COVID-19 Outcome Prediction Algorithm

Study Overview
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Brief Summary
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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
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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.

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

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

Intervention / Treatment
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* Other: Blood and nasal swab sampling


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| civilian<br> | Other: Blood and nasal swab sampling<br>* Blood and nasal swab sampling<br>|
| Veteran<br> | Other: Blood and nasal swab sampling<br>* Blood and nasal swab sampling<br>|

What is the study measuring?
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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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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outcome prediction, systems biology, multiomics, algorithm, veterans, high-risk populations