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: 10438888
• 负责人: Anna Evans Phillips
• 金额: $ 56.89万
• 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/10438888
• 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; 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; psychiatric comorbidity; 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，其影响 大约90%的患者在他们的病程中对生活质量产生了毁灭性的影响。 脑性瘫痪疼痛的病因是多因素的。有证据表明胰管结石造成梗阻的患者 和/或狭窄被提供侵入性治疗，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，目前还没有可用的临床工具来确定 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 到脊髓上中枢敏化(SCS)，一种神经病理性和神经可塑性重塑的现象 来自持续的疼痛刺激。定量感觉测试(QST)，一种标准化的研究技术 测试伤害性感觉的刺激(编码伤害性刺激及其下游的神经信号 疼痛体验)，用于其他疼痛情况，以区分患者亚组以指导 治疗。QST有可能改变疼痛CP患者的管理算法。我们的 初步数据显示，胰腺QST(P-QST)可将CP患者分为三组：正常组 疼痛处理、节段性(胰腺的T10皮肤刀)敏感化和广泛性痛觉过敏 (与SCS一致)。在这项建议中，我们将评估P-QST预测侵袭性反应的能力 治疗疼痛的CP，并开发个体化预测治疗的预测模型 回应。我们的具体目标是：目的1.检验治疗前P-QST表型对脑出血的预测能力 疼痛CP的侵入性治疗后疼痛改善。使用术前P-QST，我们将进行表型 在UPMC和Johns Hopkins接受临床指征的侵入性治疗疼痛CP的150例患者 大学。我们的主要结果将是6个月后用数字评级表测量的平均疼痛评分 干预后。目标2.将P-QST与已知和可疑的患者、疾病和治疗- 相关因素建立个体化预测侵入性治疗反应的模型。使用机器 学习工具，我们将开发一个模型，优化对入侵反应概率的预测 针对个别患者的治疗。这也将决定P-QST作为总体预测指标的相对强度 治疗反应。目标3.使用生化炎症标志物增强预测模型(目标2 评估在侵入性治疗后提高疼痛改善预测能力的潜力 疼痛的CP。在Aim 2中开发的预测模型将通过纳入血清而得到进一步加强 基线时的神经炎性标志物。我们的发现将是朝着发展 侵入性治疗痛性脑瘫后治疗反应的个体化预测。他们将铺设 为充分确定P-QST在局部侵入性和其他治疗中的作用的多中心研究奠定基础 痛性脑瘫的疼痛管理。