Related Experiment Video
Updated: Oct 29, 2025
Author Spotlight: Development of a Minimally Invasive Large-Animal Model for Reliable and Reproducible Cardiovascular Research
Published on: October 20, 2023
Preload & Frank-Starling curves, from textbook to bedside: Clinically applicable non-additionally invasive
Rachel Smith1, J Geoffrey Chase1, Christopher G Pretty1
1Department of Mechanical Engineering, University of Canterbury, New Zealand.
Background:
Determining physiological mechanisms leading to circulatory failure can be challenging, contributing to the difficulties in delivering effective hemodynamic management in critical care. Continuous, non-additionally invasive monitoring of preload changes, and assessment of contractility from Frank-Starling curves could potentially make it much easier to diagnose and manage circulatory failure.
Method:
This study combines non-additionally invasive model-based methods to estimate left ventricle end-diastolic volume (LEDV) and stroke volume (SV) during hemodynamic interventions in a pig trial (N = 6). Agreement of model-based LEDV and measured admittance catheter LEDV is assessed. Model-based LEDV and SV are used to identify response to hemodynamic interventions and create Frank-Starling curves, from which Frank-Starling contractility (FSC) is identified as the gradient.
Results:
Model-based LEDV had good agreement with measured admittance catheter LEDV, with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 2.2 ml [-13.8, 22.5]. Model LEDV and SV were used to identify non-responsive interventions with a good area under the receiver-operating characteristic (ROC) curve of 0.83. FSC was identified using model LEDV and SV with Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] of 0.07 [-0.68, 0.56], with FSC from admittance catheter LEDV and aortic flow probe SV used as a reference method.
Conclusions:
This study provides proof-of-concept preload changes and Frank-Starling curves could be non-additionally invasively estimated for critically ill patients, which could potentially enable much clearer insight into cardiovascular function than is currently possible at the patient bedside.
