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
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
189-197Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.