Publication Date

2-1-2023

Journal

Pain

DOI

10.1097/j.pain.0000000000002710

PMID

36149018

PMCID

PMC9726990

PubMedCentral® Posted Date

6-7-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Female, Humans, Quality of Life, Cross-Sectional Studies, Longitudinal Studies, Pancreatitis, Chronic, Abdominal Pain, Phenotype

Abstract

Pain is common in chronic pancreatitis (CP) and profoundly reduces quality of life (QoL). Multiple underlying mechanisms contribute to a heterogenous pain experience and reduce efficacy of pain management. This study was designed to characterize the distribution of mechanism-based pain phenotypes in painful CP. The data analyzed were collected as part of the PROspective Evaluation of Chronic Pancreatitis for EpidEmiologic and Translational StuDies, an NCI/NIDDK-funded longitudinal study of the natural history of CP. The PROspective Evaluation of Chronic pancreatitis for EpidEmiologic and translational stuDies includes patient-reported outcome (PRO) measures of pain, medication use, global health, and QoL. Of subjects (N = 681) with CP, 80% experienced abdominal pain within the year before enrollment. Subjects who experienced pain in the week before enrollment (N = 391) completed PROMIS Neuropathic and Nociceptive Pain Quality instruments which were then used to classify them by pain type: 40% had nociceptive, 5% had neuropathic-like, and 32% had both types of pain. The prevalence of having both types of pain was higher among women and subjects with diabetes mellitus, whereas nociceptive-only pain was more prevalent among men and those with pancreatic duct stricture. Other factors, including pain medication use and healthcare utilization, did not differ between groups based on pain type. Subjects in the Both group had significantly worse health and QoL scores relative to those with nociceptive-only pain, suggesting that using psychosocial pain surveys may be useful for understanding pain subtypes in patients with CP. Additional research is needed to identify biochemical and biophysical signatures that may associate with and predict responses to mechanism-specific interventions.

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