Using Large Electronic Health Records and Advanced Analytics to Develop Predictive Frailty Trajectories in Patients with Heart Failure

使用大型电子健康记录和高级分析来开发心力衰竭患者的预测衰弱轨迹

• 批准号: 10630281
• 负责人: Javad Razjouyan
• 金额: $ 14.39万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10630281
• 关键词: Accident and Emergency department; Activities of Daily Living; Admission activity; Adult; Affect; Aging; American; Award; Big Data; Biological Models; Cessation of life; Chronic Disease; Clinical; Clinical Data; Clinical Informatics; Clinical Investigator; Clinical Research; Code; Complex; Computer Models; Congestive Heart Failure; Consensus; Data; Data Collection; Data Science; Data Scientist; Data Set; Databases; Decision Aid; Decision Making; Dependence; Detection; Diagnosis; Disease; Early Intervention; Effectiveness; Elderly; Electronic Health Record; Environment; Faculty; Foundations; Frail Elderly; Future; Goals; Guidelines; Health; Health Care Costs; Health Status; Heart failure; Hospitalization; Individual; International; Intervention; Laboratories; Length of Stay; Link; Machine Learning; Measures; Medicare; Medicare claim; Medicine; Mentors; Methods; Modeling; Natural Language Processing; Older Population; Outcome; Outpatients; Patient Care; Patients; Pattern; Physiological; Population; Predictive Value; Prevalence; Procedures; Quality of Care; Reading; Recommendation; Records; Research; Research Project Grants; Risk; Safety; Scientist; Series; Source; Structure; Survival Analysis; Symptoms; Syndrome; Techniques; Testing; Text; Time; Training; advanced analytics; adverse outcome; analytical method; career; career development; clinical care; clinical decision support; clinical decision-making; clinical practice; college; data warehouse; deep neural network; experience; frailty; functional disability; functional status; high risk; hospital readmission; indexing; individual variation; innovation; member; mortality; mortality risk; multidisciplinary; novel; point of care; poor health outcome; predictive modeling; prognostic value; random forest; routine care; screening; signal processing; skills; stressor; support tools; tool

Candidate objective: My objective for this award is to become an independent quantitative scientist in analytical clinical research through structured training and mentored research experience. My goal is to become an academic leader and developer of advanced predictive models of health trajectories using electronic health records (EHR). Training objectives: I seek to sharpen my skill set as a clinical quantitative scientist using clinical informatics, EHR data warehouses, and advanced computational models. I will use the protected time provided by this award to gain proficiency in patient-clinician interactions, clinical informatics, natural language processing, and advanced survival analysis to accomplish my research aims. Background: Frailty is a complex clinical syndrome associated with aging and chronic illness. It decreases physiological reserves and increases vulnerability to stressors. The prevalence of frailty in patients with heart failure is 74%. The interplay of frailty and heart failure increases the risk for death, prolonged hospital stays, and functional dependence. One conceptual framework to operationalize frailty is accumulation of deficits: the frailty index (FI). The FI provides a risk score based on the assumption that the more ailments a patient has, the higher the risk of adverse outcomes, including mortality. Prior FI models have not been used in routine clinical practice due to the following limitations: insufficient number and range of clinical variables, lack of personalized deficit detection, use of data not commonly found in EHRs, insufficient use of longitudinal analytical models including survival analysis techniques, and the reduction of FI to a cross-sectional health status rather than a health trajectory. Research Aim: The overarching goal of this application is to develop a frailty trajectory (FT) for heart failure patients that provides information integrating prior functional impairment, current functional status, and future risk of mortality. In Aim 1, we will develop a novel cross-sectional FI that uses the full breadth of outpatient EHR data and innovative machine learning data science methods to predict mortality. In Aim 2, we will use serial cross-sectional FIs to build FTs and identify clusters of individuals following a similar progression of frailty over time. In Aim 3, we will compare the prognostic value of cross-sectional FI versus FT. The VA national EHR offers the ideal context for this study, as it provides longitudinal data since 1999 and can link to administrative data from non- VA sources, including linked Medicare databases. Mentoring & environment: A multidisciplinary mentoring team will supervise my training and will oversee my mentored research projects, formal coursework, directed reading, and career development. The proposed activities will provide a foundation for transitioning to an independent quantitative data scientist developing clinical decision aids to guide patient care. Baylor College of Medicine and the Center for Innovations in Quality, Effectiveness, and Safety have a national reputation of mentoring and supporting junior faculty members from diverse academic backgrounds to independent careers as clinical-investigators.

