SCH: INT: Collaborative Research: Development and analysis of new mathematical and statistical models for chronic pain

SCH：INT：合作研究：慢性疼痛新数学和统计模型的开发和分析

基本信息

批准号：
10180356
负责人：
Daniel M Abrams
金额：
$ 36.85万
依托单位：
NORTHWESTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-13 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10180356
关键词：
Address Agreement Analgesics Biological Biology CD4 Lymphocyte Count Car Phone Characteristics Chemical Engineering Chronic Disease Classification Custom Data Data Science Development Differential Equation Documentation Educational Background Event Financial cost Future Goals Growth HIV Hospitalization Hybrids Intervention Knowledge Literature Machine Learning Mathematics Medical Medical Device Methods Microbiology Mining Modeling Names Optimum Populations Pain Patients Pharmaceutical Preparations Play Principal Investigator Process Reporting Research Role Science Sickle Cell Anemia Source Statistical Data Interpretation Statistical Models Stream System Time Treatment Effectiveness Treatment Protocols Viral Load result Visit acute care base biological systems biomedical data science chronic pain comparative data streams dynamic system follow-up health management human disease individual patient insight intervention cost mathematical model mathematical sciences medical complication mobile application mobile computing neural network patient population precision medicine predictive modeling programs readmission rates research and development statistics theories tool

项目摘要

Program Director/Principal Investigator (Last, First, Middle): Abrams, Daniel, M Project Description 1. Intellectual merit (see Sec. 2, pages 13-14, for "Broader impacts") 1.1 Introduction and background 1.1.1 General introduction During recent decades there has been an extraordinary growth in the availability of data relating to a wide range of microbiological systems. That data has enabled new quantitative approaches to biology, including the development of new mathematical and statistical models that given fun- damental insight into the workings of biological systems. Another source is now growing explosively: biomedical data. This data has significant potential for use in treatment of human disease, but thus far comparatively fewer mathematical models for medical phenomena have been developed. The hope is that quantitative models will allow for "personalized" or "precision" medicine, where treatment protocols are customized based on an understanding of how individual patient characteristics impact the effectiveness of the treatment. Deep mathematical understanding of biomedical systems also promises to allow for optimization of medical interventions: the physical and/or financial costs of intervention could be minimized for a given desired level of benefit. The broad goal of the proposed research is to develop new integrative mathematical models for the dynamics of subjective pain in patients suffering from chronic pain. These models will combine existing qualitative knowledge with insight gained from newly available patient data, with the goal of incorporating data streams corning on line in the near future. We plan to develop multiple models in parallel using a variety of approaches and then to select the best rnodel(s) based on agreement with objective data. 1.1.2 Background on biological application: Sickle cell disease Sickle cell disease (SCD) is a chronic illness associated with frequent medical complications and hospitalizations. Approximately 90% of acute care visits are for pain events, and 30-day reuti- lization rates are alarmingly high [27]. While factors influenci.ng these high re-utilization rates are poorly understood, close follow-up and continued use of pain medication has been shown to de- crease re-hospitalization rates. Mobile technology has become an integral part of health care management and Pl Shah's recently developed mobile application (SMART app - see Figure 1) for SCD assists with documentation of pain and interventions. 1.1.3 Background on hybrid approach Perhaps because of the often distinct educational backgrounds of practitioners or distinct typical applications, statistical and mechanistic approaches are not frequently combined in addressing a single problem. The majority of attempts in the scientific literature have appeared in the context of neural networks [37, 38, 29] and chemical engineering [38, 33, 11], where they largely play a computational rather than analytical role. Some attempts have also been made with medical applications: Rosenberg et al. [30] and Adams et al. [4] developed a model by combining a dy- namical systems approach with a statistical model to predict a patient's CD4 cell counts and HIV viral load over time in an HIV study. Timms et al. [39] proposed a dynamical systems approach 1 0MB No. 0925-0001/0002 (Rev. 01/18 Approved Through 03/31/2020) Page_ Continuation Format Page

项目总监/首席研究员（最后、第一、中间）：Abrams、Daniel、M 项目描述 1. 智力优势（参见第 2 节第 13-14 页“更广泛的影响”） 1.1 简介和背景 1.1.1 概述近几十年来，相关数据的可用性出现了惊人的增长。广泛的微生物系统。这些数据启用了新的定量方法生物学，包括开发新的数学和统计模型，这些模型赋予了乐趣对生物系统运作的基本洞察。另一个来源现在正在爆炸性增长：生物医学数据。该数据具有巨大潜力用于治疗人类疾病，但迄今为止相对较少的数学模型医学现象已经发展起来。希望定量模型能够允许 “个性化”或“精准”医学，即根据患者的情况定制治疗方案了解患者的个体特征如何影响治疗的有效性。对生物医学系统的深入数学理解也有望实现优化医疗干预措施：干预措施的物理和/或财务成本可以最小化给定的期望利益水平。拟议研究的总体目标是开发新的综合数学模型患有慢性疼痛的患者主观疼痛的动态。这些模型将结合现有的定性知识以及从新获得的患者数据中获得的见解，其目标合并在不久的将来上线的数据流。我们计划开发多种型号并行使用多种方法，然后根据协议选择最佳 rnodel(s) 用客观数据。 1.1.2 生物学应用背景：镰状细胞病镰状细胞病 (SCD) 是一种与频繁的医疗并发症相关的慢性疾病，住院治疗。大约 90% 的急性护理就诊是因为疼痛事件，并且 30 天的回顾化率高得惊人[27]。虽然影响这些高再利用率的因素是知之甚少，密切随访和继续使用止痛药已被证明可以消除提高再住院率。移动技术已成为医疗保健不可或缺的一部分管理和 Pl Shah 最近开发的移动应用程序（SMART 应用程序 - 见图 1） SCD 有助于记录疼痛和干预措施。 1.1.3 混合方法的背景也许是因为从业者通常具有不同的教育背景或不同的典型特征应用、统计和机械方法在解决问题时并不经常结合起来单一问题。科学文献中的大多数尝试都出现在这样的背景下神经网络 [37,38,29] 和化学工程 [38,33,11]，它们主要发挥作用计算而非分析的角色。医疗方面也做了一些尝试应用：罗森伯格等人。 [30] 和亚当斯等人。 [4]通过结合dy-开发了一个模型使用统计模型的自然系统方法来预测患者的 CD4 细胞计数和 HIV HIV 研究中随时间变化的病毒载量。蒂姆斯等人。 [39]提出了一种动力系统方法 1 0MB No. 0925-0001/0002（修订版 01/18 已批准至 03/31/2020） Page_ 继续格式页