Validation of a Computational Respiratory System Model for Mechanical Ventilation
Abstract
Computational modeling and simulation have emerged as powerful tools for assessing medical device performance and safety, particularly in silico clinical trials (ISCTs) for automated medical systems. In ventilation, where gas exchange, respiratory mechanics, and patient-ventilator interaction must be managed under evolving pathophysiology, clinical translation of automated control strategies remains slow and resource-intensive. These challenges are particularly relevant for AI-based therapy-control systems, whose data-driven decision-making must be evaluated across heterogeneous and safety-critical patient states that may be sparsely represented in clinical datasets. Mechanistic, physiology-based models provide a complementary and interpretable environment for testing such scenarios. This paper applies a standards-aligned framework for credibility assessment of a computational respiratory model, demonstrated using an automated weaning case study. The framework operationalizes ASME V&V 40 and FDA principles within a structured validation workflow. The model integrates respiratory mechanics, gas exchange, respiratory control, and a ventilator representation, with validation under a defined context of use and explicit questions of interest. Model credibility is assessed through calibration, physiological plausibility, population-based evaluation, and reproduction of emergent behavior. All model requirements derived from the intended context of use are addressed, and gaps are transparently reported. The resulting credibility argument supports applicability of the model for medium-low-risk preclinical ISCTs of automated weaning strategies. Residual limitations relate to the extent of in vivo evidence, population representativeness, and external validation. The validation procedure provides a blueprint for validation of this and similar models in mechanical ventilation and related use cases.
Related
- Combined Approach with Ultrasound and Pressure–Volume Loops to Manage Mechanical Ventilation in Acute Hypoxemic Respiratory Failure
- Intubation and mechanical ventilation lead to an extensive expansion of VAP-associated bacterial species in respiratory secretions of ventilated pigs
- Expert Consensus-Based Clinical Practice Guidelines for Care and Weaning Procedures in Tracheostomized ICU Patients after Invasive Mechanical Ventilation: A Joint Statement by the Intensive Care Physiotherapy Society (SKR) and the French Intensive Care Society (SRLF)
- Impact of an early extended rehabilitation programme combining individually tailored nutrition and physical activity on patient outcomes after invasive mechanical ventilation and vasopressor therapy in the ICU: design and conduct of a multicentre, randomised, controlled, open-label, assessor-blinded trial (NUTRIREA-4)
- Severe pneumonia-associated lung injury: gut-lung-immune-metabolic connections in the critically ill host environment-microecology, barrier function, and mechanical ventilation
BibTeX
@misc{hennigs2026validation,
title = {{Validation of a Computational Respiratory System Model for Mechanical Ventilation}},
author = {Carlotta Hennigs and Charlott Danielson and Franziska Bilda and Dimitrios Karachalios and Niklas Hackelberg and Helene Selpien and Georg Männel and Dirk Schädler and Folker Spitzenberger and Philipp Rostalski},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2607.06210},
eprint = {2607.06210},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2607.06210},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Validation of a Computational Respiratory System Model for Mechanical Ventilation
AU - Carlotta Hennigs
AU - Charlott Danielson
AU - Franziska Bilda
AU - Dimitrios Karachalios
AU - Niklas Hackelberg
AU - Helene Selpien
AU - Georg Männel
AU - Dirk Schädler
AU - Folker Spitzenberger
AU - Philipp Rostalski
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2607.06210
UR - https://arxiv.org/abs/2607.06210
AB - Computational modeling and simulation have emerged as powerful tools for assessing medical device performance and safety, particularly in silico clinical trials (ISCTs) for automated medical systems. In ventilation, where gas exchange, respiratory mechanics, and patient-ventilator interaction must be managed under evolving pathophysiology, clinical translation of automated control strategies remains slow and resource-intensive. These challenges are particularly relevant for AI-based therapy-control systems, whose data-driven decision-making must be evaluated across heterogeneous and safety-critical patient states that may be sparsely represented in clinical datasets. Mechanistic, physiology-based models provide a complementary and interpretable environment for testing such scenarios. This paper applies a standards-aligned framework for credibility assessment of a computational respiratory model, demonstrated using an automated weaning case study. The framework operationalizes ASME V&V 40 and FDA principles within a structured validation workflow. The model integrates respiratory mechanics, gas exchange, respiratory control, and a ventilator representation, with validation under a defined context of use and explicit questions of interest. Model credibility is assessed through calibration, physiological plausibility, population-based evaluation, and reproduction of emergent behavior. All model requirements derived from the intended context of use are addressed, and gaps are transparently reported. The resulting credibility argument supports applicability of the model for medium-low-risk preclinical ISCTs of automated weaning strategies. Residual limitations relate to the extent of in vivo evidence, population representativeness, and external validation. The validation procedure provides a blueprint for validation of this and similar models in mechanical ventilation and related use cases.
ER -