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Numerical simulation tool for predicting the impact of bronchoscopy on ventilation, supporting clinical decision-making by guiding ventilator settings to reduce dynamic hyperinflation and hypoventilation. Includes Python script and a browser-based interface.

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Bronchoscopy

Numerical simulation tool for predicting the impact of endotracheal tube diameter and bronchoscopy on pressure and flow patterns during mechanical ventilation. Includes a Python script and a browser-based interface.

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Abstract

Bronchoscopy in mechanically ventilated patients is performed by passing a bronchoscope through the endotracheal tube (ETT), which substantially increases airflow resistance and may compromise ventilation. Here, we quantify the nonlinear, flow-dependent resistance of ETTs with and without a bronchoscope by analyzing pressure–flow relationships across multiple tube–bronchoscope configurations. We find that with bronchoscope insertion, tube resistance increases with the inverse fifth power of the effective tube diameter, defined as the diameter of a circular tube with the same cross-sectional area as the remaining lumen. Using an intensive care ventilator in combination with an active lung simulator, we demonstrate that the increased resistance during bronchoscopy causes dynamic hyperinflation and intrinsic positive end-expiratory pressure (PEEP) in volume-controlled modes, and reduced tidal volumes in pressure-controlled modes. Numerical simulations using a simple scaling law relating resistance to effective tube diameter accurately reproduce the observed impairments. This demonstrates that the impact of tube narrowing during bronchoscopy can be reliably predicted from ventilator settings and patient respiratory mechanics. We present a predictive model that allows clinicians to anticipate and manage ventilation impairments, supporting evidence-based selection of endotracheal tubes and bronchoscopes. In addition, we provide proof of principle that combining pressure-controlled ventilation with automatic tube compensation can fully prevent these impairments, pointing to a technically feasible solution to an underrecognized clinical problem.

Authors

Ben Fabry1, Navid Bonakdar1, Christian Kuster1, Johannes Bartl1, Saskia Balling2, Frederick Krischke2, Roland Francis2

1 Department of Physics, Friedrich-Alexander University Erlangen-Nürnberg, Germany
2 Clinic for Anesthesiology, University Clinics Erlangen, Germany

Usage

You can run the simulation either locally or view it hosted online

Option 1: View online (recommended)

Option 2: Run locally

git clone https://github.com/fabrylab/Bronchoscopy.git

A. HTML / JS Version

Open index.html in any modern web browser (e.g., by double-clicking the file).

B. Python Version

  • Install dependencies if needed
pip install -r requirements.txt
  • Adapt patient parameter in the file

  • Run the script with python3

python simulation.py

License

MIT License

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Numerical simulation tool for predicting the impact of bronchoscopy on ventilation, supporting clinical decision-making by guiding ventilator settings to reduce dynamic hyperinflation and hypoventilation. Includes Python script and a browser-based interface.

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