Abstract
Hemodynamic instability is frequently present in critically ill patients, primarily caused by a decreased preload, contractility, and/or afterload. We hypothesized that peripheral arterial blood pressure waveforms allow to differentiate between these underlying causes. In this in-silico experimental study, a computational cardiovascular model was used to simulate hemodynamic instability by decreasing blood volume, left ventricular contractility or systemic vascular resistance, and additionally adaptive and compensatory mechanisms. From the arterial pressure waveforms, 45 features describing the morphology were discerned and a sensitivity analysis and principal component analysis were performed, to quantitatively investigate their discriminative power. During hemodynamic instability, the arterial waveform morphology changed distinctively, for example, the slope of the systolic upstroke having a sensitivity of 2.02 for reduced preload, 0.80 for reduced contractility, and -0.02 for reduced afterload. It was possible to differentiate between the three underlying causes based on the derived features, as demonstrated by the first two principal components explaining 99% of the variance in waveforms. The features with a high correlation coefficient (>0.25) to these principal components are describing the systolic up- and downstroke, and the anacrotic and dicrotic notches of the waveforms. In this study, characteristic peripheral arterial waveform morphologies were identified that allow differentiation between deficits in preload, contractility, and afterload causing hemodynamic instability. These findings are confined to an in silico simulation and warrant further experimental and clinical research in order to prove clinical usability in daily practice.