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Related Concept Videos

Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

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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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Neural Regulation of Blood Pressure01:18

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The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
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Cardiac Output and Stroke Volume01:11

Cardiac Output and Stroke Volume

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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...
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Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

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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
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...
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Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
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Assessment of blood pressure in brachial artery(two-step method)01:23

Assessment of blood pressure in brachial artery(two-step method)

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Measuring blood pressure is a fundamental skill in healthcare that aids in diagnosing and monitoring hypertension and other cardiovascular conditions. An aneroid sphygmomanometer, commonly used in clinical settings, offers a manual and precise method for blood pressure measurement. The technique for using this instrument involves specific steps that must be carefully executed to ensure accuracy. The following detailed description outlines a two-step technique for assessing blood pressure using...
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Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural

Hyun-Lim Yang1,2, Chul-Woo Jung1,3, Seong Mi Yang1,3

  • 1Department of Anesthesiology and Pain Medicine, Seoul National University Hospital, Seoul, Republic of Korea.

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A new open-source algorithm using deep learning improves arterial pressure-based cardiac output (APCO) estimation. This AI-driven method demonstrated superior accuracy compared to existing commercial devices, enhancing patient care.

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Area of Science:

  • Cardiovascular physiology
  • Medical artificial intelligence
  • Hemodynamic monitoring

Background:

  • Arterial pressure-based cardiac output (APCO) offers a less invasive alternative to pulmonary artery catheters (PAC) for estimating cardiac output.
  • Existing APCO devices have reported inaccuracies, limiting their clinical utility.
  • Proprietary algorithms hinder research and development for APCO accuracy improvements.

Purpose of the Study:

  • To develop and validate an open-source APCO algorithm utilizing convolutional neural networks (CNNs) and transfer learning.
  • To enhance the accuracy of non-invasive cardiac output monitoring.
  • To provide a more reliable tool for clinical hemodynamic management.

Main Methods:

  • A retrospective study analyzed intraoperative bio-signal data from a university hospital cohort.
  • A CNN model was trained using arterial pressure waveforms to predict stroke volume (SV).
  • Transfer learning involved pretraining on commercial APCO device SV and fine-tuning with PAC-derived SV, with performance evaluated against PAC SV.

Main Results:

  • The study included 2057 surgical cases (1958 training, 99 testing).
  • The deep learning model achieved mean absolute SV errors of 14.5 mL (overall), 10.2 mL (cardiac surgery), and 17.4 mL (liver transplantation).
  • The deep learning model significantly outperformed the commercial FloTrac device (P<.001) in accuracy.

Conclusions:

  • The developed deep learning-based APCO algorithm demonstrates superior performance over commercial devices.
  • This open-source approach offers a promising avenue for accurate cardiac output estimation.
  • Further refinement of this algorithm could significantly aid clinical practice and optimize care for high-risk patients.