Model-based estimation of Frank-Starling curves at the patient bedside

Determining physiological mechanisms contributing to circulatory failure can be challenging, contributing to the difficulties of delivering effective hemodynamic management in critical care. Measured or estimated Frank-Starling curves could potentially make it much easier to assess patient response to interventions, and thus to manage circulatory failure. 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. Frank-Starling curves are created using these metrics and Frank-Starling contractility (FSC) is identified as the gradient. Bland-Altman median bias [limits of agreement (2.5th, 97.5th percentile)] are 0.14[-0.56, 0.57] for model-based FSC agreement with measured reference method FSC using admittance catheter LEDV and aortic flow probe SV. This study provides proof-of-concept Frank-Starling curves could be non-additionally invasively estimated clinically for critically ill patients to provide clearer insight into cardiovascular function than is currently possible.