Central Hypovolemia Detection During Environmental Stress—A Role for Artificial Intelligence? - van der Ster, Björn J. P.; Kim, Yu-Sok; Westerhof, Berend E.; van Lieshout, Johannes J. (Frontiers in Physiology, 2021)
The first step to exercise is preceded by the required assumption of the upright body position which itself involves physical activity. The gravitational displacement of blood elicits a fall in central blood volume (CBV) which corresponds to the fraction of thoracic blood volume directly available to the left ventricle. Dehydration-induced reductions in CBV and stroke volume at rest and during exercise result in reduced left ventricular filling. When termination of exercise removes the leg muscle pump CBV is no longer maintained. The resulting imbalance between cardiac output too low and peripheral vascular conductance too high may provoke post-exercise hypotension. Instruments to quantify CBV are not readily available and to express which magnitude the CBV in a healthy subject should have and how to quantify it remains difficult. In the physiological laboratory the CBV can be modified by making use of postural stressors like lower body negative pressure or passive head-up tilt while quantifying relevant biomedical parameters of blood flow and oxygenation. Several approaches including wearable sensors and advanced machine-learning techniques have been followed in an attempt to improve methodologies for better prediction of outcomes and to guide treatment in civil patients and on the battlefield. In the recent decade efforts have been made to develop algorithms and apply artificial intelligence in the field of hemodynamic monitoring. Advances in quantifying and monitoring CBV during environmental stress from exercise to hemorrhage and understanding the analogy between postural stress and central hypovolemia during anesthesia offers great relevance for healthy subjects and clinical populations.