Heart Function Analysis in Cardiac Patients with Focus on Sex-Specific Aspects.

Cardiac function is best described by investigating the pressure-volume relationships. This information permits description in terms of the ventricular volume regulation graph (VRG), estimation of systolic elastance, evaluation of lusitropic properties, and assessment of ventriculo-arterial coupling. Current techniques yield noninvasive determination of cardiac compartmental volumes, along with systolic/diastolic arterial pressure, while ventricular end-diastolic pressure can be inferred from an echocardiography-based surrogate measure. Ventricular volume is known to vary with age, as well as to be affected by intrinsic cardiac disease and abnormalities of the vascular system. Moreover, 35 years ago it has been shown in healthy adults that left ventricular volume is significantly smaller in women compared to men. This important observation has serious implications for several metrics which are routinely used in clinical practice, e.g., ejection fraction. The remarkable difference between ventricular size in men and women is also a powerful starting point for the study of aging and the investigation of interventions such as exercise. In this review we evaluate sex-specific characteristics of the VRG and the implications for various cardiac patient populations, during basal conditions and intervention such as exercise.

Exploring Guidelines for Classification of Major Heart Failure Subtypes by Using Machine Learning.

BACKGROUND: Heart failure (HF) manifests as at least two subtypes. The current paradigm distinguishes the two by using both the metric ejection fraction (EF) and a constraint for end-diastolic volume. About half of all HF patients exhibit preserved EF. In contrast, the classical type of HF shows a reduced EF. Common practice sets the cut-off point often at or near EF = 50%, thus defining a linear divider. However, a rationale for this safe choice is lacking, while the assumption regarding applicability of strict linearity has not been justified. Additionally, some studies opt for eliminating patients from consideration for HF if 40 < EF < 50% (gray zone). Thus, there is a need for documented classification guidelines, solving gray zone ambiguity and formulating crisp delineation of transitions between phenotypes. METHODS: Machine learning (ML) models are applied to classify HF subtypes within the ventricular volume domain, rather than by the single use of EF. Various ML models, both unsupervised and supervised, are employed to establish a foundation for classification. Data regarding 48 HF patients are employed as training set for subsequent classification of Monte Carlo-generated surrogate HF patients (n = 403). Next, we map consequences when EF cut-off differs from 50% (as proposed for women) and analyze HF candidates not covered by current rules. RESULTS: The training set yields best results for the Support Vector Machine method (test error 4.06%), covers the gray zone, and other clinically relevant HF candidates. End-systolic volume (ESV) emerges as a logical discriminator rather than EF as in the prevailing paradigm. CONCLUSIONS: Selected ML models offer promise for classifying HF patients (including the gray zone), when driven by ventricular volume data. ML analysis indicates that ESV has a role in the development of guidelines to parse HF subtypes. The documented curvilinear relationship between EF and ESV suggests that the assumption concerning a linear EF divider may not be of general utility over the complete clinically relevant range.

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.