Blood pressure and heart rate from the arterial blood pressure waveform can reliably estimate cardiac output in a conscious sheep model of multiple hemorrhages and resuscitation using computer machine learning approaches.
Journal:
The journal of trauma and acute care surgery
Published Date:
Oct 1, 2015
Abstract
BACKGROUND: This study was a first step to facilitate the development of automated decision support systems using cardiac output (CO) for combat casualty care. Such systems remain a practical challenge in battlefield and prehospital settings. In these environments, reliable CO estimation using blood pressure (BP) and heart rate (HR) may provide additional capabilities for diagnosis and treatment of trauma patients. The aim of this study was to demonstrate that continuous BP and HR from the arterial BP waveform coupled with machine learning (ML) can reliably estimate CO in a conscious sheep model of multiple hemorrhages and resuscitation.
Authors
Keywords
Algorithms
Animals
Arterial Pressure
Blood Pressure Determination
Calibration
Cardiac Output
Decision Support Techniques
Disease Models, Animal
Female
Heart Rate
Hemodynamics
Hemorrhage
Machine Learning
Military Medicine
Predictive Value of Tests
Pulse Wave Analysis
Resuscitation
Sheep, Domestic
Traumatology