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An early diagnosis and treatment of cardiovascular dysfunctions is a challenge due to the complexity of the underlying processes.
The cardiovascular system incorporates several controlling mechanisms acting as feedback loops over different time horizons. Because of their complex interrelationships, information-based methods such as autonomic information flow (AIF) functions promise to be useful in identifying normal and pathological behavior. Optimal adjustment between those controllers is necessary for healthy global behavior of the organism.
We investigated the question, whether there are typical relationships between short-term and long-term AIF by means of a meta-analysis of several of our own clinical studies of the prognosis of patients with multiple organ dysfunction syndrome, chronic heart failure, cardiac arrest, myocardial infarction, idiopathic dilated cardiomyopathy, and after abdominal aorta surgery.
We found a fundamental association of increased short-term randomness (decreased AIF) and decreased long-term randomness (increased AIF) due to pathology and associated with increasing risk.
A systems theoretic validation of this fundamental type of association was done by an appropriate mathematical model using a dissipative system with two feedback loops over different time horizons. The systematic simulation of an increasing collapse of the short feedback loop confirmed the inverse association between short-term and long-term information flow as a fundamental, system inherent type of re-adjustment which occurs under pathological conditions.
Assessing the interplay between mechanisms acting on different time scales by AIF functions presented in this paper may improve the understanding of complex cardiovascular control and developing therapeutic implications.
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