P67 Detecting Preload Reduction with Machine Learning on Arterial Waveform Parameters
Background: Hemodynamic optimization of unstable patients by means of fluid resuscitation improves patient outcome, but choosing the correct amount of fluid can be difficult. Too little fluid may not ensure adequate perfusion whereas too much fluid is associated with increased mortality. Static para...
Main Authors: | Björn van der Ster, Marije Wijnberge, Marthe Huntelaar, Job de Haan, Koen van der Sluijs, Denise Veelo, Berend Westerhof |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2020-02-01
|
Series: | Artery Research |
Online Access: | https://www.atlantis-press.com/article/125934577/view |
Similar Items
-
The Effect of Intermittent versus Continuous Non-Invasive Blood Pressure Monitoring on the Detection of Intraoperative Hypotension, a Sub-Study
by: Marije Wijnberge, et al.
Published: (2022-07-01) -
Distinct morphologies of arterial waveforms reveal preload‐, contractility‐, and afterload‐deficient hemodynamic instability: An in silico simulation study
by: Marijn P. Mulder, et al.
Published: (2022-04-01) -
Immediate reduction in left ventricular ejection time following TAVI is associated with improved quality of life
by: Jimmy Schenk, et al.
Published: (2022-09-01) -
Central Hypovolemia Detection During Environmental Stress—A Role for Artificial Intelligence?
by: Björn J. P. van der Ster, et al.
Published: (2021-12-01) -
15.9 MODELLING ARTERIAL PULSE PRESSURE FROM HEART RATE DURING SYMPATHETIC ACTIVATION
by: Bjorn van der Ster, et al.
Published: (2016-11-01)