P-QST Project: Pancreatic Quantitative Sensory Testing (P-QST) to Predict Treatment Response for Pain in Chronic Pancreatitis

P-QST 项目：胰腺定量感觉测试 (P-QST) 预测慢性胰腺炎疼痛的治疗反应

• 批准号: 10297329
• 负责人: Anna Evans Phillips
• 金额: $ 58.04万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10297329
• 关键词: Abdominal Pain; Adopted; Affect; Algorithms; Analgesics; Automobile Driving; Biochemical; Biological Markers; Clinical; Clinical Trials; Data; Denmark; Development; Disease; Drainage procedure; Duct (organ) structure; Enrollment; Enzyme-Linked Immunosorbent Assay; Etiology; Fibrosis; Foundations; Goals; Health; Health Care Costs; Health Expenditures; Hyperalgesia; Individual; Inflammatory; Investigative Techniques; Lasso; Lead; Linear Regressions; Machine Learning; Measures; Medical; Modeling; Morbidity - disease rate; Multicenter Studies; Nerve; Neuronal Plasticity; Neurons; Neuropathy; Nociception; Nomograms; Numeric Rating Scale; Obstruction; Operative Surgical Procedures; Outcome; Pain; Pain Measurement; Pain management; Pancreas; Pancreatic Diseases; Pancreatic duct; Patient Outcomes Assessments; Patients; Peripheral Nervous System Diseases; Persistent pain; Persons; Phenotype; Prediction of Response to Therapy; Prevalence; Probability; Procedures; Proteins; Protocols documentation; Quality of life; Reporting; Risk; Role; Sampling; Sensory; Serum; Severities; Spinal; Standardization; Stimulus; Suggestion; Techniques; Testing; Treatment outcome; Universities; Up-Regulation; Urine; Visceral; Wages; central pain; central sensitization; chemokine; chronic pancreatitis; comorbidity; cost; demographics; dermatome; disability; experience; follow-up; health care service utilization; high risk; impression; improved; individual patient; inflammatory marker; neuroinflammation; neurosensory; neurotransmission; nociceptive response; novel; opioid therapy; opioid use; pain processing; pain reduction; pain relief; pain score; patient response; patient subsets; personalized approach; personalized predictions; post intervention; predicting response; predictive modeling; predictive test; primary outcome; productivity loss; response; secondary outcome; side effect; success; symptom management; tool; treatment strategy

ABSTRACT: Abdominal pain is the primary driver of morbidity in chronic pancreatitis (CP) and affects approximately 90% of patients over the course of their disease with devastating effects on quality of life. Etiology of pain in CP is multi-factorial. Patients with evidence of pancreatic duct obstruction due to stones and/or strictures are offered invasive treatments such as endotherapy or surgical drainage to relieve pain. However, response to invasive treatments is unpredictable, and currently no clinical tool is available to identify patients who will respond to technically successful treatment. The lack of pain response is at least partially due to supraspinal central sensitization (SCS), a phenomenon of neuropathic and neuroplastic remodeling resulting from persistent pain stimuli. Quantitative Sensory Testing (QST), an investigative technique of standardized stimulations to test nociception (the neural signaling that encodes noxious stimuli and the downstream experience of pain), is used in other pain conditions to differentiate between patient subgroups to guide treatment. QST has the potential to change the management algorithm of patients with painful CP. Our preliminary data show that pancreatic QST (P-QST) can phenotype patients with CP into three groups: normal pain processing, segmental (T10 dermatome at the pancreas) sensitization, and widespread hyperalgesia (consistent with SCS). In this proposal, we will evaluate the ability of P-QST to predict response to invasive treatment for painful CP, and to develop a predictive model for individualized prediction of treatment response. Our specific aims are: Aim 1. Test the predictive capability of pre-treatment P-QST phenotype for pain improvement following invasive treatment for painful CP. Using pre-procedure P-QST, we will phenotype 150 patients undergoing clinically-indicated invasive treatment for painful CP at UPMC and Johns Hopkins University. Our primary outcome will be average pain score measured by Numeric Rating Scale at 6 months post-intervention. Aim 2. Incorporate P-QST with known and suspected patient, disease, and treatment- related factors to create a model for individualized prediction of response to invasive treatment. Using machine learning tools, we will develop a model that optimizes the prediction of probability of response to invasive treatment in individual patients. This will also determine the relative strength of P-QST as an overall predictor of treatment response. Aim 3. Augment the predictive model (Aim 2) with biochemical inflammatory markers to assess the potential to increase predictive capability for pain improvement following invasive treatment for painful CP. The predictive model developed in aim 2 will be further strengthened by incorporating serum neuroinflammatory markers at baseline. Our findings will be a major step toward development of individualized prediction of treatment response following invasive treatment for painful CP. They will lay the foundation for multicenter studies to fully define the role of P-QST in locally invasive and other treatments for pain management for painful CP.

摘要：腹痛是慢性胰腺炎（CP）发病率的主要驱动因素， 大约90%的患者在疾病过程中会对生活质量产生毁灭性影响。 CP疼痛的病因是多因素的。有结石导致胰管梗阻证据的患者 和/或狭窄提供侵入性治疗，例如内治疗或外科引流以减轻疼痛。 然而，对侵入性治疗的反应是不可预测的，目前没有临床工具可用于识别 对技术上成功的治疗有反应的患者。疼痛反应的缺乏至少部分是由于 脊髓上中枢敏化（SCS），一种神经病理性和神经重塑的现象， 持续的疼痛刺激。定量感官测试（QST），一种标准化的调查技术， 刺激以测试伤害感受（编码伤害性刺激的神经信号传导和下游刺激）。 疼痛经验），用于其他疼痛状况，以区分患者亚组， 治疗QST有可能改变疼痛性CP患者的管理算法。我们 初步数据显示，胰腺QST（P-QST）可将CP患者分为三组：正常组、正常对照组和正常对照组 疼痛处理、节段性（胰腺T10皮节）致敏和广泛痛觉过敏 （与SCS一致）。在这项建议中，我们将评估P-QST预测对侵入性治疗反应的能力。 治疗疼痛性CP，并开发一个预测模型，用于个性化预测治疗 反应我们的具体目标是：目标1。检测治疗前P-QST表型对以下指标的预测能力： 疼痛性CP的侵入性治疗后疼痛改善。使用程序前P-QST，我们将表型 在UPMC和约翰霍普金斯接受临床指征侵入性治疗疼痛性CP的150例患者 大学我们的主要结果将是6个月时通过数字评定量表测量的平均疼痛评分 干预后。目标二。将P-QST与已知和疑似患者、疾病和治疗合并- 相关因素，以创建模型，用于个性化预测对侵入性治疗的反应。使用机器 学习工具，我们将开发一个模型，优化预测的概率响应侵入性 个别患者的治疗。这也将决定P-QST作为整体预测因子的相对强度 治疗反应。目标3.用生化炎症标志物增强预测模型（目标2） 评估增加侵入性治疗后疼痛改善的预测能力的潜力， 疼痛性CP在aim 2中开发的预测模型将通过纳入血清 神经炎性标志物。我们的发现将是朝着发展的重要一步。 疼痛性CP侵入性治疗后的治疗反应的个体化预测。他们将把 多中心研究的基础，以充分确定P-QST在局部侵入性和其他治疗中的作用， 疼痛性CP的疼痛管理。