Predicting hypotension in perioperative and intensive care medicine

TY - JOUR

T1 - Predicting hypotension in perioperative and intensive care medicine

AU - Saugel, Bernd

AU - Kouz, Karim

AU - Hoppe, Phillip

AU - Maheshwari, Kamal

AU - Scheeren, Thomas W L

N1 - Copyright © 2019 Elsevier Ltd. All rights reserved.

PY - 2019/6

Y1 - 2019/6

N2 - Blood 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.

AB - Blood 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.

U2 - 10.1016/j.bpa.2019.04.001

DO - 10.1016/j.bpa.2019.04.001

M3 - SCORING: Review article

C2 - 31582098

VL - 33

SP - 189

EP - 197

JO - BEST PRAC RES-CL ANA

JF - BEST PRAC RES-CL ANA

SN - 1521-6896

IS - 2

ER -