Computational modeling of ventricular-ventricular interactions suggest a role in clinical conditions involving heart failure.
Computational modeling of ventricular-ventricular interactions suggest a role in clinical conditions involving heart failure.
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DOI:
10.3389/fphys.2023.1231688
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发表时间:
2023
影响因子:
4
通讯作者:
中科院分区:
文献类型:
--
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Introduction: The left (LV) and right (RV) ventricles are linked biologically, hemodynamically, and mechanically, a phenomenon known as ventricular interdependence. While LV function has long been known to impact RV function, the reverse is increasingly being realized to have clinical importance. Investigating ventricular interdependence clinically is challenging given the invasive measurements required, including biventricular catheterization, and confounding factors such as comorbidities, volume status, and other aspects of subject variability. Methods: Computational modeling allows investigation of mechanical and hemodynamic interactions in the absence of these confounding factors. Here, we use a threesegment biventricular heart model and simple circulatory system to investigate ventricular interdependence under conditions of systolic and diastolic dysfunction of the LV and RV in the presence of compensatory volume loading. We use the end-diastolic pressure-volume relationship, end-systolic pressure-volume relationship, Frank Starling curves, and cardiac power output as metrics. Results: The results demonstrate that LV systolic and diastolic dysfunction lead to RV compensation as indicated by increases in RV power. Additionally, RV systolic and diastolic dysfunction lead to impaired LV filling, interpretable as LV stiffening especially with volume loading to maintain systemic pressure. Discussion: These results suggest that a subset of patients with intact LV systolic function and diagnosed to have impaired LV diastolic function, categorized as heart failure with preserved ejection fraction (HFpEF), may in fact have primary RV failure. Application of this computational approach to clinical data sets, especially for HFpEF, may lead to improved diagnosis and treatment strategies and consequently improved outcomes.
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