Predictive Biosignature for Endoscopic Therapy for Chronic Pancreatitis Pain

慢性胰腺炎疼痛内镜治疗的预测生物特征

• 批准号: 10794609
• 负责人: Zhe Sage Chen
• 金额: $ 124.48万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10794609
• 关键词: Adverse event; Alcohols; Area; Area Under Curve; Brief Pain Inventory; Caring; Chronic; Clinical; Collection; Data Collection; Development; Duct (organ) structure; Effectiveness; Electroencephalography; Engineering; Enrollment; Etiology; Failure; Goals; Impairment; Individual; Inflammation; Infrastructure; Intervention; Lead; Liquid substance; Machine Learning; Measures; Medicine; Methodology; Modality; Modeling; Moods; Multicenter Studies; Obstruction; Operative Surgical Procedures; Opioid; Pain; Pain Measurement; Pain Research; Pain management; Pancreas; Pancreatic Diseases; Pancreatic duct; Pancreatitis; Pathology; Patients; Peripheral; Phase; Phenotype; Prediction of Response to Therapy; Procedures; Process; Psychosocial Assessment and Care; Psychosocial Factor; Quality of life; Questionnaires; ROC Curve; Refractory; Research; Resistance; Rest; Risk; Running; Sampling; Schedule; Sensitivity and Specificity; Sensory; Specificity; Stents; Stimulus; Structural defect; Symptoms; Syndrome; Testing; Therapeutic; Therapeutic Intervention; Time; Treatment outcome; Validation; alcohol comorbidity; alcohol use disorder; biomarker development; biomarker selection; biopsychosocial factor; biosignature; calcification; candidate marker; central pain; chronic abdominal pain; chronic alcohol ingestion; chronic pain; chronic pancreatitis; clinical care; clinical decision-making; clinical pain; cohort; data standards; density; experience; feature extraction; improved; machine learning algorithm; multimodality; neuroimaging; non-opioid analgesic; opioid epidemic; opioid therapy; opioid use; pain processing; pain reduction; peripheral pain; personalized medicine; pharmacologic; predicting response; predictive marker; predictive tools; psychosocial; response; source localization; success; therapy resistant; tool; treatment response

Pain occurs in more than 80% of patients with chronic pancreatitis (CP) , which is most commonly caused by chronic alcohol use. Management of CP pain is challenging, and greater than 50% of patients with CP pain are placed on chronic opioids, contributing to the opioid epidemic. CP with ductal obstruction can be treated by endoscopic therapies. However, success rates vary, and despite technically successful procedures, some CP patients continue to experience pain. The lack of tools for predicting pain treatment response to endoscopic therapies presents challenges for clinical care, potentially delaying care, leading to more invasive therapies such as surgery, or unnecessarily exposing patients to opioids. In this proposal we aim to use machine learning to develop multimodal predictive biosignatures for pain response to endoscopic therapies utilizing electroencephalography (EEG), quantitative sensory testing (QST), and biopsychosocial variables. Our study is founded on the premise that while pancreatic pathology initially drives pain, over time alterations in central pain processing may become a dominant driver of pain in some patients with CP, making them resistant to therapies aimed at the periphery. Neuroimaging with EEG, sensory testing with QST, and psychosocial questionnaires assess central vs peripheral changes in pain processing. Combining these tools in a multimodal biosignature can improve sensitivity and specificity of prediction and advance s election of appropriate treatment of CP pain. In the UG3 phase of our proposal, we will measure EEG and QST and assess psychosocial factors in ~100 patients with alcohol-induced CP pain undergoing endoscopic therapy as part of standard clinical care. Using machine learning algorithms, we will extract features and develop candidate predictive biosignatures for pain treatment response to endoscopic therapy. In the UH3 phase we will validate and select the biosignature with the highest area under the curve met ric in a new cohort of patients. Our success will have direct clinical impact, improving care of this refractory chronic pain syndrome and enabling similar studies in other chronic abdominal pain syndromes.

超过80%的慢性胰腺炎（CP）患者会出现疼痛，这是最常见的原因