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Related Concept Videos

Regulation of Stroke Volume01:27

Regulation of Stroke Volume

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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.
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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
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Pre-Procedural Guidelines for Assessing Blood Pressure01:10

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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Related Experiment Video

Updated: Jun 14, 2025

Continuous Venous-Arterial Doppler Ultrasound During a Preload Challenge
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Predicting stroke volume variation using central venous pressure waveform: a deep learning approach.

Insun Park1,2, Jae Hyon Park3,4, Bon-Wook Koo1,2

  • 1Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, 82, Gumi 173, Bundang, Seongnam, Gyeonggi 13620, Republic of Korea.

Physiological Measurement
|August 30, 2024
PubMed
Summary
This summary is machine-generated.

A deep learning model accurately predicts stroke volume variation (SVV) using central venous pressure (CVP) waveforms. This approach shows high concordance with commercial arterial pulse waveform analysis, offering a promising non-invasive monitoring tool.

Keywords:
anesthesiacentral venous pressuredeep learningfluid therapyhemodynamicsstroke volume

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Area of Science:

  • Anesthesiology and Critical Care Medicine
  • Biomedical Engineering
  • Artificial Intelligence in Healthcare

Background:

  • Accurate estimation of stroke volume variation (SVV) is crucial for guiding fluid management in critically ill patients.
  • Central venous pressure (CVP) waveforms offer a potential, less invasive source of data for SVV prediction compared to arterial methods.
  • Traditional methods for SVV estimation often rely on arterial pulse waveform analysis, which may not always be feasible or optimal.

Purpose of the Study:

  • To evaluate the predictive performance of a deep learning model for estimating SVV from CVP waveforms.
  • To compare the accuracy of the deep learning model's SVV predictions against a commercial arterial pulse waveform analysis system.

Main Methods:

  • A deep learning architecture combining Long Short-Term Memory (LSTM) and feed-forward neural networks was developed.
  • The model utilized 10-second CVP waveforms and demographic data as inputs.
  • Performance was assessed by comparing predicted SVV with SVV estimated by the commercial EV1000 device using concordance correlation coefficient (CCC).

Main Results:

  • The deep learning model achieved a high concordance correlation coefficient (CCC) of 0.993 (95% CI: 0.992-0.993) when comparing predicted SVV to EV1000-derived SVV.
  • The model was trained and tested on a large dataset comprising 224 cases with over 1.7 million CVP waveforms.
  • The model demonstrated robust performance in approximating SVV values.

Conclusions:

  • Deep learning models can effectively predict SVV using readily available CVP waveforms.
  • This CVP-based deep learning approach provides an accurate alternative to commercial arterial pulse waveform analysis for SVV estimation.
  • The findings suggest a potential for improved, non-invasive hemodynamic monitoring in clinical practice.