painPREDICT: first interim data from the development of a new patient-reported pain questionnaire to predict treatment response using sensory symptom profiles

Objective: Sensory symptom patterns may be useful for predicting treatment response, and, thus, improve individual therapy in patients suffering from neuropathic pain (NeP). Existing screening questionnaires focus predominately on neuropathic mechanisms without consideration of nociceptive mechanisms or mixed pain states. This study aimed to develop a new questionnaire, painPREDICT, using a wide set of patient-reported descriptors potentially associated with neuropathic and nociceptive pain mechanisms, and to explore sensory symptom patterns.Methods: PainPREDICT was constructed based on exploratory (n=27 patients) and cognitive debriefing interviews (n=49 patients and nine physicians), across five NeP conditions. The pilot questionnaire was then administered in a non-interventional, cross-sectional, multi-center study to 840 pain patients across the US and Germany. The identification of a sensory symptom pattern was based on hybrid clustering resulting from items standardization followed by principal component analysis.Results: The final questionnaire included 20 items covering: pain intensity, location of pain, course of pain, and sensory symptoms. Most patients participating in the cross-sectional study suffered either from painful diabetic polyneuropathy (n=330) or radiculopathy (n=349), fewer from central pain (n=61) or other types of NeP (n=100). The hybrid clustering of the new questionnaire data identified three different characteristic sensory symptom profiles in patients with NeP: Irritable nociceptors, deafferentation pain, and pain attacks with nociceptive component. Although some differences in the distribution of the sensory profiles were found, all profiles were represented in all NeP etiology groups.Conclusions: This study set the ground of painPREDICT and showed promising results for its use to categorize patients according to sensory symptom patterns.