SCH: EXP: Collaborative Research: Group-Specific Learning to Personalize Evidence-Based Medicine

SCH：EXP：协作研究：针对群体的特定学习以个性化循证医学

• 批准号: 1602198
• 负责人: Pedro Caraballo
• 金额: $ 21.77万
• 依托单位: Mayo Clinic Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1602198&HistoricalAwards=false
• 关键词: SCH; EXP; Collaborative; Research; Group

Patient care is increasingly guided by evidence based practice guidelines, which are defined as "systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances." These guidelines are viewed as the key to improving patient care and reducing costs, but current guidelines tend to be specific to individual diseases, and rarely consider all of the relevant details of a patient's condition, such as age, gender, and ethnic background, as well as other diseases from which patients suffer. By addressing the challenge of personalized evidence based medicine, the research in this project will positively impact patients suffering from multiple chronic conditions, which is becoming the norm in the aging US population. To that end, this project will develop new clinical modeling techniques that can use the data available in electronic health records (EHRs) to improve the personalization of these guidelines. More specifically, the ultimate goal of this project is to generate more personalized guidelines that can be implemented in clinical decision support systems and used by physicians and others for the comprehensive treatment of patients with multiple chronic conditions.To address the challenge of personalized care guidelines to handle multiple chronic conditions, this project develops a modeling framework, Group-Specific Learning (GSL), with the ability to enhance clinical modeling by making models increasingly personalized without rendering them excessively specific. In particular, the GSL modeling paradigm is applied to enhance four modeling techniques commonly used in health sciences research: survival analysis, causal analysis via propensity scoring, competing risk models and multi-state models. This work focuses on type-II diabetes mellitus (T2DM), its precursor, pre-diabetes, its comorbidities (hypertension, obesity, hyperlipidemia), and its consequences (chronic kidney disease, renal failure and the various cardiac and vascular complications). Diabetes has a number of interrelated comorbidities and severe complications, but evidence-based guidelines for the treatment of these conditions treat these conditions in isolation. To address this limitation, this project develops a suite of analytics techniques that can take the substantial heterogeneity that exists in the diabetic population into account in order to measure the effect of existing evidence-based guideline elements (interventions) in terms of risk of progression to diabetic complications. These guideline elements can then be compiled into guidelines, thus allowing for the systemic and comprehensive treatment of the population with heterogeneity.

患者护理越来越多地受到基于证据的实践指南的指导，这些指南被定义为“系统开发的陈述，以协助从业人员和患者对特定临床状况的适当医疗保健的决定”。这些准则被视为改善患者护理和降低成本的关键，但是当前的指南往往针对个体疾病，很少考虑患者病情的所有相关细节，例如年龄，性别和种族背景以及其他患者受苦的疾病。通过应对基于个性化证据的医学的挑战，该项目的研究将对患有多种慢性疾病的患者产生积极影响，这已成为美国老年人群的常态。 为此，该项目将开发新的临床建模技术，这些技术可以使用电子健康记录（EHR）中可用的数据来改善这些准则的个性化。更具体地说，该项目的最终目标是生成更个性化的准则，这些准则可以在临床决策支持系统中实施，并由医生和其他人使用，以全面治疗具有多种慢性状况的患者，以解决个性化护理指南的挑战以处理多个慢性状况的挑战，该项目通过建模框架进行了特定的模型，并通过建模为模型，以增强临床模型，以增强临床模型。特别是，GSL建模范式用于增强健康科学研究中常用的四种建模技术：生存分析，通过倾向评分，竞争风险模型和多状态模型的因果分析。这项工作的重点是II型糖尿病（T2DM），其前体，糖尿病前，合并症（高血压，肥胖，高脂血症）及其后果（慢性肾脏疾病，肾衰竭，肾脏疾病，各种心脏疾病和各种心脏和血管并发症）。糖尿病患有许多相互关联的合并症和严重的并发症，但是基于证据的治疗这些疾病的指南分别治疗这些疾病。为了解决这一局限性，该项目开发了一系列分析技术，可以将糖尿病人群中存在的实质异质性采用，以衡量现有的基于证据的指南元素（干预措施）在糖尿病并发症的风险方面的影响。然后可以将这些指南要素汇编成准则，从而使人口具有异质性的系统性和全面治疗。