Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms

TL;DR

This study uses a computational model to investigate whether afterload increase or contractility loss primarily causes right ventricle dysfunction after lung resection, providing insights into mechanisms and potential clinical implications.

Contribution

First computational model simulating effects of lung resection on right ventricle function, isolating mechanisms like afterload increase and contractility loss.

Findings

Contractility and afterload show opposite pressure variations.

RV dysfunction may result from combined effects of contractility loss and afterload increase.

Model helps predict RV dysfunction post-lung resection.

Abstract

Functional limitation after lung resection surgery has been consistently documented in clinical studies, and right ventricle (RV) dysfunction has been hypothesized as a contributing reason. However, the mechanisms of RV dysfunction after lung resection remain unclear, particularly whether change in afterload or contractility is the main cause. This study is the first to employ a lumped parameter model to simulate the effects of lung resection. The implementation of a computational model allowed us to isolate certain mechanisms that are difficult to perform clinically. Specifically, two mechanisms were compared: afterload increase and RV contractility loss. Furthermore, our rigorous approach included local and global sensitivity analyses to evaluate the effect of parameters on our results, both individually and collectively. Our results demonstrate that contractility and afterload exhibited consistent trends across various pressure and volume conditions, pulmonary artery systolic pressure, pulmonary artery diastolic pressure, and right ventricular systolic pressure showed opposite variations. The results show that post-operative RV dysfunction may result from a combination of RV contractility loss and afterload increase. Further exploration and refinement of this first computational model presented herein will help us predict RV dysfunction after lung resection and pave the way towards improving outcomes for lung cancer patients.