Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regulation of Stroke Volume01:27

Regulation of Stroke Volume

3.4K
The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
Preload refers to the degree of stretch on the heart before it contracts. It's analogous to the stretching of a rubber band; the more it's stretched, the more forcefully it snaps back. This concept is encapsulated in the Frank-Starling law of the...
3.4K
Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

Cardiac Output II: Effect of Stroke Volume on Cardiac Output

1.3K
Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
Preload
Preload refers to the initial elongation of the cardiac myocytes before contraction and is related to the volume of blood filling the heart at the end of diastole, or end-diastolic volume. The...
1.3K
Cardiac Output and Stroke Volume01:11

Cardiac Output and Stroke Volume

3.2K
Cardiac output (CO) is an integral aspect of human physiology, reflecting the heart's efficiency and responsiveness to the body's needs. It represents the volume of blood that the left or right ventricle ejects into the aorta or pulmonary trunk each minute. The CO is calculated by multiplying the heart rate (HR)—the number of heartbeats per minute—by the stroke volume (SV)—the amount of blood pumped out with each heartbeat.
In an average resting adult male, the typical cardiac...
3.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Role and modulation of perioperative stress response to improve outcomes.

Intensive care medicine experimental·2026
Same author

Electroencephalographic Monitoring in the Recovery Room for Identification of Patients at Risk for Postoperative Delirium.

Anesthesiology·2026
Same author

Clinical Criteria for the Definition of Refractory Septic Shock: A Joint Delphi Consensus from the Society of Critical Care Medicine (SCCM) and European Society of Intensive Care Medicine (ESICM).

Critical care medicine·2026
Same author

Clinical criteria for the definition of refractory septic shock: a joint Delphi consensus from the Society of Critical Care Medicine (SCCM) and European Society of Intensive Care Medicine (ESICM).

Intensive care medicine·2026
Same author

EEG Dynamics in Children Before, During and After General Anesthesia.

Paediatric anaesthesia·2026
Same author

Correction: Perioperative micro‑arterial function and extravasation in cytoreductive ovarian cancer surgery: an observational study.

Intensive care medicine experimental·2026

Related Experiment Video

Updated: Sep 3, 2025

Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test
10:21

Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test

Published on: September 22, 2023

702

Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental

Matthias Stetzuhn1, Timo Tigges2, Alexandru Gabriel Pielmus2

  • 1Department of Anaesthesiology and Operative Intensive Care Medicine (CCM, CVK), Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a machine-learning model using electrical cardiometry (EC) to non-invasively detect decreases in stroke volume index (SVI). The EC model accurately predicts hypovolaemia and compensated shock, outperforming traditional vital signs.

Keywords:
compensated shockelectrical cardiometryhypovolaemialower body negative pressure chambermachine learningprediction model

More Related Videos

Real-time Pressure-volume Analysis of Acute Myocardial Infarction in Mice
07:28

Real-time Pressure-volume Analysis of Acute Myocardial Infarction in Mice

Published on: July 2, 2018

9.2K
Cardiac Response to β-Adrenergic Stimulation Determined by Pressure-Volume Loop Analysis
08:05

Cardiac Response to β-Adrenergic Stimulation Determined by Pressure-Volume Loop Analysis

Published on: May 19, 2021

3.7K

Related Experiment Videos

Last Updated: Sep 3, 2025

Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test
10:21

Evaluation of Hydration Status by Bioelectrical Impedance Vector Analysis in Patients with Ischemic Heart Disease Undergoing Exercise Stress Test

Published on: September 22, 2023

702
Real-time Pressure-volume Analysis of Acute Myocardial Infarction in Mice
07:28

Real-time Pressure-volume Analysis of Acute Myocardial Infarction in Mice

Published on: July 2, 2018

9.2K
Cardiac Response to β-Adrenergic Stimulation Determined by Pressure-Volume Loop Analysis
08:05

Cardiac Response to β-Adrenergic Stimulation Determined by Pressure-Volume Loop Analysis

Published on: May 19, 2021

3.7K

Area of Science:

  • Cardiovascular Physiology
  • Medical Technology
  • Data Science in Medicine

Background:

  • Compensated shock and hypovolaemia are often undetected, leading to patient deterioration.
  • Current detection methods lack accuracy and non-invasiveness for early identification.
  • Automated, non-invasive monitoring is crucial for perioperative and critically ill patients.

Purpose of the Study:

  • To develop a predictive model for stroke volume index (SVI) decrease using electrical cardiometry (EC).
  • To assess the model's efficacy in detecting simulated central hypovolaemia.
  • To compare the predictive power of EC variables against traditional vital signs.

Main Methods:

  • Experimental study involving 30 healthy male volunteers.
  • Central hypovolaemia simulated using a lower body negative pressure (LBNP) chamber.
  • Stroke volume index (SVI) assessed via transthoracic echo (SVI-TTE) as reference.
  • Machine-learning algorithm developed using EC variables.
  • Comparison of model performance against heart rate and systolic arterial pressure.

Main Results:

  • Simulated hypovolaemia caused significant SVI-TTE decline with stable vital signs.
  • The EC-based model demonstrated superior predictive ability for SVI decrease (AUC: 0.91).
  • EC model significantly outperformed heart rate (AUC: 0.83) and systolic arterial pressure (AUC: 0.82).

Conclusions:

  • EC variables, analyzed by machine learning, can accurately predict relevant SVI decreases.
  • This approach offers a potential automated, non-invasive method for indicating hypovolaemia and compensated shock.
  • The developed model shows promise for early detection and management of critical hemodynamic conditions.