# A COVID-19 Pulmonary Outcome Clinical Prediction Rule Using Epigenetics

> **NIH NIH F32** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2022 · $80,310

## Abstract

PROJECT SUMMARY/ABSTRACT
Although most SARS-CoV-2 infected patients develop mild illness, a minority progress to develop severe
pulmonary outcomes. The pathogenesis of COVID-19 pneumonia and associated respiratory failure remains
poorly understood. Unlike patients with community-acquired pneumonia, who rapidly develop clinical and
radiologic evidence of infection, patients with COVID-19 pneumonia have a several-day interval from the start
of infective symptoms to hospitalization with radiographically apparent pneumonia. Predicting which patients
who initially present with mild symptoms will remain minimally symptomatic versus those who progress to
severe pulmonary outcomes is currently impossible. This is a critical knowledge gap because these patients
could be targeted with early critical interventions to improve outcomes and preserve limited resources. The
objective of this project is to model and validate a clinical prediction rule that incorporates existing, detailed
clinical variables and epigenetic markers derived from our electronic medical record data warehouse to
develop the COVID-19 severity clinical prediction rule (COPR). The central hypothesis is that, in patients
initially presenting with minimal symptoms, the COPR will predict who will remain minimally symptomatic and
who will progress to severe pulmonary outcomes. The Specific Aims therefore include: (1) to identify clinical
variables and epigenetic markers to predict progression to severe COVID-19 pulmonary outcomes, and (2) to
internally validate this clinical prediction rule. This study will facilitate the efficient use of healthcare resources
through the identification of infected individuals early in their disease course and prediction of severe
pulmonary outcomes during periods of minimal symptoms. Through this project I will learn how to: 1) develop
and validate clinical decision rules, and 2) apply `omics to clinical investigation. This combination clinical-
epigenetic variable approach could also be beneficial for the prediction of clinical outcomes in other viral
infections and may be remodeled, validated, and deployed for the next pandemic.

## Key facts

- **NIH application ID:** 10661384
- **Project number:** 7F32HL160123-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Cosby Arnold
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $80,310
- **Award type:** 7
- **Project period:** 2021-08-20 → 2023-08-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10661384

## Citation

> US National Institutes of Health, RePORTER application 10661384, A COVID-19 Pulmonary Outcome Clinical Prediction Rule Using Epigenetics (7F32HL160123-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10661384. Licensed CC0.

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