候选目标：我的目标是成为一名独立的定量科学家，在分析 通过结构化培训和指导研究经验进行临床研究。我的目标是成为 使用电子健康的健康轨迹的先进预测模型的学术领导者和开发者 记录（EHR）。培训目标：我寻求提高我的技能，作为一个临床定量科学家使用临床 信息学、EHR数据仓库和高级计算模型。我会利用提供的受保护时间 通过这个奖项，以获得熟练的病人与临床医生的互动，临床信息学，自然语言处理， 和先进的生存分析来实现我的研究目标。背景：虚弱是一种复杂的临床 与衰老和慢性病有关的综合征。它会减少生理储备， 对压力源的脆弱性心力衰竭患者虚弱的患病率为74%。脆弱和 心力衰竭增加了死亡、延长住院时间和功能依赖的风险。一个概念 一个可操作的框架是缺陷的积累：脆弱指数（FI）。FI提供风险评分 基于这样的假设，即患者患有的疾病越多，不良后果的风险就越高，包括 mortality.由于以下局限性，之前的FI模型尚未用于常规临床实践： 临床变量的数量和范围不足，缺乏个性化的缺陷检测，数据的使用不 在EHR中常见，纵向分析模型（包括生存分析）的使用不足 技术，并减少FI的横截面健康状况，而不是健康轨迹。研究 目的：本应用程序的首要目标是为心力衰竭患者开发一个虚弱轨迹（FT）， 提供整合既往功能损害、当前功能状态和未来死亡风险的信息。 在目标1中，我们将开发一种新的横截面FI，它使用门诊EHR数据的全部广度， 创新的机器学习数据科学方法来预测死亡率。在目标2中，我们将使用连续横截面 金融机构建立FT和识别集群的个人后，随着时间的推移，类似的进展脆弱。在目标3中， 我们将比较横截面FI与FT的预后价值。VA国家EHR提供了理想的 这项研究的背景，因为它提供了自1999年以来的纵向数据，并可以链接到非 VA来源，包括链接的医疗保险数据库。指导与环境：多学科指导 团队将监督我的培训，并监督我指导的研究项目、正式课程作业、指导 阅读和职业发展。拟议的活动将为过渡到 独立的定量数据科学家开发临床决策辅助工具，以指导患者护理。Baylor College of 医学和质量、有效性和安全性创新中心在全国享有盛誉， 指导和支持来自不同学术背景的初级教师独立职业 作为临床研究者。

项目成果

Implementation of automated early warning decision support to detect acute decompensation in the emergency department improves hospital mortality.

• DOI: 10.1136/bmjoq-2021-001653
• 发表时间: 2022-04
• 期刊: BMJ open quality
• 影响因子: 1.4
• 作者: Howard C;Amspoker AB;Morgan CK;Kuo D;Esquivel A;Rosen T;Razjouyan J;Siddique MA;Herlihy JP;Naik AD
• 通讯作者: Naik AD

Smoking Status and Factors associated with COVID-19 In-Hospital Mortality among US Veterans.

• DOI: 10.1093/ntr/ntab223
• 发表时间: 2022-03-26
• 期刊: Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco
• 作者: Razjouyan J;Helmer DA;Lynch KE;Hanania NA;Klotman PE;Sharafkhaneh A;Amos CI
• 通讯作者: Amos CI

Elevated Risk of Chronic Respiratory Conditions within 60 Days of COVID-19 Hospitalization in Veterans.

• DOI: 10.3390/healthcare10020300
• 发表时间: 2022-02-04
• 期刊: Healthcare (Basel, Switzerland)
• 作者: Park C;Razjouyan J;Hanania NA;Helmer DA;Naik AD;Lynch KE;Amos CI;Sharafkhaneh A
• 通讯作者: Sharafkhaneh A

Inflammatory Biomarkers Differ among Hospitalized Veterans Infected with Alpha, Delta, and Omicron SARS-CoV-2 Variants.

• DOI: 10.3390/ijerph20042987
• 发表时间: 2023-02-08
• 期刊: International journal of environmental research and public health
• 作者: Park C;Tavakoli-Tabasi S;Sharafkhaneh A;Seligman BJ;Hicken B;Amos CI;Chou A;Razjouyan J
• 通讯作者: Razjouyan J

Assessing Smoking Status and Risk of SARS-CoV-2 Infection: A Machine Learning Approach among Veterans.

• DOI: 10.3390/healthcare10071244
• 发表时间: 2022-07-04
• 期刊: HEALTHCARE
• 影响因子: 2.8
• 作者: Djotsa, Alice B. S. Nono;Helmer, Drew A.;Park, Catherine;Lynch, Kristine E.;Sharafkhaneh, Amir;Naik, Aanand D.;Razjouyan, Javad;Amos, Christopher, I
• 通讯作者: Amos, Christopher, I