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

Cardiomyopathy V: Interprofessional Care01:29

Cardiomyopathy V: Interprofessional Care

33
Managing cardiomyopathy involves addressing underlying or precipitating causes, treating heart failure with medications, and implementing dietary changes and a balanced exercise and rest regimen.Lifestyle ModificationsCardiomyopathy patients should adopt a low-sodium diet to reduce fluid retention and manage heart failure. A personalized exercise and rest plan helps maintain physical fitness without overstraining the heart. Avoiding alcohol and tobacco is essential to prevent further damage to...
33
Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

27
Additional therapies for treating patients with heart failure (HF) may include procedural interventions, supplemental oxygen, the management of sleep disorders, and nutritional therapy.Procedural InterventionsImplantable Cardioverter-Defibrillator: For patients at risk of life-threatening arrhythmias due to severe left ventricular dysfunction, an Implantable Cardioverter-Defibrillator (ICD) can detect and terminate these arrhythmias, preventing sudden cardiac death and improving survival rates.
27

You might also read

Related Articles

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

Sort by
Same author

Comparison of Inpatient End-of-Life Care Intensity Between Heart Failure and Cancer.

Journal of cardiac failure·2026
Same author

Paraganglioma and cardiomyopathy leading to cardiogenic shock: a case report.

ESC heart failure·2026
Same author

Incidence and risk factors for malignancy after heart transplantation- Analysis of the UNOS Registry.

American heart journal·2025
Same author

Noninvasive Pulmonary Capillary Wedge Pressure Estimation in Heart Failure Patients With the Use of Wearable Sensing and AI.

JACC. Heart failure·2025
Same author

Barriers and Facilitators to Heart Failure Guideline-Directed Medical Therapy in an Integrated Health System and Federally Qualified Health Centers: A Thematic Qualitative Analysis.

Journal of general internal medicine·2025
Same author

SEISMIC-HF 1: key findings from AHA24 and implications for remote cardiac monitoring.

Heart failure reviews·2025

Related Experiment Video

Updated: Sep 10, 2025

Use of a Percutaneous Ventricular Assist Device/Left Atrium to Femoral Artery Bypass System for Cardiogenic Shock
07:39

Use of a Percutaneous Ventricular Assist Device/Left Atrium to Femoral Artery Bypass System for Cardiogenic Shock

Published on: August 16, 2021

3.7K

Risk prediction model for waitlist mortality in patients with left ventricular assist devices.

Anjan Tibrewala1,2, Duc Thinh Pham2,3, Mo Hu1

  • 1Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.

JHLT Open
|August 21, 2025
PubMed
Summary

A new risk model predicts waitlist mortality for patients with left ventricular assist devices (LVAD) awaiting heart transplantation (HT). This tool aids in prioritizing patients for HT, addressing the challenge of limited donor organs.

Keywords:
Heart failureMachine learningMortality

More Related Videos

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.6K
Insertion, Maintenance, and Removal of the Percutaneous Dual Lumen Cannula Right Ventricular Assist Device
07:41

Insertion, Maintenance, and Removal of the Percutaneous Dual Lumen Cannula Right Ventricular Assist Device

Published on: July 20, 2022

2.0K

Related Experiment Videos

Last Updated: Sep 10, 2025

Use of a Percutaneous Ventricular Assist Device/Left Atrium to Femoral Artery Bypass System for Cardiogenic Shock
07:39

Use of a Percutaneous Ventricular Assist Device/Left Atrium to Femoral Artery Bypass System for Cardiogenic Shock

Published on: August 16, 2021

3.7K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.6K
Insertion, Maintenance, and Removal of the Percutaneous Dual Lumen Cannula Right Ventricular Assist Device
07:41

Insertion, Maintenance, and Removal of the Percutaneous Dual Lumen Cannula Right Ventricular Assist Device

Published on: July 20, 2022

2.0K

Area of Science:

  • Cardiovascular Medicine
  • Transplantation Science
  • Medical Informatics

Background:

  • Left ventricular assist devices (LVAD) serve as a crucial bridge to heart transplantation (HT).
  • Prioritizing patients for HT is vital due to limited donor organ availability.
  • Accurate risk assessment for waitlist mortality in LVAD patients is essential for effective HT prioritization.

Purpose of the Study:

  • To derive and validate a risk prediction model for waitlist mortality in patients with LVAD.
  • To improve the prioritization process for heart transplantation in LVAD recipients.

Main Methods:

  • Utilized data from the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) and the European Registry for Patients with Mechanical Circulatory Support (EUROMACS).
  • Included adult patients with continuous-flow, centrifugal, durable LVADs listed for HT.
  • Employed Fine-Gray models and multiple logistic regression techniques to identify predictors and develop a survival model, validated in independent cohorts.

Main Results:

  • The derived risk prediction model showed an area-under-the-curve (AUC) of 0.72 in the INTERMACS cohort (2364 patients, 11% mortality).
  • Validation in the EUROMACS cohort (577 patients, 12% mortality) yielded an AUC of 0.62.
  • The model effectively stratified patients into low-, medium-, and high-risk groups for waitlist mortality in both cohorts.

Conclusions:

  • A robust risk prediction model for waitlist mortality in LVAD patients has been successfully derived and validated.
  • This risk assessment tool can significantly inform and enhance the prioritization of patients for heart transplantation.