Development of Patient-Tailored Adaptive Treatment Strategies for Acute Severe Ulcerative Colitis

制定针对急性重症溃疡性结肠炎的患者定制适应性治疗策略

• 批准号: 10569397
• 负责人: Jeffrey Berinstein
• 金额: $ 19.39万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-14 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10569397
• 关键词: 18 year old; Accounting; Achievement; Acute; Address; Admission activity; Adrenal Cortex Hormones; Applications Grants; Applied Research; Area; Award; Behavioral; C-reactive protein; Calcineurin inhibitor; Caring; Cessation of life; Characteristics; Clinical; Clinical Trials Design; Colectomy; Crohn' s disease; Cyclosporine; Data; Data Analyses; Data Analytics; Deterioration; Development; Discrimination; Disease; Early identification; Ensure; Feces; Frequencies; Future; General Hospitals; Goals; Growth; Health; Heterogeneity; Hospitalization; Individual; Inflammatory Bowel Diseases; Institution; Intervention; Intravenous; Janus kinase; Knowledge; Laboratories; Life; Link; Machine Learning; Malignant - descriptor; Massachusetts; Mentorship; Methods; Michigan; Modeling; Morbidity - disease rate; Participant; Pathway interactions; Patient Admission; Patient Selection; Patients; Pharmacological Treatment; Physicians; Positioning Attribute; Prediction of Response to Therapy; Preparation; Probability; Randomized; Refractory; Research; Research Personnel; Research Proposals; Risk; Scientist; Sequential Multiple Assignment Randomized Trial; Sequential Treatment; Specific qualifier value; Steroids; Symptoms; TNF gene; Testing; Time; Training; Treatment Efficacy; Treatment Failure; Ulcerative Colitis; Universities; Work; acceptability and feasibility; analytical method; brief intervention; career; cohort; cost; effective therapy; efficacy evaluation; evidence base; improved; individual patient; individual response; infliximab; inhibitor; kinase inhibitor; machine learning method; machine learning model; mortality; multidimensional data; novel strategies; personalized care; personalized medicine; personalized strategies; pharmacologic; predictive modeling; prognostic; prospective; public health relevance; symposium; treatment response; treatment strategy; trial design

Modified Project Summary/Abstract Section This K23 application proposes a focused training and research plan to support the applicant’s career growth into an independent physician-scientist. The proposed plan will ensure the applicant develops expertise in patient-tailored adaptive treatment strategies (ATS) that deliver personalized care to patients with inflammatory bowel diseases, including Crohn’s disease and ulcerative colitis (UC). CONTEXT: Acute severe ulcerative colitis (ASUC) is a life-threatening presentation of UC requiring prompt treatment. Thirty percent of patients with UC will require hospitalization for ASUC during their lifetime. First-line therapy for ASUC, intravenous (IV) corticosteroids, has remained unchanged since the 1950s, despite evidence that up to 30% of patients fail treatment and require emergent colectomy. Cyclosporine and infliximab rescue therapies are effective in treating patients who fail to respond to IV corticosteroids, yet up to 60% of steroid-refractory patients will fail to respond to these rescue therapies as well. A combination of factors, including substantial patient heterogeneity in treatment response and dynamic day-to-day changes in clinical course, have limited our ability to develop effective personalized treatment pathways. There is a critical need to improve the current one-size-fits-all approach to provide more efficacious and safer treatment for ASUC. In this proposal, we hypothesize that ATS, which use prespecified sequential treatment decisions to guide stepwise management changes according to well-defined baseline and longitudinal clinical and laboratory thresholds (tailoring variables) specific to a patient, will reduce the rate of colectomy by guiding clinicians on the type, intensity, and timing of therapy that is most likely to be effective at each stage of treatment. RESEARCH STRATEGY: The objectives of this proposal are to: identify baseline patient characteristics predictive of ASUC treatment response (Aim 1), develop a time-specific risk model to identify pharmacologic treatment failure using tailoring variables to guide treatment decisions (Aim 2), and develop and optimize ATS personalized to patients’ evolving health needs throughout their ASUC hospitalization. We will evaluate the feasibility and acceptability of our ATS using a pilot sequential multiple assignment randomized trial (SMART) (Aim 3). SMARTs, a trial design that sequentially randomizes participants multiple times to one of several interventions at selected time points, have been used successfully to develop, optimize, and test ATS. TRAINING PLAN: To facilitate achievement of the applicant’s long-term career goal – to become an independent physician-scientist and expert in ATS development and optimization – this award will allow the applicant to develop a deeper understanding of the components, principles for optimization, and trial designs that inform the construction of personalized ATS. This will be aided by a deeper understanding of predictive modeling and longitudinal data analysis using machine learning. These objectives will be accomplished through close mentorship from experts in these areas, selected didactic activities, conference attendance, applied research, and grant proposal preparation.

修改的项目摘要/摘要部分 该K23申请提出了一项重点培训和研究计划，以支持申请人的职业发展成为独立的身体科学家。拟议的计划将确保患者规定的自适应治疗策略（ATS）的应用程序开发专家（ATS）为包括克罗恩病和溃疡性结肠炎（UC）在内的炎症性肠病患者提供个性化护理。上下文：急性严重溃疡性结肠炎（ASUC）是对需要及时治疗的UC的威胁生命的表现。 UC患者中有30％需要住院治疗ASUC。自1950年代以来，对ASUC，静脉（IV）皮质类固醇的一线治疗一直保持不变，这表明高达30％的患者失败治疗，需要新兴的收集术。环孢素和英夫利昔单抗救援疗法可有效治疗未反应静脉皮质类固醇的患者，但是高达60％的立体情节患者也无法对这些救援疗法做出反应。各种因素的结合，包括在治疗反应中的大量患者异质性和临床过程中动态的日常变化，限制了我们开发有效的个性化治疗途径的能力。迫切需要改善当前的一定大小的方法 - 为ASUC提供更有效和安全的处理。在这项提案中，我们假设使用预测的顺序治疗决策ATS来指导逐步管理根据定义明确的基线和纵向临床和实验室阈值（裁缝变量）的变化，将通过指导临床医生对治疗的类型，强度和治疗时间来降低收集率。研究策略：该提案的目标是：确定可预测ASUC治疗反应的基线患者特征（AIM 1），开发特定时间的风险模型，以使用裁缝变量来指导治疗决策（AIM 2），并开发和优化ATS，并在其ASUC住院期间不断发展，以确定药物治疗失败（AIM 2），并开发和优化ATS。我们将使用飞行员顺序多重分配随机试验（SMART）（AIM 3）评估ATS的可行性和可接受性。 Smarts是一种在选定时间点多次将参与者多次随机随机随机随机随机的试验设计，已成功地用于开发，优化和测试AT。培训计划：为了促进申请人的长期职业目标 - 成为ATS开发和优化方面的独立身体科学家和专家 - 该奖项将使申请人能够更深入地了解组件，优化原则以及为个性化ATS构建的试验设计。使用机器学习对预测建模和纵向数据分析的深入了解将有助于这一点。这些目标将通过这些领域的专家，选定的教学活动，会议出勤，应用研究和赠款提案准备的仔细思想来实现。