Advancing Chronic Condition Symptom Cluster Science Through Use of Electronic Health Records and Data Science Techniques

通过使用电子健康记录和数据科学技术推进慢性病症状群科学

• 批准号: 10118580
• 负责人: Theresa Ann Koleck
• 金额: $ 24.9万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10118580
• 关键词: 18 year old; Academic Medical Centers; Address; Adult; Advance Directives; American; Anxiety; Area; Award; Behavioral; Biological; Chronic; Chronic Obstructive Airway Disease; Clinical; Clinical Data; Clinical Management; Clinical assessments; Cluster Analysis; Code; Competence; Complement; Data; Data Analyses; Data Science; Data Set; Depressed mood; Detection; Development; Diagnosis; Dyspnea; Electronic Health Record; Equipment and supply inventories; Ethnic Origin; Fatigue; Funding; Future; Genetic; Genomics; Goals; Health Care Costs; Health Promotion; Healthcare Systems; Heart failure; Impaired cognition; Informatics; Intervention; Knowledge; Knowledge acquisition; Laboratories; Lead; Life; Literature; Machine Learning; Malignant Neoplasms; Mastectomy; Medical Genetics; Medical center; Mentorship; Modeling; Natural Language Processing; Nausea; Nausea and Vomiting; Neurobehavioral Manifestations; Non-Insulin-Dependent Diabetes Mellitus; Oncology; Pain; Pathologic; Patients; Pharmaceutical Preparations; Phase; Postoperative Nausea and Vomiting; Procedures; Proteomics; Pruritus; Public Health; Quality of life; Race; Reproducibility; Research; Research Personnel; Resources; Risk Factors; Science; Scientist; Signs and Symptoms; Sleep disturbances; Strategic Planning; Structure; Symptoms; Techniques; Time; Training; United States National Institutes of Health; Universities; Validation; Woman; Work; Xerostomia; base; biomarker discovery; biomedical informatics; career; career development; clinical data warehouse; clinical practice; clinical predictors; clinical translation; clinically relevant; cohort; computerized data processing; data analysis pipeline; data mining; data visualization; data warehouse; electronic data; experience; health care service utilization; health data; innovation; knowledge translation; malignant breast neoplasm; patient oriented; pre-doctoral; process optimization; programs; sociodemographics; statistics; symptom cluster; symptom management; symptom science; symptomatic improvement; unsupervised learning

Despite their adverse impact on patient quality of life and healthcare utilization and costs, symptom clusters (SCs) in common adult chronic conditions such as cancer, heart failure (HF), type 2 diabetes mellitus (T2DM), and chronic obstructive pulmonary disease (COPD) are understudied and poorly understood. The lack of access to real world, longitudinal patient symptom data sets and inability to adequately model the complexity of SCs has greatly limited research. Based on our previous work, we propose that these gaps can be addressed in an innovative way using electronic health records (EHRs) and data science techniques. Our overall objective is to develop, apply and refine, and implement an optimized data processing and analysis pipeline for the characterization of SCs in common adult chronic conditions for use with EHR data. We hypothesize that a core set of SCs is shared among all common adult chronic conditions and that distinct SCs characterize specific conditions and/or treatments. The long term training goal of this project is to assist Dr. Koleck in becoming an independent investigator conducting a program of research dedicated to mitigating symptom burden in patients with chronic conditions through use of informatics and omics (e.g., genomics and proteomics), the focus of her pre-doctoral work. Using exceptional resources available from Columbia University, the K99 phase of this project will focus on the development of a rigorous pipeline; essential competencies in SC analysis and interpretation; and the data science techniques of clinical data mining, natural language processing, machine learning, and data visualization. In the R00 phase, Dr. Koleck will independently implement the pipeline in another medical center to determine the reproducibility of identified SCs and begin to explore clinical predictors (e.g., socio-demographics, laboratory results, and medications) of SCs. The specific aims are to 1) develop a data-driven pipeline for the characterization of SCs from EHRs using a cohort of adult patients diagnosed with cancer, as SCs have been most systematically characterized in this condition; 2) apply the pipeline to three other common adult chronic conditions that share biological and behavioral risk factors with cancer, i.e., HF, T2DM, and COPD, and evaluate SCs in these conditions; and 3) determine if SCs differ for cancer, HF, T2DM, and COPD when implementing the pipeline within another medical center and explore clinically relevant, EHR- documented predictors of identified SCs. To accomplish research aims and training goals, an interdisciplinary team of scientists with expertise in symptom science, biomedical informatics, data science, pertinent clinical domains, and career development mentorship has been assembled. This research is significant because a pipeline that accommodates the format in which symptom data is already being documented in EHRs has the potential to greatly accelerate the acquisition of SC knowledge and expedite clinical translation of symptom mitigation strategies. Given the array of new competencies to be developed, this K99/R00 award is necessary for achieving the candidate’s career goal of advancing chronic condition symptom science.

尽管它们对患者的生活质量、医疗保健利用率和成本产生了不利影响，但症状集群 (SCS)常见的成人慢性疾病，如癌症、心力衰竭(HF)、2型糖尿病(T2 DM)、 和慢性阻塞性肺疾病(COPD)的研究还不够充分和了解甚少。缺乏 能够接触到真实世界、纵向患者症状数据集，并且无法对 SCS的研究非常有限。基于我们以前的工作，我们认为这些差距是可以弥补的 以一种创新的方式使用电子健康记录和数据科学技术。我们的总体目标 是开发、应用、提炼和实施优化的数据处理和分析管道 与EHR数据一起使用的常见成人慢性病中干细胞的特征。我们假设一个核心 所有常见成人慢性病共享一组SC，且不同的SC具有特定的特征 条件和/或治疗。这个项目的长期培训目标是帮助科莱克博士成为一名 独立调查员开展了一项致力于减轻患者症状负担的研究计划 通过使用信息学和组学(例如基因组学和蛋白质组学)，HER的重点是 博士前工作。使用哥伦比亚大学提供的特殊资源，K99阶段 该项目将侧重于开发严格的管道；在SC分析和 解释；临床数据挖掘、自然语言处理、机器等数据科学技术 学习和数据可视化。在R00阶段，Koleck博士将在 另一个医学中心，确定已鉴定的干细胞的重复性，并开始探索临床预测因素 (例如，社会人口学、实验室结果和药物)。具体目标是1)开发一种 使用一组诊断为EHR的成人患者的数据驱动的管道来表征EHR中的干细胞 癌症，因为干细胞在这种情况下被最系统地描述；2)将管道应用于三个 其他与癌症具有共同的生物学和行为风险因素的常见成人慢性疾病，即心衰， T2 DM和COPD，并在这些条件下评估SCs；以及3)确定SCs在癌症、HF、T2 DM、 和COPD在另一个医疗中心内实施管道，并探索临床相关的EHR- 记录已确定的SC的预测值。为了实现研究目标和培养目标，一个跨学科的 拥有症状科学、生物医学信息学、数据科学、相关临床专业知识的科学家团队 此外，还建立了多个领域，并建立了职业发展指导。这项研究具有重要意义，因为 适应已在EHR中记录症状数据的格式的管道具有 极大地加速SC知识的获取和加快症状的临床翻译的潜力 缓解战略。鉴于需要开发的一系列新能力，K99/R00奖是必要的 为了实现候选人的职业目标，推进慢性疾病症状科学。