• Bernd Saugel, Karim Kouz, Phillip Hoppe, Kamal Maheshwari, and Scheeren Thomas W L TWL Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 33.001, 9700 RB, Groningen, the.
• Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany; Outcomes Research Consortium, Cleveland, OH, USA. Electronic address: bernd.saugel@gmx.de.
• Best Pract Res Clin Anaesthesiol. 2019 Jun 1; 33 (2): 189-197.

AbstractBlood pressure is the main determinant of organ perfusion. Hypotension is common in patients having surgery and in critically ill patients. The severity and duration of hypotension are associated with hypoperfusion and organ dysfunction. Hypotension is mostly treated reactively after low blood pressure values have already occurred. However, prediction of hypotension before it becomes clinically apparent would allow the clinician to treat hypotension pre-emptively, thereby reducing the severity and duration of hypotension. Hypotension can now be predicted minutes before it actually occurs from the blood pressure waveform using machine-learning algorithms that can be trained to detect subtle changes in cardiovascular dynamics preceding clinically apparent hypotension. However, analyzing the complex cardiovascular system is a challenge because cardiovascular physiology is highly interdependent, works within complicated networks, and is influenced by compensatory mechanisms. Improved hemodynamic data collection and integration will be a key to improve current models and develop new hypotension prediction models.Copyright © 2019 Elsevier Ltd. All rights reserved.

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