CVD Risk and Outcome Heterogeneity in Older Adults with Diabetes

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: unknown

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2017
    2021
  • Known Financial Commitments (USD)

    $368,910
  • Funder

    National Institutes of Health (NIH)
  • Principle Investigator

    Pending
  • Research Location

    United States of America, Americas
  • Lead Research Institution

    NEW YORK UNIVERSITY SCHOOL OF MEDICINE
  • Research Category

    Epidemiological studies

  • Research Subcategory

    Disease susceptibility

  • Special Interest Tags

    Gender

  • Study Subject

    Clinical

  • Clinical Trial Details

    Not applicable

  • Broad Policy Alignment

    Pending

  • Age Group

    Adults (18 and older)Older adults (65 and older)

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

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 ofhospitalization, while admission oxygen impairment and markers of inflammation are most strongly associatedwith 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 ClinicalTrial Optional)" and supplements parent NIA R01 grant (1R01AG054467-01A1) "CVD Risk and OutcomeHeterogeneity 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 anddisability, 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) datasetsin 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 HealthEHR 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), anEHR 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 following3 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 ahigh level of scientific rigor to the proposed project.