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.

修改后的项目摘要/摘要部分 该 K23 申请提出了一项有针对性的培训和研究计划，以支持申请人成为一名独立的医师科学家。拟议的计划将确保申请人发展为患者量身定制的适应性治疗策略 (ATS) 的专业知识，为炎症患者提供个性化护理。肠道疾病，包括克罗恩病和溃疡性结肠炎 (UC) 背景：急性严重溃疡性结肠炎 (ASUC) 是一种危及生命的 UC 表现，需要立即治疗。尽管有证据表明高达 30% 的患者治疗失败并需要紧急结肠切除术和英夫利昔单抗挽救疗法，但 ASUC 的一线治疗方法自 20 世纪 50 年代以来一直保持不变。治疗对静脉注射皮质类固醇无效的患者，但高达 60% 的类固醇难治性患者也对这些挽救疗法无效。包括患者治疗反应的显着异质性和临床过程的动态日常变化在内的因素限制了我们开发有效的个性化治疗途径的能力，迫切需要改进当前的一刀切的方法。在此提案中，我们追求 ATS，它使用预先指定的序贯治疗决策，根据针对患者的明确定义的基线以及纵向临床和实验室阈值（定制变量）来指导逐步的管理变化，会降低利率通过指导顾客在每个治疗阶段最有可能有效的治疗类型、强度和时间来评估结肠切除术的效果。 研究策略：本提案的目标是：确定预测 ASUC 治疗反应的基线患者特征（目标）。 1)、开发一个特定时间的风险模型，使用定制变量来识别药物治疗失败，以指导治疗决策（目标 2），并根据患者在 ASUC 住院期间不断变化的健康需求开发和优化个性化 ATS。使用试点序贯多重分配随机试验（SMART）（目标 3）来评估我们 ATS 的可接受性，SMART 是一种在选定时间点将参与者多次随机分配到几种干预措施之一的试验设计，已成功用于开发、优化、并测试 ATS 培训计划：为了促进申请人实现长期职业目标——成为一名独立的医师科学家和 ATS 开发和优化专家——该奖项将使申请人对组件有更深入的了解，优化原则和为构建个性化 ATS 提供信息的试验设计将通过使用机器学习对预测建模和纵向数据分析进行更深入的理解来帮助实现这些目标，这些目标将通过这些领域专家的密切指导来实现。活动、会议出席、应用研究和赠款提案准备。