Why the study?
Instruments that quantify central blood volume are not readily available, making it difficult to detect central hypovolemia and guide treatment during environmental stress.
Does artificial intelligence improve the early detection and prediction of central hypovolemia and hypotension compared to traditional monitoring?
Does artificial intelligence improve the early detection and prediction of central hypovolemia and hypotension compared to traditional monitoring?
Answers are grounded in this paper and cite their sources.
AI and machine-learning algorithms offer a promising approach to predict central hypovolemia and intraoperative hypotension before they manifest clinically, potentially improving patient management.
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 from the chest to the lower parts of the body elicits a fall in central blood volume (CBV), which corresponds to the fraction of thoracic blood volume directly available to the left ventricle. The reduction in CBV and stroke volume (SV) in response to postural stress, post-exercise, or to blood loss results in reduced left ventricular filling, which may manifest as orthostatic intolerance. When termination of exercise removes the leg muscle pump function, CBV is no longer maintained. The resulting imbalance between a reduced cardiac output (CO) and a still enhanced peripheral vascular conductance may provoke post-exercise hypotension (PEH). Instruments that quantify CBV are not readily available and to express which magnitude of the CBV in a healthy subject should remains difficult. In the physiological laboratory, the CBV can be modified by making use of postural stressors, such as lower body "negative" or sub-atmospheric pressure (LBNP) or passive head-up tilt (HUT), while quantifying relevant biomedical parameters of blood flow and oxygenation. Several approaches, such as 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 (AI) 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 offer great relevance for healthy subjects and clinical populations.
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Ster et al. (2021) conducted a review in Central hypovolemia and intraoperative hypotension. Artificial intelligence and machine-learning algorithms vs. Standard monitoring was evaluated.
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