# An integrated approach to predict and improve the outcomes of lung injury

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $776,764

## Abstract

SUMMARY
Acute respiratory distress syndrome (ARDS) is a hypoxemic syndrome characterized by disseminated
inflammation in the lungs, and often requires prolonged mechanical ventilation and intensive care. Although the
hospital mortality of ARDS has decreased, more than 40% of patients do not survive hospitalization.
Accelerating progress in treatment requires the ability to reliably attenuate ARDS severity. However, promising
therapies for ARDS are often ineffective when tested in clinical trials, due to the fact that current definitions of
ARDS select patients who have heterogeneous outcomes, thereby confounding treatment effects. The
proposed research exploits recent developments in morphological and functional imaging to investigate
integrated strategies for predicting ARDS outcomes and preventing disseminated pulmonary inflammation.
A deeper knowledge of the mechanisms that generate severe ARDS will allow us to develop better strategies
for clinical management. However, because most research addresses ARDS when it is already established,
we do not know how inflammation initially disseminates in the lungs. To better understand the propagation of
lung injury, this project will make extensive use of imaging—including quantitative computed tomography (CT),
dual-energy CT, and hyperpolarized magnetic resonance imaging (HP MRI)—to spatiotemporally track this
process in ARDS models.
In many patients, ARDS develops or worsens during mechanical ventilation, in part because the excessive
heterogeneity of lung inflation caused by ventilation worsens lung injury. For this reason, we study imaging
methodologies which predict progression of experimental lung injury during mechanical ventilation by
measuring regional inflation and perfusion. Our primary hypothesis is that healthy areas of the lung can be
protected from inflammatory propagation by improving the regional distribution of lung inflation and perfusion,
as well as by decreasing tissue edema in injured regions. Using imaging, physiologic, and biological
methodologies, we will test this hypothesis in a large animal model of early primary lung injury from acid
aspiration. Specifically, this research will: 1) develop a methodology to predict ARDS outcomes and treatment
responses from a single set of matched inspiratory-expiratory CT scans; 2) attest to the impact of therapies
aimed at spatially containing inflammation before it becomes too severe; and 3) demonstrate that the
heterogeneous distribution of lung inflation and perfusion governs the propagation of inflammation in
pulmonary tissue.
This project will lay the foundation for future applications of imaging-based techniques to predict the risk of
severe ARDS and death, attenuate its development, and monitor the progression of lung injury and the effects
of novel treatments. The potential to contain primary lung injury in select patients at high risk of adverse
outcomes represents a major leap forward in developing effective, personalized approac...

## Key facts

- **NIH application ID:** 9984510
- **Project number:** 5R01HL137389-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Maurizio Franco Cereda
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $776,764
- **Award type:** 5
- **Project period:** 2018-07-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9984510

## Citation

> US National Institutes of Health, RePORTER application 9984510, An integrated approach to predict and improve the outcomes of lung injury (5R01HL137389-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9984510. Licensed CC0.

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