinimetric properties of pain scales used in PD were systematically evaluated. A scale was classified as ‘recommended’ if was used in PD, showed adequate clinimetric properties, and had been used by investigators other than the original developers; as ‘suggested’ if it was used in PD and fulfilled only one other criterion; and as ‘listed’ if it was used in PD but did not meet the other criteria. Only scales rating pain intensity or for syndromic classification were assessed. Results: Eleven of the 34 scales initially considered fulfilled inclusion criteria. Among the scales rating pain intensity, the “Brief Pain Inventory short form,” “McGill Pain Questionnaire short and long forms,” “Neuropathic Pain Symptoms Inventory,” “11-point Numeric Rating Scale,” “10-cm Visual Analog Scale,” and “Pain-O-Meter” were “recommended with caution” because of lack of clinimetric data in PD, whereas the “King's PD Pain Scale” was “recommended.” Among scales for pain syndromic classification, the “DN4” was “recommended with caution” because of lack of clinimetric data in PD; the “Leeds Assessment of Neuropathic Symptoms and Signs,” “Pain-DETECT,” and the “King's PD Pain Scale” were “suggested.”. Conclusions: King's PD pain scale can be recommended for the assessment of pain intensity in PD. Syndromic classification of pain in PD may be achieved by the DN4, but clinimetric data in PD are needed for this scale.






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Publication Info

Perez-Lloret, Santiago, Daniel Ciampi de Andrade, Kelly E Lyons, Carmen Rodríguez-Blázquez, Kallol Ray Chaudhuri, Guenther Deuschl, Girgio Cruccu, Cristina Sampaio, et al. (2016). Rating Scales for Pain in Parkinson's Disease: Critique and Recommendations. Movement Disorders Clinical Practice, 3(6). pp. 527–537. 10.1002/mdc3.12384 Retrieved from https://hdl.handle.net/10161/26139.

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Sheng Luo

Professor of Biostatistics & Bioinformatics

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