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 Experiment Video

Updated: Oct 13, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K

A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury.

Shara I Feld1, Daniel S Hippe2, Ljubomir Miljacic2

  • 1Anesthesiology and Pain Medicine, University of Washington.

Journal of Neurosurgical Anesthesiology
|November 11, 2021
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

ctDNA or Merkel Virus Antibodies for Surveillance of Merkel Cell Carcinoma Recurrence.

JAMA dermatology·2026
Same author

Loss of the USP22 deubiquitylase confers resistance to chemotherapy in small cell lung cancer.

Nature communications·2026
Same author

Deep Learning-Based Synthetic Contrast-Enhanced Breast MRI for Monitoring Response to Neoadjuvant Therapy.

Cancers·2026
Same author

Mature Outcomes and Patterns of Failure in the Phase II FLARE-RT Trial of Biological Image-guided and Risk-Adaptive Chemoradiation for Unresectable NSCLC.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Lung cancer screening care pathways in community settings: an examination of 3 healthcare systems.

Annals of the American Thoracic Society·2026
Same author

Concurrent Chemotherapy With Adjuvant Radiation for Patients With High-risk Locally Advanced Breast Cancer: Safety and Outcomes.

Clinical breast cancer·2026

Machine learning models can predict intraoperative hypotension in traumatic brain injury (TBI) patients using real-time data. Recent trends in mean arterial pressure are key predictors, improving patient outcomes.

Area of Science:

  • Neurosurgery
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Traumatic brain injury (TBI) is a significant cause of mortality and disability.
  • Hypotension episodes worsen TBI patient outcomes.
  • Real-time prediction of intraoperative hypotension in TBI patients is crucial.

Purpose of the Study:

  • To model the real-time risk of intraoperative hypotension in TBI patients.
  • To compare machine learning and traditional modeling techniques.
  • To identify key predictors for intraoperative hypotension.

Main Methods:

  • Utilized data from 1005 TBI patients undergoing neurosurgery.
  • Defined intraoperative hypotension as mean arterial pressure <65 mmHg for 5+ minutes.

More Related Videos

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.3K
A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury
06:18

A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury

Published on: March 26, 2019

9.2K

Related Experiment Videos

Last Updated: Oct 13, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K
Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury
07:21

Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

Published on: May 27, 2022

3.3K
A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury
06:18

A Bedside, Single Burr Hole Approach to Multimodality Monitoring in Severe Brain Injury

Published on: March 26, 2019

9.2K
  • Developed logistic regression and gradient boosting models using pre-operative and intraoperative data.

    • Main Results:

    • Intraoperative models demonstrated good predictive performance (AUCs 0.80-0.83).
    • Gradient boosting model showed superior performance.
    • Preoperative models had poor predictive power.
    • Recent trends in mean arterial pressure were the most significant predictors.

    Conclusions:

    • A model for real-time prediction of intraoperative hypotension in TBI patients was developed.
    • Machine learning techniques and streamlined features from patient monitor data are effective.
    • This approach can improve patient management during neurosurgery.