CVD Risk and Outcome Heterogeneity in Older Adults with Diabetes

总结 老年人面临更高的机会经历严重并发症的冠状病毒疾病2019（新冠肺炎- 19）。尤其是老年糖尿病患者，一般都有患糖尿病相关并发症的风险 如心脏病或其他合并症，这可能会增加患上严重疾病的可能性- 19.在最近一项针对Covid-19患者的研究中，年龄、肥胖和合并症是最强的预测因素， 住院，而入院氧损伤和炎症标志物最强烈相关 危重病（重症监护/机械通气/临终关怀/死亡）该提案响应了NOT-AG- 20 - 022，《NIA 2019冠状病毒病（Covid-19）行政补充》至PA-18 - 591 "对现有NIH赠款和合作协议的行政补充（家长行政补充临床 试验可选）"和补充父母NIA R01补助金（1R01AG054467 - 01A1）" CVD风险和结局 老年糖尿病患者的糖尿病并发症父母补助金的目标是研究老年患者， 糖尿病，以确定老年糖尿病如何影响心血管疾病的风险和发展， 残疾，以便我们能够更好地个性化治疗。我们建议同时进行补充研究， 调查有糖尿病和无糖尿病的老年患者的新冠肺炎住院风险和严重结局 通过分析超大型和种族/民族多样化的电子健康记录（EHR）数据集， 在纽约市（NYC）。具体来说，我们将在确诊Covid-19之前调查患者的特征 以及住院患者的住院过程。我们将分析1）纽约大学朗格尼健康 EHR数据（NYULH-EHR）; 2）纽约市健康和医院（NYC H + H），一个由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）， EHR网络由20家纽约市医疗机构组成，拥有1200万患者的纵向链接数据 在通用数据模型下进行。我们将分析人口统计学，生命体征，诊断，实验室， 在新冠肺炎确诊前和住院期间的处方和程序。我们提议下列 3具体目标：目标1）确定门诊/门诊访视史的风险因素，并制定个体化风险 老年患者因新冠肺炎住院的预测工具;目的2）识别门诊/门诊的风险因素 住院老年患者的访视史和住院期间的严重结果， 目标3：NYC INSIGHT网络中风险预测工具的跨医院评估和验证， 纽约H + H。该提案通过分析异常大的， 位于美国COVID-19疫情中心的全面而多样化的纽约市EHR网络。我们广泛 老年糖尿病患者在门诊护理环境中的纵向EHR队列的经验，以及我们的 由精通最先进风险预测的临床医生和生物统计学家组成的跨学科团队， 科学严谨的高水平的建议项目。

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.