Effective arterial elastance and arterial compliance in heart failure patients with preserved ejection fraction

Left Ventricle-Arterial System Interaction in Heart Failure

Ejection fraction (EF) has been viewed as an important index in assessing the contractile state of the left ventricle (LV). However, it is frequently inadequate for the diagnosis and management of heart failure (HF), as a significant subset of HF patients have been found to have reduced EF (HFrEF) whereas others have preserved EF (HFpEF). It should be noted that the function of the LV is dependent on both preload and afterload, as well as its intrinsic contractile state. Furthermore, stroke volume (SV) is dependent on the properties of the arterial system (AS). Thus, the LV-arterial system interaction plays an important role in those patients with HF. This aspect is investigated through the analysis of the specific parameters involved in the coupling of the LV and AS. This includes contractility and the systolic/diastolic indices of the LV. Furthermore, AS afterload parameters such as vascular stiffness and arterial compliance, and their derived coupling coefficient, are also investigated. We conclude that those parameters, which relate to LV structural changes, are most appropriate in quantifying the LV-AS interaction.

The Volume Regulation Graph versus the Ejection Fraction as Metrics of Left Ventricular Performance in Heart Failure with and without a Preserved Ejection Fraction: A Mathematical Model Study

In left ventricular heart failure, often a distinction is made between patients with a reduced and a preserved ejection fraction (EF). As EF is a composite metric of both the end-diastolic volume (EDV) and the end-systolic ventricular volume (ESV), the lucidity of the EF is sometimes questioned. As an alternative, the ESV-EDV graph is advocated. This study identifies the dependence of the EF and the EDV-ESV graph on the major determinants of ventricular performance. Numerical simulations were made using a model of the systemic circulation, consisting of an atrium-ventricle valves combination; a simple constant pressure as venous filling system; and a three-element Windkessel extended with a venous system. ESV-EDV graphs and EFs were calculated using this model while varying one by one the filling pressure, diastolic and systolic ventricular elastances, and diastolic pressure in the aorta. In conclusion, the ESV-EDV graph separates between diastolic and systolic dysfunction while the EF encompasses these two pathologies. Therefore, the ESV-EDV graph can provide an advantage over EF in heart failure studies.

Characterizing heart failure in the ventricular volume domain

Heart failure (HF) may be accompanied by considerable alterations of left ventricular (LV) volume, depending on the particular phenotype. Two major types of HF have been identified, although heterogeneity within each category may be considerable. All variants of HF show substantially elevated LV filling pressures, which tend to induce changes in LV size and shape. Yet, one type of HF is characterized by near-normal values for LV end-diastolic volume (EDV) and even a smaller end-systolic volume (ESV) than in matched groups of persons without cardiac disease. Furthermore, accumulating evidence indicates that, both in terms of shape and size, in men and women, the heart reacts differently to adaptive stimuli as well as to certain pharmacological interventions. Adjustments of ESV and EDV such as in HF patients are associated with (reverse) remodeling mechanisms. Therefore, it is logical to analyze HF subtypes in a graphical representation that relates ESV to EDV. Following this route, one may expect that the two major phenotypes of HF are identified as distinct entities localized in different areas of the LV volume domain. The precise coordinates of this position imply unique characteristics in terms of the actual operating point for LV volume regulation. Evidently, ejection fraction (EF; equal to 1 minus the ratio of ESV and EDV) carries little information within the LV volume representation. Thus far, classification of HF is based on information regarding EF combined with EDV. Our analysis shows that ESV in the two HF groups follows different patterns in dependency of EDV. This observation suggests that a superior HF classification system should primarily be founded on information embodied by ESV.