# Computational physiological modeling for lung-specific ventilation and perfusion management in ex vivo lung perfusion

## TL;DR

This paper introduces a computational model to better understand and manage lung function during ex vivo lung perfusion.

## Contribution

A novel computational physiological model for ex vivo lung perfusion that integrates clinical data and simulates lung mechanics and gas exchange.

## Key findings

- The model's simulations closely matched clinical measurements of oxygen pressure with an RMSE of 6.4 mmHg.
- Sensitivity analysis identified key factors influencing gas dynamics, such as shunt and dead space.
- The model provides mechanistic insights that could improve EVLP protocols and reduce lung injury.

## Abstract

Ex vivo lung perfusion (EVLP) relies on standardized ventilation and perfusion protocols to evaluate and preserve donor lungs before transplantation. Yet, these protocols overlook graft-specific physiology, leading to variable dead-space ventilation, intrapulmonary shunting, and increased lung injury.

We developed and validated a computational physiological model (CPM) of lungs on EVLP. The CPM integrates established principles of lung mechanics, gas exchange, and perfusion with clinical input data. It provides mechanistic insight into ex vivo lung physiology and quantifies intrinsic properties such as alveolar dead space and intrapulmonary shunting. Model validation combined in silico experiments to verify physiological coherence with calibration against clinical EVLP data to evaluate predictive performance.

Simulation results closely aligned with clinical measurements of left atrial partial oxygen pressure (root mean squared error (RMSE) of 6.4 mmHg). Sensitivity analysis and uncertainty quantification further elucidated key determinants of oxygen and carbon dioxide dynamics, including the inspired oxygen fraction, intrapulmonary shunt, dead space, and perfusate flow.

This CPM enhances understanding of ex vivo lung physiology, which may lead to less injurious EVLP management and support safe, extended-duration EVLP.