SummaryStructural heart diseases arise from complex interactions between cardiac structure, systemic biology, and environmental factors. While left ventricular dysfunction has traditionally been the central focus of cardiovascular risk assessment, the right ventricle is increasingly recognised as a key determinant of prognosis and disease progression. In particular, the extent to which right ventricular structure and function provide additional information beyond left ventricular dysfunction remains incompletely understood. The right ventricle is highly sensitive to changes in pulmonary vascular load, venous congestion, systemic inflammation, metabolic dysfunction, and multi-organ disease. Right ventricular dysfunction may therefore represent not only a cardiac phenotype, but also an integrative marker of cardio-renal and cardio-hepatic interactions. Using the NAKO cohort, we will perform a multimodal analysis integrating cardiac imaging, laboratory biomarkers, patient history, and lifestyle factors. A particular focus will be placed on multiorgan interactions between the heart, liver, and kidney. We will investigate how organ-specific dysfunction and systemic biological signatures contribute to right ventricular maladaptation and clinically relevant outcomes, including disease progression, all-cause mortality, and major adverse cardiovascular events (MACE). Insights derived from explainable AI analyses will be incorporated into structural equation models to explore potential causal pathways. This approach aims to improve understanding of the systemic drivers of right ventricular dysfunction in structural heart disease and identify potential targets for prevention and early intervention.

Keywords explainable-AI machine-learning multi-modal multi-organ right-ventricle-dysfunction structural-heart-disease

InstitutionsDeutsches Herzzentrum der Charité, Charité Universitätsmedizin Berlin