A damaged-informed lung model for ventilator waveforms

TL;DR

This paper introduces a physiologically anchored, data-driven lung model that captures injury dynamics from ventilator waveforms, aiming to improve ARDS management and reduce ventilator-induced lung injury.

Contribution

It develops a novel, estimable lung model based on clinical waveform features, bridging the gap between simple and complex models for better patient-specific insights.

Findings

Model accurately reproduces lung injury physiology

Estimated parameters correlate with lung damage measures

Applicable to both mouse and human ICU data

Abstract

The acute respiratory distress syndrome (ARDS) is characterized by the acute development of diffuse alveolar damage (DAD) resulting in increased vascular permeability and decreased alveolar gas exchange. Mechanical ventilation is a potentially lifesaving intervention to improve oxygen exchange but has the potential to cause ventilator-induced lung injury (VILI). A general strategy to reduce VILI is to use low tidal volume and low-pressure ventilation, but optimal ventilator settings for an individual patient are difficult for the bedside physician to determine and mortality from ARDS remains unacceptably high. Motivated by the need to minimize VILI, scientists have developed models of varying complexity to understand diseased pulmonary physiology. However, simple models often fail to capture real-world injury while complex models tend to not be estimable with clinical data, limiting the clinical utility of existing models. To address this gap, we present a physiologically anchored data-driven model to better model lung injury. Our approach relies on using clinically relevant features in the ventilator waveform data that contain information about pulmonary physiology, patients-ventilator interaction and ventilator settings. Our lung model can reproduce essential physiology and pathophysiology dynamics of differently damaged lungs for both controlled mouse model data and uncontrolled human ICU data. The estimated parameters values that are correlated with a known measure of lung physiology agree with the observed lung damage. In future endeavors, this model could be used to phenotype ventilator waveforms and serve as a basis for predicting the course of ARDS and improving patient care.