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，最常见的原因是 长期酗酒CP疼痛的管理具有挑战性，超过50%的CP疼痛患者 长期服用阿片类药物，导致阿片类药物流行。CP伴导管阻塞可通过以下方法治疗： 内镜治疗然而，成功率各不相同，尽管技术上成功的程序，一些CP 患者仍然会感到疼痛。缺乏预测内镜下疼痛治疗反应的工具 治疗对临床护理提出了挑战，可能会延迟护理，导致更具侵入性的治疗 例如手术或不必要地将患者暴露于阿片类药物。在这个建议中，我们的目标是使用机器 学习开发多模式预测生物特征，用于对内镜治疗的疼痛反应， 脑电图（EEG）、定量感觉测试（QST）和生物心理社会变量。我们的研究 是建立在这样的前提下，虽然胰腺病理最初驱动疼痛，随着时间的推移，改变中央 疼痛处理可能成为一些CP患者疼痛的主要驱动因素，使他们对药物产生抵抗。 针对外围的治疗。EEG神经成像，QST感觉测试，以及心理社会 问卷调查评估疼痛处理的中枢与外周变化。将这些工具组合到多模式中 生物特征可以提高预测的敏感性和特异性， CP疼痛的治疗在我们提案的UG3阶段，我们将测量EEG和QST，并评估 接受内窥镜治疗的约100例酒精性CP疼痛患者的心理社会因素， 标准临床护理。使用机器学习算法，我们将提取特征并开发候选 对内镜治疗的疼痛治疗反应的预测性生物特征。在UH3阶段，我们将进行验证 并在新的患者组群中选择具有最高曲线下面积的生物特征。我们 成功将有直接的临床影响，改善这种难治性慢性疼痛综合征的护理， 其他慢性腹痛综合征的类似研究。