COVID-19 Outcome Prediction Algorithm (COPA)

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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: COVID-19; Frailty Syndrome; Long COVID; Post Acute Sequelae of COVID-19; Organ Dysfunction Syndrome, Multiple
• Intervention / Treatment: Other: Blood and nasal swab sampling

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.

• Study Type: Observational
• Enrollment (Estimated): 600

Contacts and Locations

Study Locations

• United States
  • California
    • Los Angeles, California, United States, 90095
      • Ronald Reagan UCLA Medical Center
    • Los Angeles, California, United States, 90073
      • VA Greater Los Angeles Healthcare System
    • Sylmar, California, United States, 91342
      • Olive View-UCLA Education & Research Institute
    • Torrance, California, United States, 90502
      • Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
  • Georgia
    • Decatur, Georgia, United States, 30033
      • Atlanta VA Medical Center
  • New York
    • The Bronx, New York, United States, 10468
      • Bronx VA Medical Center
  • Texas
    • Houston, Texas, United States, 77030
      • Michael E. DeBakey VA Medical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Symptomatic COVID-19 infected civilians and symptomatic COVID-19 infected veterans

Description

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
civilian	Other: Blood and nasal swab sampling; Blood and nasal swab sampling
Veteran	Other: Blood and nasal swab sampling; Blood and nasal swab sampling

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
New onset or worsening frailty, single organ dysfunction, multi-organ dysfunction, and death within one year	The primary outcome clinical composite endpoints that include new onset or worsening frailty, single organ dysfunction, multi organ dysfunction, and death within one year will include a follow-up period of time of at least 52 weeks after initial enrollment encounter. The assessment of time to event (outcome events will include various frailty measurement tools including short physical performance battery, laboratory test-based organ function assessment measures, and survival status as per publicly-accessible databases) will consist of calculating the time difference between first outcome event and baseline encounter date.	From hospital admission to one year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
New onset or worsening frailty, single organ dysfunction, multi-organ dysfunction, and death at time of discharge	The secondary outcome clinical composite endpoints that include new onset or worsening frailty, single organ dysfunction, multi organ dysfunction, and death within the time from initial encounter to the time of discharge will include a follow-up period consisting of the time from initial enrollment encounter to the time of discharge. The assessment of time to event (outcome events will include various frailty measurement tools including short physical performance battery, laboratory test-based organ function assessment measures, and survival status as per publicly-accessible databases) will consist of calculating the time difference between first outcome event and baseline encounter date.	From hospital admission to time of discharge

Collaborators and Investigators

• Sponsor: University of California, Los Angeles
• Collaborators: Michael E. DeBakey VA Medical Center; Atlanta VA Medical Center; VA Greater Los Angeles Healthcare System; Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center; Olive View-UCLA Education & Research Institute; Bronx VA Medical Center

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): August 8, 2022
• Primary Completion (Estimated): April 30, 2026
• Study Completion (Estimated): May 31, 2026

Study Registration Dates

• First Submitted: July 15, 2022
• First Submitted That Met QC Criteria: July 20, 2022
• First Posted (Actual): July 22, 2022

Study Record Updates

• Last Update Posted (Actual): April 24, 2026
• Last Update Submitted That Met QC Criteria: April 22, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: algorithm; veterans; outcome prediction; systems biology; multiomics; high-risk populations
• Additional Relevant MeSH Terms: Post-Infectious Disorders; Pathologic Processes; Chronic Disease; Disease Attributes; Respiratory Tract Infections; Infections; RNA Virus Infections; Virus Diseases; Respiratory Tract Diseases; Lung Diseases; Pneumonia, Viral; Pneumonia; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; Shock; Pathological Conditions, Signs and Symptoms; COVID-19; Frailty; Post-Acute COVID-19 Syndrome; Multiple Organ Failure; Investigative Techniques; Specimen Handling; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Punctures; Surgical Procedures, Operative; Blood Specimen Collection
• Other Study ID Numbers: 1R01AI159946-01A1 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: After our data is cleaned, quality checked, and analyzed, we will make the data available to the general research community. Data collected in this proposal will be submitted to the appropriate public databases (e.g. Gene Expression Omnibus), along with complete documentation to enable efficient use of the data by the general research community. Cumulative datasets will be submitted on a regular basis in a timely manner. All data made available for public use will be de-identified data, i.e., stripped of private, protected health information that could be used to deduce the identity of individual subjects, in compliance with the HIPPA Privacy Rule. The study will be registered in the database of Genotypes and Phenotypes and the following data and information will be shared through the Sequence Read Archive and Gene Expression Omnibus.
• IPD Sharing Time Frame: The data will be available following the completion of the study and will be available indefinitely.
• IPD Sharing Access Criteria: COPA Study website to be determined
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No