COVID-19 Outcome Prediction Algorithm

NCT ID: NCT05471011

Last Updated: 2023-05-11

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 600 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-08-08
• Study Completion Date: 2026-05-31

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.

Conditions

COVID-19; Post Acute Sequelae of COVID-19; Long COVID; Organ Dysfunction Syndrome, Multiple; Frailty Syndrome

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

civilian

Blood and nasal swab sampling

Intervention Type: OTHER

Blood and nasal swab sampling

Veteran

Blood and nasal swab sampling

Intervention Type: OTHER

Blood and nasal swab sampling

Interventions

Blood and nasal swab sampling

Blood and nasal swab sampling

Intervention Type: OTHER

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center

OTHER

Sponsor Role: collaborator

Olive View-UCLA Education & Research Institute

OTHER

Sponsor Role: collaborator

VA Greater Los Angeles Healthcare System

FED

Sponsor Role: collaborator

Michael E. DeBakey VA Medical Center

FED

Sponsor Role: collaborator

Atlanta VA Medical Center

FED

Sponsor Role: collaborator

Bronx VA Medical Center

FED

Sponsor Role: collaborator

University of California, Los Angeles

OTHER

Sponsor Role: lead

Responsible Party

Mario C. Deng

Professor of Medicine

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

VA Greater Los Angeles Healthcare System

Los Angeles, California, United States

Site Status: RECRUITING

Ronald Reagan UCLA Medical Center

Los Angeles, California, United States

Site Status: RECRUITING

Olive View-UCLA Education & Research Institute

Sylmar, California, United States

Site Status: RECRUITING

Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center

Torrance, California, United States

Site Status: RECRUITING

Atlanta VA Medical Center

Decatur, Georgia, United States

Site Status: ACTIVE_NOT_RECRUITING

Bronx VA Medical Center

The Bronx, New York, United States

Site Status: ACTIVE_NOT_RECRUITING

Michael E. DeBakey VA Medical Center

Houston, Texas, United States

Site Status: ACTIVE_NOT_RECRUITING

Countries

United States

Central Contacts

Mario C Deng, MD

Role: CONTACT

mdeng@mednet.ucla.edu

3107532759

David Beenhouwer, MD

Role: CONTACT

dbeenhou@g.ucla.edu

3104783711

Facility Contacts

David Beenhouwer, MD

Role: primary

dbeenhou@ucla.edu

310-268-3936

Mario C Deng, MD

Role: primary

mdeng@mednet.ucla.edu

310-753-2759

Irina Silacheva, BS

Role: backup

isilacheva@ucla.edu

3109105445

Glenn Mathisen, MD

Role: primary

gmathisen@dhs.lacounty.gov

747-210-3205

Tim Hatlen, MD

Role: primary

timothy.hatlen@lundquist.org

424-201-3000 ext. 7319

References

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.

Reference Type: BACKGROUND

PMID: 33283045 (View on PubMed)

Provided Documents

Document Type: Study Protocol

View Document

Other Identifiers

1R01AI159946-01A1

Identifier Type: NIH

Identifier Source: org_study_id

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