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

尽管它们对患者的生活质量和医疗保健利用率和成本有不利影响， (SCs)在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）的研究不足，了解甚少。缺乏 访问真实的世界，纵向患者症状数据集和无法充分建模的复杂性 SC极大地限制了研究。基于我们以前的工作，我们建议可以解决这些差距 以创新的方式使用电子健康记录（EHR）和数据科学技术。我们的整体目标 是开发，应用和完善，并实施优化的数据处理和分析管道， 用于EHR数据的常见成人慢性病症中的SC的表征。我们假设一个核心 所有常见的成人慢性病都有一组SC，并且不同的SC具有特定的特征 条件和/或治疗。该项目的长期培训目标是帮助Koleck博士成为一名 一名独立研究者，开展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），她的重点 博士前的工作利用哥伦比亚大学提供的特殊资源， 该项目将侧重于开发一个严格的管道;在供应链分析和 解释;以及临床数据挖掘，自然语言处理，机器 学习和数据可视化。在R 00阶段，Koleck博士将独立实施管道， 另一个医学中心来确定所鉴定的SC的再现性，并开始探索临床预测因子 (e.g.,社会人口统计学、实验室结果和药物治疗）。具体目标是：（1）制定 数据驱动的管道，用于使用诊断为患有以下疾病的成年患者队列表征来自EHR的SC 癌症，因为SC在这种情况下已经得到了最系统的表征; 2）将管道应用于三个 与癌症具有生物和行为风险因素的其他常见成人慢性病，即，HF， T2 DM和COPD，并评估这些病症中的SC;以及3）确定SC是否与癌症、HF、T2 DM、 和COPD时，在另一个医疗中心内实施管道，并探索临床相关，EHR- 已识别SC的记录预测因子。为了实现研究目标和培养目标，一个跨学科的 具有症状科学、生物医学信息学、数据科学、相关临床 职业发展和职业发展辅导已经汇集。这项研究意义重大，因为 一个管道，适应的格式，其中症状数据已经被记录在EHR中， 有可能大大加快SC知识的获取，并加快症状的临床转化 缓解战略。考虑到需要开发的一系列新能力，K99/R 00奖是必要的 以实现候选人的职业目标，推进慢性病症状科学。

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.