A framework based on information dynamics to assess and dissect complex cardiovascular interactions across pathophysiological states

The assessment of cardiovascular dynamics from heart period (RR) and systolic arterial pressure (SAP) time series requires analytical tools capable of capturing the complexity and nonlinearity of the underlying regulatory mechanisms and the directionality of their interactions. This work presented and tested a methodological framework combining Mutual Information Rate (MIR) with multiple surrogate data analysis to quantify and statistically validate complexity, causality, and nonlinearity in cardiovascular dynamics. Specifically, MIR was estimated through a model-free nearest-neighbor approach and decomposed into measures of complexity (i.e., entropy rate) and causality (i.e., transfer entropy). The capability of the method to characterize cardiovascular regulation was evaluated across young normotensive healthy subjects, older healthy individuals, and post-acute myocardial infarction (AMI) patients, undergoing an orthostatic stress test. In healthy young subjects, orthostatic stress induced the expected reduction in cardiovascular entropy rate and nonlinear coupling, reflecting a regulated baroreflex response. In contrast, older adults and post-AMI patients exhibited altered dynamics already at rest and lacked the adaptive modulation observed in the young group. These results demonstrated the potential of MIR decomposition, supported by surrogate analysis, as a robust framework to characterize the modulation of cardiovascular control mechanisms in response to orthostatic stress and their alterations across pathophysiological conditions.