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

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $661,105

## Abstract

PROJECT SUMMARY
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.

## Key facts

- **NIH application ID:** 10656282
- **Project number:** 5R01AI159946-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** DAVID Owen BEENHOUWER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $661,105
- **Award type:** 5
- **Project period:** 2021-07-08 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10656282

## Citation

> US National Institutes of Health, RePORTER application 10656282, Multi-Dimensional Outcome Prediction Algorithm for Hospitalized COVID-19 Patients (5R01AI159946-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10656282. Licensed CC0.

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