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 长期饮酒。CP疼痛的管理是具有挑战性的，超过50%的CP疼痛患者 长期服用阿片类药物，助长了阿片类药物的流行。慢性阻塞性肺病合并导管梗阻可通过以下方法治疗 内窥镜治疗。然而，成功率各不相同，尽管技术上成功，但一些CP 患者继续经历疼痛。缺乏预测内窥镜疼痛治疗反应的工具 治疗给临床护理带来了挑战，可能会延误护理，导致更具侵入性的治疗 例如手术，或不必要地让患者接触阿片类药物。在这份提案中，我们打算使用机器 学习开发用于内窥镜治疗疼痛反应的多模式预测性生物特征 脑电(EEG)、定量感觉测试(QST)和生物心理社会变量。我们的研究 建立在这样的前提下，虽然胰腺病理最初会导致疼痛，但随着时间的推移，中枢 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 针对外围的疗法。EEG的神经成像，QST的感觉测试，以及心理社会 调查问卷评估疼痛处理过程中中枢与外周的变化。将这些工具组合成一个多模式 生物签名可提高预测的敏感性和特异度，促进S选择合适的 治疗慢性盆腔炎疼痛。在我们计划的UG3阶段，我们将测量EEG和QST并评估 100例酒精性慢性前列腺炎患者接受内窥镜治疗的心理社会因素分析 标准的临床护理。使用机器学习算法，提取特征并开发候选 疼痛治疗对内窥镜治疗反应的预测性生物标志。在UH3阶段，我们将验证 并在新的患者队列中选择曲线下面积最高的生物特征。我们的 成功将产生直接的临床影响，改善这种难治性慢性疼痛综合征的护理，并使 其他慢性腹痛综合征的类似研究。