Developing a Digital Twin of the Cardiopulmonary System in a Mouse: Inferring Hemodynamics from Sparse Measurements.

Authors

Vitaly O Kheyfets, Kenzo Ichimura, Paul M Heerdt, Mengqian Zhang, Ella Lyon, Kurt R Stenmark, Edda Spiekerkoetter

Published in

Annals of biomedical engineering. Aug 04, 2026. Epub Aug 04, 2026.

Abstract

The use of rodent models in the study of cardiopulmonary disease is widespread, but comprehensive functional and hemodynamic characterization of the cardiopulmonary system in each rodent is often impractical and may require integration of multimodal measurements. The objective of this study is to evaluate a 0D cardiopulmonary model for simulating mouse-specific physiology and inferring individualized parameters.
We developed a 0D model of the cardiopulmonary axis, bounded by the right and left atria, incorporating 13 unknown parameters representing vascular impedance, RV pressure (RVP)-volume dynamics, and tricuspid/pulmonic valve regurgitation. The model fitted RVP and volume data from 28 mice across four surgical conditions, including two scenarios of mechanically induced RVP overload. Sensitivity and identifiability analyses revealed a reduced subset of nine parameters that were structurally and practically identifiable.
Optimization of the identifiable parameters adequately reproduced RVP waveforms (r = 0.94 for maximum dP/dt with LOA < 1 mmHg s^-1) and volume extrema (r = 0.97 for EDV, r = 0.98 for ESV with LOA for both measurements ~ 5 μL), while one non-physiological case was excluded from the analysis. As expected, mice with RVP overload exhibited elevated inferred Ees, Eed, and RAP. Also, model-inferred Ees was moderately correlated with single-beat estimates of RV contractility (r = 0.68, p < 0.01).
This study demonstrates that subject-specific computational modeling enables inference of ventricular function and pulmonary hemodynamics from RV pressure and volume data. This approach provides access to otherwise unmeasurable quantities and lays the groundwork for digital twins to support disease tracking and in silico testing of interventions.

PMID:
42550388
Bibliographic data and abstract were imported from PubMed on 04 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.