Bringing medical advances from the lab to the clinic.
急性胰腺炎：通常表现为突发的严重腹部疼痛，疼痛可能放射到背部。伴有恶心、呕吐、发热和心跳加速。急性发作可以由多种原因引起，如长期酗酒、胆石阻塞等。 慢性胰腺炎：特征为胰腺长期炎症，导致胰腺功能逐渐丧失，常表现为反复的腹痛、消化不良和体重下降。慢性胰腺炎可能导致胰腺组织硬化和瘢痕形成。
尽管近年来在胰腺炎的诊断和治疗方面取得了显著进展，但仍存在一些重要而未解决的临床问题：
早期诊断：尽管有进步，早期诊断胰腺炎仍具挑战性，特别是在症状不典型的情况下。提高早期诊断能力可以减少并发症和改善预后。 疼痛管理：胰腺炎的疼痛管理仍是一个重要问题，尤其是在慢性胰腺炎患者中，有效的疼痛控制方案有限。 治疗策略：当前治疗主要依赖对症治疗，缺乏针对性的治疗方法来逆转或停止疾病进程。研发新的药物和治疗方法以更有效地治疗胰腺炎是迫切需要的。 长期并发症：如何管理和预防胰腺炎患者的长期并发症，包括糖尿病和消化系统问题，仍是临床上的一个难题。
胰腺炎的研究和治疗领域仍需更多的创新和突破，以改善患者的临床管理和生活质量。
PENNSYLVANIA STATE UNIV HERSHEY MED CTR 的 CHINCHILLI, VERNON M
UNIVERSITY OF TX MD ANDERSON CAN CTR 的 YUAN, YING
NEW YORK UNIVERSITY SCHOOL OF MEDICINE 的 DOAN, LISA
INDIANA UNIVERSITY INDIANAPOLIS 的 FOGEL, EVAN L
MAYO CLINIC ARIZONA 的 SINGH, VIJAY PREM
宾夕法尼亚州立大学赫尔希医学中心
德克萨斯大学 MD 安德森癌症中心
加州大学洛杉矶分校
梅奥诊所杰克逊维尔
俄亥俄州立大学等
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.
The Type 1 Diabetes and Acute Pancreatitis Consortium (T1DAPC): The overall goals of the T1DAPC are to describe the risk for and mechanisms of diabetes mellitus occurring after one or more episodes of acute pancreatitis. Secondary objectives include defining the natural history of acute pancreatitis, the clinical risk factors that predict development of diabetes and pre-diabetes, the natural history of beta cell function after acute pancreatitis, and the potential contribution of an altered immune state to the increased risk for diabetes after acute pancreatitis. These objectives are being addressed the ongoing, prospective, observational T1DAPC study entitled “Diabetes RElated to Acute Pancreatitis and its Mechanisms (DREAM)”.
DREAM-ON: This ancillary study pilot proposal, entitled “Diabetes RElated to Acute Pancreatitis and its Mechanisms: Metabolic Outcomes Using Novel CGM Metrics (DREAM-ON)” which will be conducted in participants enrolled in the DREAM study, will test whether continuous glucose monitoring (CGM) is clinically useful to predict risk for developing diabetes, the need for insulin therapy among those who develop diabetes and whether CGM can provide insight into the diabetes subtype among patients who have experienced an episode of acute pancreatitis. Thus, the results of this ancillary study will complement and extend the primary research questions in the DREAM study.
Specific Aims: Aim 1: To perform CGM in all participants in the DREAM study in conjunction with their scheduled visits at months 3, 12, 24 and subsequent annual visits and to determine if CGM metrics predict incident pre-diabetes and DM. Aim 2: To determine if CGM metrics predict need for insulin therapy in patients who develop DM after AP. Aim 3: To determine whether CGM metrics correlate with measures of insulin secretion and insulin resistance derived from the OGTT, MMT and FSIVGTT, and can be used as a surrogate to predict diabetes subtype.
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