Clinical indicators to identify neuropathic pain in low back-related leg pain: protocol for a modified Delphi study

Jai Mistry, Deborah Falla, Tim Noblet, Nicola R Heneghan, Alison B Rushton

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
130 Downloads (Pure)

Abstract

INTRODUCTION: Neuropathic low back-related leg pain (LBLP) can be a challenge to healthcare providers to diagnose and treat. Accurate diagnosis of neuropathic pain is fundamental to ensure appropriate intervention is given. However, to date there is no gold standard to diagnose neuropathic LBLP. A Delphi study will therefore be conducted to obtain an expert-derived consensus list of clinical indicators to identify a neuropathic component to LBLP.

METHODS/ANALYSIS: Included participants will be considered experts within the field as measured against a predefined eligibility criterion. Through an iterative multistage process, participants will rate their agreement with a list of clinical indicators and suggest any missing clinical indicators during each round. Agreement will be measured using a 5-point Likert scale. Descriptive statistics will be used to measure agreement; median, IQR and percentage of agreement. A priori consensus criteria will be defined for each round. Data analysis at the end of round three will enable a list of clinical indicators to be derived.

ETHICS AND DISSEMINATION: Ethical approval was gained from the University of Birmingham (ERN_19-1142). On completion of the study, findings will be disseminated in a peer-reviewed journal and presented at relevant conferences.

Original languageEnglish
Article numbere033547
Number of pages9
JournalBMJ open
Volume10
Issue number2
DOIs
Publication statusPublished - 17 Feb 2020

Bibliographical note

© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Keywords

  • Delphi
  • diagnosis
  • leg pain
  • neurological pain
  • neuropathic pain

ASJC Scopus subject areas

  • Medicine(all)

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