CVD Risk and Outcome Heterogeneity in Older Adults with Diabetes

摘要 老年人面临2019年冠状病毒病严重并发症的更高机会(Covid- 19)。特别是，患有糖尿病的老年人通常有患上一些糖尿病相关并发症的风险。 例如心脏病或其他合并症，这可能会增加因Covid- 19.在最近对新冠肺炎患者的一项研究中，年龄、肥胖和合并疾病是预测高血压的最有力因素 住院治疗，而入院时氧损伤和炎症标志物的相关性最强 病情危重(重症监护/机械通气/临终关怀/死亡)。这项提议回应了NOT-AG- 20-022，“国家海洋局关于冠状病毒病的行政补充资料2019年(新冠肺炎)”至PA-18-591 “对现有NIH补助金和合作协议的行政补充(家长管理补充临床 可选试验)“和补充父母NIA R01补助金(1R01AG054467-01A1)”心血管疾病风险和结果 老年糖尿病患者的异质性“。父母资助的目标是研究老年患者 糖尿病，以确定糖尿病年龄如何影响心血管疾病的风险和发展，以及 残疾，这样我们才能更好地个体化治疗。我们建议同时进行一项补充研究，以 新冠肺炎对老年糖尿病患者和非糖尿病患者的住院风险和严重后果的调查 通过对超大且种族/民族多样化的电子健康记录(EHR)数据集的分析 纽约市(NYC)。具体地说，我们将在诊断出新冠肺炎之前调查患者的特征 以及那些住院的人的住院疗程。我们将分析1)纽约大学朗格尼健康中心 EHR数据(NYULH-EHR)；2)纽约市健康和医院(NYC H+H)，由11家纽约市公立医院组成的网络， 长期护理中心和诊所；3)纽约市临床数据研究网络(NYC-Insight)，以及 由纽约市20家医疗机构组成的EHR网络，纵向链接了1200万名患者的数据 在公共数据模型下的相遇。我们将分析人口统计，生命体征，诊断，实验室， 新冠肺炎确诊前和住院期间的处方和程序。我们提出以下建议 3个具体目标：目标1)确定门诊/门诊就诊史的危险因素，并制定个性化风险 新冠肺炎对老年患者住院的预测工具；目的2)确定门诊/门诊的危险因素 新冠肺炎对住院老年患者的严重后果的就诊历史和住院期间 目标3：跨医院评估和验证纽约市洞察力网络和 NYC H+H。该提案通过分析异常大的、 位于美国新冠肺炎疫情中心的全面和多样化的纽约市电子健康记录网络。我们广泛的 在门诊护理环境中对老年糖尿病患者进行纵向EHR队列的经验，以及我们的 由精通最新风险预测的临床医生和生物统计学家组成的跨学科团队为 对拟议的项目具有高度的科学严谨性。

SUMMARY Older people face a higher chance of experiencing serious complications from coronavirus disease 2019 (Covid- 19). In particular, older people with diabetes are generally at risk for a number of diabetes-related complications such as heart disease or other comorbidities, which can worsen the chance of getting seriously ill from Covid- 19. In a recent study of Covid-19 patients, age, obesity and comorbidities are strongest predictors of hospitalization, while admission oxygen impairment and markers of inflammation are most strongly associated with critical illness (intensive care/mechanical ventilation/hospice/death). This proposal responds to NOT-AG- 20-022, “NIA Administrative Supplements on Coronavirus Disease 2019 (Covid-19)” through PA-18-591 “Administrative Supplements to Existing NIH Grants and Cooperative Agreements (Parent Admin Supp Clinical Trial Optional)” and supplements parent NIA R01 grant (1R01AG054467-01A1) “CVD Risk and Outcome Heterogeneity in Older patients with Diabetes”. The goal of the parent grant is to study older patients with diabetes to identify how aging with diabetes affects the risks and development of cardiovascular disease and disability, so that we can better individualize treatment. We propose a supplementary study, in parallel, to investigate the risk of hospitalization and severe outcomes of Covid-19 in older patients with and without diabetes through analyses of the exceptionally large and racial/ethnically diverse Electronic Health Record (EHR) datasets in New York City (NYC). Specifically, we will investigate patient characteristics prior to their Covid-19 diagnoses as well as the hospital course of those hospitalized. We will analyze 1) the New York University Langone Health EHR data (NYULH-EHR); 2) the NYC Health and Hospitals (NYC H+H), a network of 11 NYC public hospitals, long term care centers and clinics; 3) the New York City Clinical Data Research Network (NYC-INSIGHT), an EHR network comprising 20 NYC healthcare institutions, with longitudinally linked data on 12 million patient encounters under a Common Data Model. We will analyze demographics, vital signs, diagnoses, labs, prescriptions, and procedures both pre Covid-19 diagnosis and during hospitalizations. We propose the following 3 Specific Aims: Aim 1) Identify risk factors of clinic/ambulatory visit histories and develop individualized risk prediction tools for hospitalization from Covid-19 for older patients; Aim 2) Identify risk factors of clinic/ambulatory visit histories and during hospital stays for severe outcomes from Covid-19 for hospitalized older patients and Aim 3: Cross-hospital assessment and validation of the risk prediction tools in the NYC INSIGHT networks and NYC H+H. The proposal addresses the urgent clinical needs by analyzing the exceptionally large, comprehensive and diverse NYC EHR network in the epicenter of the Covid-19 outbreak in the US. Our extensive experiences with the longitudinal EHR cohort of older patients with diabetes in ambulatory care settings, and our interdisciplinary team of clinicians and biostatisticians well-versed in the state-of-the-art risk-prediction bring a high level of scientific rigor to the proposed project.