# Predicting and Preventing Ventilator-Induced Lung Injury

> **NIH NIH R01** · UNIVERSITY OF COLORADO DENVER · 2023 · $568,983

## Abstract

Project Summary
Acute respiratory distress syndrome (ARDS) is a rapid onset respiratory failure that is caused by factors ranging
from pneumonia to sepsis. The impact of ARDS is substantial with more than 200,000 cases per year in the
United States and an estimated mortality rate of 40%. All ARDS patients are mechanically ventilated to overcome
the derangements in lung function caused by pulmonary edema, surfactant inactivation, and alveolar collapse.
However, this essential mechanical ventilation can cause additional ventilator-induced lung injured (VILI) through
tissue overdistension (volutrauma), the cyclic collapse and reopening of small airways and alveoli (atelectrauma),
and inflammatory effects (biotrauma). Since VILI is a risk in all ARDS patients, and a significant contributor to
ARDS mortality, improvements in ventilatory management are a key step in improving ARDS survival. However,
further refinement of ventilation protocols to reduce VILI is challenging because of differences between patients
and the changes in lung function that occur over time as ARDS worsens or resolves. Because of this inter- and
intra-patient variability, ventilation that is beneficial in one person can be harmful in another. To overcome this
challenge, we postulate that ventilation should be guided using a VILI cost function that provides real-time
feedback of ventilation safety by describing the amount of VILI that is occurring. Our study will define such VILI
cost functions based on the changes in lung function, structure, and inflammation that are the result of injurious
ventilation. Using the cost function as a guide, the optimally safe ventilation for each patient could be determined
by manually adjusting the ventilator settings. However, given the large number of permutations of ventilation
adjustments this is not a practical approach. Instead, we will develop a mathematical model to predict optimal
ventilation for each patient. These simulations will be personalized by fitting to real time pressure-flow
measurements and then used to find the ventilation pattern that minimizes the VILI Cost Function. The predicted
optimally safe ventilation will then be applied, and the process repeated to account for changes in lung function
over time. The potential benefits of the proposed study are substantial. The VILI cost functions we define will
provide an essential measurement of ventilation safety. Our innovative approach to optimize lung-protective
ventilation using predictive models may lead to decreased ARDS mortality by protecting the injured lung while,
at the same time, reducing provider workload. The proposed system also represents a paradigm shift in the way
that ventilation strategies are established. Instead of testing a strategy in animal models and then in the
heterogeneous ARDS patient population, where the effect may be beneficial to some patients and harmful to
others, focus may be directed towards identifying algorithms that predict and prevent VI...

## Key facts

- **NIH application ID:** 10543770
- **Project number:** 5R01HL151630-03
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Bradford J Smith
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $568,983
- **Award type:** 5
- **Project period:** 2021-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10543770

## Citation

> US National Institutes of Health, RePORTER application 10543770, Predicting and Preventing Ventilator-Induced Lung Injury (5R01HL151630-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10543770. Licensed CC0.

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