# Predictive Modeling of COVID-19 Progression in Older Patients

> **NIH NIH P20** · TULANE UNIVERSITY OF LOUISIANA · 2020 · $379,884

## Abstract

The objective of this proposal is to develop a predictive model to identify individuals who are infected with
SARS-CoV-2 and at risk of developing severe COVID-19. Louisiana has the 5th highest death rate per capita in
the United States as of May 4th, 2020. Severe disease is seen in older individuals and those with underlying
conditions. The New Orleans population is particularly susceptible to severe COVID-19 as hypertension,
diabetes and obesity are rampant. After infection, acute lung injury caused by the virus must be repaired to
regain lung function and avoid acute respiratory distress syndrome and pulmonary fibrosis. Mounting evidence
suggests that patients with severe COVID-19 have cytokine storm syndrome, which may exacerbate
multiorgan injury and risk of fibrotic complications. Lack of effective ways to identify and attenuate severe
COVID-19 progression persist due to limited understanding of the biological pathways responsible for cytokine
storm syndrome and increased risk in older patients. Therefore, there is a need to determine the critical
cytokine profiles responsible for severe COVID-19 progression to develop effective treatments. Further, it is
essential to find a way to stage disease trajectory(ies) to identify therapeutic targets with precision to attenuate
disease progression and uncover preventive strategies. Towards this end, we seek to leverage a mathematical
model of SARS-CoV-2-induced lung damage to predict severity of acute respiratory distress syndrome and
pulmonary fibrosis by considering key cytokine-cell interactions. We hypothesize that the model will accurately
predict quantitative changes in suites of key cytokines and matrix accumulation with varying COVID-19
progression within 10% accuracy. To accomplish this, we have assembled an investigative team at Tulane
University with key expertise in virology, clinical infectious disease research, bioinformatics, and predictive
mathematical models of tissue remodeling. In Aim 1 of the proposal, we will identify the critical cytokine
markers linked to viral-induced lung damage and pulmonary fibrosis. This will be accomplished by leveraging
machine learning to determine the biomarkers and molecular pathways characterizing progression of severe
COVID-19 to focus model formulation. In Aim 2, we will predict the severity of COVID-19 in older patients.
Model predictions will be compared to blood markers of COVID-19 disease in cohorts of older patients at
different stages of disease progression. The model will be refined and informed by cytokine data to discern
causal biological pathways and disease processes that can be tested and targeted. Our expected outcome is
to have determined the critical cytokine interactions responsible for lung tissue damage and dictating pathways
for varying disease trajectories in older patients. These results are expected to have an important impact as
the proposed predictive model will open new avenues of research to rationally design pharmaceuti...

## Key facts

- **NIH application ID:** 10162283
- **Project number:** 3P20GM103629-09S1
- **Recipient organization:** TULANE UNIVERSITY OF LOUISIANA
- **Principal Investigator:** S MICHAL JAZWINSKI
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $379,884
- **Award type:** 3
- **Project period:** 2012-08-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10162283

## Citation

> US National Institutes of Health, RePORTER application 10162283, Predictive Modeling of COVID-19 Progression in Older Patients (3P20GM103629-09S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10162283. Licensed CC0.

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