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
基本信息
- 批准号:10794609
- 负责人:
- 金额:$ 124.48万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:Adverse eventAlcoholsAreaArea Under CurveBrief Pain InventoryCaringChronicClinicalCollectionData CollectionDevelopmentDuct (organ) structureEffectivenessElectroencephalographyEngineeringEnrollmentEtiologyFailureGoalsImpairmentIndividualInflammationInfrastructureInterventionLeadLiquid substanceMachine LearningMeasuresMedicineMethodologyModalityModelingMoodsMulticenter StudiesObstructionOperative Surgical ProceduresOpioidPainPain MeasurementPain ResearchPain managementPancreasPancreatic DiseasesPancreatic ductPancreatitisPathologyPatientsPeripheralPhasePhenotypePrediction of Response to TherapyProceduresProcessPsychosocial Assessment and CarePsychosocial FactorQuality of lifeQuestionnairesROC CurveRefractoryResearchResistanceRestRiskRunningSamplingScheduleSensitivity and SpecificitySensorySpecificityStentsStimulusStructural defectSymptomsSyndromeTestingTherapeuticTherapeutic InterventionTimeTreatment outcomeValidationalcohol comorbidityalcohol use disorderbiomarker developmentbiomarker selectionbiopsychosocial factorbiosignaturecalcificationcandidate markercentral painchronic abdominal painchronic alcohol ingestionchronic painchronic pancreatitisclinical careclinical decision-makingclinical paincohortdata standardsdensityexperiencefeature extractionimprovedmachine learning algorithmmultimodalityneuroimagingnon-opioid analgesicopioid epidemicopioid therapyopioid usepain processingpain reductionperipheral painpersonalized medicinepharmacologicpredicting responsepredictive markerpredictive toolspsychosocialresponsesource localizationsuccesstherapy resistanttooltreatment response
项目摘要
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 疼痛的治疗具有挑战性,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。伴有导管阻塞的 CP 可以通过以下方法治疗:
内窥镜治疗。然而,成功率各不相同,尽管手术在技术上是成功的,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,可能会延迟护理,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。在这个提案中,我们的目标是使用机器
学习开发内窥镜治疗疼痛反应的多模式预测生物特征
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
建立的前提是,虽然胰腺病理学最初会引起疼痛,但随着时间的推移,中枢神经系统的变化
疼痛处理可能成为某些脑瘫患者疼痛的主要驱动因素,使他们对疼痛产生抵抗力
针对外围的治疗。脑电图神经影像、QST 感官测试和社会心理测试
问卷评估疼痛处理的中枢与外周变化。将这些工具组合成多模式
生物印记可以提高预测的敏感性和特异性,并提前选择合适的
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
大约 100 名酒精引起的 CP 疼痛患者接受内镜治疗作为治疗的一部分,其中的心理社会因素
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者
内窥镜治疗疼痛治疗反应的预测生物特征。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印记。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。
项目成果
期刊论文数量(0)
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{{ truncateString('Zhe Sage Chen', 18)}}的其他基金
Cortical information integration as a model for pain perception and behavior
皮质信息整合作为疼痛感知和行为的模型
- 批准号:
10205303 - 财政年份:2021
- 资助金额:
$ 124.48万 - 项目类别:
CRNS: An Integrative Study of Hippocampal-Neocortical Memory Coding during Sleep
CRNS:睡眠期间海马-新皮质记忆编码的综合研究
- 批准号:
10401807 - 财政年份:2018
- 资助金额:
$ 124.48万 - 项目类别:
CRNS: An Integrative Study of Hippocampal-Neocortical Memory Coding during Sleep
CRNS:睡眠期间海马-新皮质记忆编码的综合研究
- 批准号:
9920779 - 财政年份:2018
- 资助金额:
$ 124.48万 - 项目类别:
CRCN: Dissecting Neural Circuits for Acute Pain
CRCN:剖析急性疼痛的神经回路
- 批准号:
9313960 - 财政年份:2016
- 资助金额:
$ 124.48万 - 项目类别:
CRCN: Dissecting Neural Circuits for Acute Pain
CRCN:剖析急性疼痛的神经回路
- 批准号:
9242180 - 财政年份:2016
- 资助金额:
$ 124.48万 - 项目类别:
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