Summary

Eligibility
for people ages 18 years and up (full criteria)
Location
at UCLA
Dates
study started
estimated completion
Principal Investigator
by Mario C. Deng (ucla)

Description

Summary

Severe acute respiratory syndrome coronavirus 2-mediated coronavirus disease (COVID-19) is an evolutionarily unprecedented natural experiment that causes major changes to the host immune system. We propose to develop a test that accurately predicts short- and long-term (within one-year) outcomes in hospitalized COVID-19 patients broadly reflecting US demographics who are at increased risk of adverse outcomes from COVID-19 using both clinical and molecular data. We will enroll patients from a hospitalized civilian population in one of the country's largest metropolitan areas and a representative National Veteran's population.

Official Title

Multi-Dimensional Outcome Prediction Algorithm for Hospitalized COVID-19 Patients

Details

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-mediated coronavirus disease (COVID-19) is an evolutionarily unprecedented natural experiment that causes major changes to the host immune system. Several high risk COVID-19 populations have been identified. Older adults, males, persons of color, and those with certain underlying health conditions (e.g., diabetes mellitus, obesity, etc.) are at higher risk for severe disease from COVID-19. While it is too soon to fully understand the impact of COVID-19 on overall health and well-being, there are already several reports of significant sequelae, which appear to correlate with disease severity. There is a clear and urgent need to develop prediction tests for adverse short- and long-term outcomes, especially for high-risk COVID-19 populations. We hypothesize that complementary multi-dimensional information gathered near the time of symptom onset can be used to predict new onset or worsening frailty, organ dysfunction and death within one year after COVID-19 onset. A single parameter provides limited information and is incapable of adequately characterizing the complex biological responses in symptomatic COVID-19 to predict outcome. Since they were designed for other illnesses, it is unlikely that existing clinical tools, such as respiratory, cardiovascular, and other organ function assessment scores, will precisely assess the long-term prognosis of this novel disease. Our extensive experience in biomarker development suggests that integrating molecular and clinical data increases prediction accuracy of long-term outcomes. We have chosen to test our hypothesis in a population reflecting US-demographics that is at increased risk of adverse outcomes from COVID-19. We will enroll patients, broadly reflecting US demographics, from a hospitalized civilian population in one of the country's largest metropolitan areas and a representative National Veteran's population. We anticipate that a prediction test that performs well in this hospitalized patient group will: help guide triaging and treatment decisions and, therefore, reduce morbidity and mortality rates, enhance patient quality of life, and improve healthcare cost-effectiveness. More accurate prognostic information will also assist clinicians in framing goals of care discussions in situations of likely futility and assist patients and families in this decision-making process. Finally, it will provide a logical means for allocating resources in short supply, such as ventilators or therapeutics with limited availability.

Keywords

COVID-19 Post Acute Sequelae of COVID-19 Long COVID Organ Dysfunction Syndrome, Multiple Frailty Syndrome outcome prediction systems biology multiomics algorithm veterans high-risk populations Syndrome Frailty Multiple Organ Failure Blood and nasal swab sampling civilian Veteran

Eligibility

You can join if…

Open to people ages 18 years and up

  • Symptomatic COVID-19 infection with hospital admission
  • Age 18 and above
  • Informed consent

You CAN'T join if...

  • Absence of symptomatic COVID-19 infection with hospital admission
  • Age 17 or below
  • No informed consent

Locations

  • Ronald Reagan UCLA Medical Center
    Los Angeles California 90095 United States
  • Olive View-UCLA Education & Research Institute
    Sylmar California 91342 United States
  • Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
    Torrance California 90502 United States
  • VA Greater Los Angeles Healthcare System
    Los Angeles California 90073 United States

Lead Scientist at University of California Health

Details

Status
in progress, not accepting new patients
Start Date
Completion Date
(estimated)
Sponsor
University of California, Los Angeles
ID
NCT05471011
Study Type
Observational
Participants
At least 600 people participating
Last Updated