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

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

6.1K
The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
6.1K
Blood Transfusion01:15

Blood Transfusion

2.1K
Blood transfusion is a critical medical procedure that saves lives and treats various medical conditions. It involves transferring blood from a donor to a recipient. This process requires a thorough understanding of the ABO blood group system and its associated antigens and antibodies.
Blood Transfusion Overview
A blood transfusion is a medical procedure used to replace blood lost due to injury, surgery, or to treat conditions such as anemia or cancer. During a transfusion, donor blood is...
2.1K

You might also read

Related Articles

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

Sort by
Same author

Defining and quantifying oxygen delivery potency of blood products.

Blood. Red cells & iron·2026
Same author

The red blood cell proteome and interactome identify a Band 3-BLVRB axis regulating hypoxic metabolic adaptation.

Blood·2026
Same author

A randomized, double-blind, controlled, parallel group study with amustaline/glutathione pathogen reduced red blood cells in regions at potential risk for Zika virus transfusion-transmitted infections (RedeS Study)-protocol for a phase 3 clinical trial.

Trials·2026
Same author

From Precision to Personalized: Catalyzing AI-Enabled Innovation in Drug Development.

Clinical and translational science·2026
Same author

Safety, Tolerability, and Pharmacokinetics of 6-Diazo-5-Oxo-L-Norleucine in Malawian Adults With and Without Malaria: A Phase 1 Dose-Escalation Clinical Trial.

The Journal of infectious diseases·2026
Same author

Flux Matters: IVIVC-Based Prediction of Occlusion Effects on Transdermal Oxybutynin.

The AAPS journal·2026

Related Experiment Video

Updated: Jan 17, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Predictors of Anemia Intolerance for Real-Time Transfusion Decision-Making During Resuscitation of Trauma Subjects: A

Mathangi Gopalakrishnan1, Jie Chen2, Rahul Goyal1

  • 1Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, MD.

Critical Care Explorations
|September 22, 2025
PubMed
Summary

Heart rate variability (HRV) can predict the need for red blood cell (RBC) transfusions in trauma patients. Combining HRV with clinical data improves prediction accuracy, aiding personalized transfusion decisions.

Keywords:
anemia intoleranceheart rate variabilitymachine learningtransfusion decision-makingtrauma critical care

More Related Videos

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

1.3K
Integrated Compensatory Responses in a Human Model of Hemorrhage
07:57

Integrated Compensatory Responses in a Human Model of Hemorrhage

Published on: November 20, 2016

13.1K

Related Experiment Videos

Last Updated: Jan 17, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

1.3K
Integrated Compensatory Responses in a Human Model of Hemorrhage
07:57

Integrated Compensatory Responses in a Human Model of Hemorrhage

Published on: November 20, 2016

13.1K

Area of Science:

  • Physiology
  • Machine Learning
  • Trauma Care

Background:

  • Red blood cell (RBC) transfusions in anemic patients require careful consideration due to potential harm.
  • Assessing oxygen delivery versus demand is crucial for managing anemia.
  • Personalized predictors are needed to identify patients who would benefit from transfusion.

Purpose of the Study:

  • To identify personalized predictors of anemia intolerance in trauma patients.
  • To evaluate the ability of heart rate variability (HRV) to predict the need for significant RBC transfusion.
  • To enhance transfusion decision-making through reliable physiological metrics.

Main Methods:

  • Retrospective cohort study of adult trauma patients in a specialized trauma resuscitation unit (TRU).
  • Analysis of electronic medical records, including electrocardiogram (ECG) tracings for HRV calculation.
  • Machine learning (random forest) models were used to predict RBC transfusion necessity.

Main Results:

  • A model combining demographic, clinical, trauma, and HRV variables achieved an AUROC of 0.86 for predicting transfusion.
  • HRV parameters alone demonstrated significant predictive performance (AUROC: 0.72).
  • Lower log-transformed very low frequency absolute power in HRV consistently predicted transfusion need.

Conclusions:

  • HRV parameters, collected early post-admission, can predict the likelihood of RBC transfusion.
  • Combining HRV with readily available clinical information improves predictive capabilities.
  • These findings support integrating HRV into personalized transfusion strategies for trauma patients.