Clinical trial NCT05471011: COVID-19 Outcome Prediction Algorithm
Official title: Multi-Dimensional Outcome Prediction Algorithm for Hospitalized COVID-19 Patients
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: COVID-19
Post Acute Sequelae of COVID-19
Long COVID
Organ Dysfunction Syndrome, Multiple
Frailty Syndrome
Keywords: outcome prediction
systems biology
multiomics
algorithm
veterans
high-risk populations
Lead sponsor: University of California, Los Angeles
Sponsor class: OTHER
Collaborators: name: Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
name: Olive View-UCLA Education & Research Institute
name: VA Greater Los Angeles Healthcare System
name: Michael E. DeBakey VA Medical Center
name: Atlanta VA Medical Center
name: Bronx VA Medical Center
First posted: 2022-07-22
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Symptomatic COVID-19 infected civilians and symptomatic COVID-19 infected veterans

Brief 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

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

Primary outcomes:
measure: New onset or worsening frailty, single organ dysfunction, multi-organ dysfunction, and death within one year
timeFrame: From hospital admission to one year
Secondary outcomes:
measure: New onset or worsening frailty, single organ dysfunction, multi-organ dysfunction, and death at time of discharge
timeFrame: From hospital admission to time of discharge
Other outcomes:
Locations:
city: Los Angeles
country: United States
facility: VA Greater Los Angeles Healthcare System
state: California
city: Los Angeles
country: United States
facility: Ronald Reagan UCLA Medical Center
state: California
city: Sylmar
country: United States
facility: Olive View-UCLA Education & Research Institute
state: California
city: Torrance
country: United States
facility: Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
state: California
city: Decatur
country: United States
facility: Atlanta VA Medical Center
state: Georgia
city: Bronx
country: United States
facility: Bronx VA Medical Center
state: New York
city: Houston
country: United States
facility: Michael E. DeBakey VA Medical Center
state: Texas
Study officials:
References:
citation: Deng MC. Multi-dimensional COVID-19 short- and long-term outcome prediction algorithm. Expert Rev Precis Med Drug Dev. 2020;5(4):239-242. doi: 10.1080/23808993.2020.1785286. Epub 2020 Jun 24. No abstract available.