Objectives: Pain is the foremost complication of chronic pancreatitis (CP), affecting about 70% of patients. However, the pathophysiological understanding and management of CP-related pain are complex, likely as patients have diverse "pain phenotypes" responding differently to treatment. This study aims to develop a bedside test panel to identify distinct pain phenotypes, investigate the temporal evolution, and determine whether they can be used to predict treatment response.
Methods: The INPAIN study is an international, multicenter, observational, longitudinal cohort study consisted of 4 substudies. The studies will prospectively enroll 400 CP patients (50 without pain and 350 with pain) and 50 control subjects, conducting biannual observations for 4 years. The test panel is consisted of comprehensive subjective and objective assessment parameters. Statistical analysis strategies differ across the substudies. A model to predict treatment efficacy will be developed using various machine learning techniques, including an artificial intelligence approach, with internal cross-validation. Trajectories in pain parameters will be characterized by graphical analysis and mixed effect models.
Discussion: The INPAIN study aims to comprehensively understand pain in CP through a test panel developed for routine clinical use. This tool has the potential to personalize treatments, improve clinical practice, enhance patient care, improve quality of life, and minimize treatment side effects.
Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.