Characterizing individual painDETECT symptoms by average pain severity
Authors Sadosky A, Koduru V, Bienen EJ, Cappelleri J
Received 30 January 2016
Accepted for publication 31 March 2016
Published 26 July 2016 Volume 2016:8 Pages 361—366
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Giorgio Lorenzo Colombo
Alesia Sadosky,1 Vijaya Koduru,2 E Jay Bienen,3 Joseph C Cappelleri4
1Pfizer Inc, New York, NY, 2Eliassen Group, New London, CT, 3Outcomes Research Consultant, New York, NY, 4Pfizer Inc, Groton, CT, USA
Background: painDETECT is a screening measure for neuropathic pain. The nine-item version consists of seven sensory items (burning, tingling/prickling, light touching, sudden pain attacks/electric shock-type pain, cold/heat, numbness, and slight pressure), a pain course pattern item, and a pain radiation item. The seven-item version consists only of the sensory items. Total scores of both versions discriminate average pain-severity levels (mild, moderate, and severe), but their ability to discriminate individual item severity has not been evaluated.
Methods: Data were from a cross-sectional, observational study of six neuropathic pain conditions (N=624). Average pain severity was evaluated using the Brief Pain Inventory-Short Form, with severity levels defined using established cut points for distinguishing mild, moderate, and severe pain. The Wilcoxon rank sum test was followed by ridit analysis to represent the probability that a randomly selected subject from one average pain-severity level had a more favorable outcome on the specific painDETECT item relative to a randomly selected subject from a comparator severity level.
Results: A probability >50% for a better outcome (less severe pain) was significantly observed for each pain symptom item. The lowest probability was 56.3% (on numbness for mild vs moderate pain) and highest probability was 76.4% (on cold/heat for mild vs severe pain). The pain radiation item was significant (P<0.05) and consistent with pain symptoms, as well as with total scores for both painDETECT versions; only the pain course item did not differ.
Conclusion: painDETECT differentiates severity such that the ability to discriminate average pain also distinguishes individual pain item severity in an interpretable manner. Pain-severity levels can serve as proxies to determine treatment effects, thus indicating probabilities for more favorable outcomes on pain symptoms.
Keywords: neuropathic pain, painDETECT, pain severity, psychometric properties
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