Predicting hypotension in perioperative and intensive care medicine.

artificial intelligence blood pressure cardiovascular dynamics hemodynamic monitoring hypotension prediction index machine learning

Journal

Best practice & research. Clinical anaesthesiology
ISSN: 1878-1608
Titre abrégé: Best Pract Res Clin Anaesthesiol
Pays: Netherlands
ID NLM: 101121446

Informations de publication

Date de publication:
Jun 2019
Historique:
received: 05 04 2019
accepted: 05 04 2019
entrez: 5 10 2019
pubmed: 5 10 2019
medline: 23 2 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 31582098
pii: S1521-6896(19)30009-6
doi: 10.1016/j.bpa.2019.04.001
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

189-197

Informations de copyright

Copyright © 2019 Elsevier Ltd. All rights reserved.

Auteurs

Bernd Saugel (B)

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.

Karim Kouz (K)

Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany. Electronic address: karim.kouz@gmail.com.

Phillip Hoppe (P)

Department of Anesthesiology, Center of Anesthesiology and Intensive Care Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246, Hamburg, Germany. Electronic address: p.hoppe@uke.de.

Kamal Maheshwari (K)

Departments of General Anesthesiology and Outcomes Research, Anesthesiology Institute, Cleveland Clinic, 9500 Euclid Avenue - E31, Cleveland, OH, 44195, USA. Electronic address: maheshk@ccf.org.

Thomas W L Scheeren (TWL)

Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, P.O. Box 33.001, 9700 RB, Groningen, the Netherlands. Electronic address: t.w.l.scheeren@umcg.nl.

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