{"title":"Individualized Pain Treatment in Chronic Pancreatitis (INPAIN): An International, Multicenter, Investigator-initiated, Prospective, Cohort Study.","authors":"Rasmus Hagn-Meincke,Ana Dugic,Ankit Agarwal,Anna Evans Phillips,Anna Waage,Dhiraj Yadav,Divya Pillai,Elaina Vivian,Enrique de-Madaria,Imran Khan Niazi,Jeffrey Easler,Jens Brøndum Frøkjær,Julia McNabb-Baltar,Louise Kuhlmann Asferg,Mahya Faghih,Maria Belen Garay Montiel,Mathias Cook,Misbah Unnisa,Paul Tarnasky,Peter Hegyi,Pramod Garg,Rasmus Bach Nedergaard,Robert Edwards,Rupjyoti Talukdar,Shagufta Farheen,Søren Schou Olesen,Soumya Jagannath,Suzette Schmidt,Vikesh Singh,Zoltán Hajnády,Asbjørn Mohr Drewes,","doi":"10.1097/mpa.0000000000002388","DOIUrl":null,"url":null,"abstract":"INTRODUCTION\r\nPain is the foremost complication of chronic pancreatitis (CP), affecting about 70% of patients. However, the pathophysiological understanding and management of CP-related pain is 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.\r\n\r\nMETHOD\r\nThe INPAIN study is an international, multi-center, observational, longitudinal cohort study comprised of 4 sub-studies. The studies will prospectively enroll 400 CP patients (50 without pain and 350 with pain) and 50 control subjects, conducting biannual observations for four years. The test panel is comprised of comprehensive subjective and objective assessment parameters. Statistical analysis strategies differ across the sub-studies. 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.\r\n\r\nDISCUSSION\r\nThe 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.","PeriodicalId":19733,"journal":{"name":"Pancreas","volume":"81 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pancreas","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/mpa.0000000000002388","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
INTRODUCTION
Pain is the foremost complication of chronic pancreatitis (CP), affecting about 70% of patients. However, the pathophysiological understanding and management of CP-related pain is 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.
METHOD
The INPAIN study is an international, multi-center, observational, longitudinal cohort study comprised of 4 sub-studies. The studies will prospectively enroll 400 CP patients (50 without pain and 350 with pain) and 50 control subjects, conducting biannual observations for four years. The test panel is comprised of comprehensive subjective and objective assessment parameters. Statistical analysis strategies differ across the sub-studies. 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.
期刊介绍:
Pancreas provides a central forum for communication of original works involving both basic and clinical research on the exocrine and endocrine pancreas and their interrelationships and consequences in disease states. This multidisciplinary, international journal covers the whole spectrum of basic sciences, etiology, prevention, pathophysiology, diagnosis, and surgical and medical management of pancreatic diseases, including cancer.