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

Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

929
Systolic Heart Failure and Compensatory MechanismsSystolic heart failure (also termed HFrEF, Heart Failure with Reduced Ejection Fraction) is the most prevalent type of heart filure. It results in a decreased volume of blood being pumped from the ventricle. The aortic arch and carotid sinuses have baroreceptors that detect reduced blood pressure, triggering the sympathetic nervous system (SNS) to release epinephrine and norepinephrine. Initially, this response aims to boost heart rate and...
929
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

3.7K
Heart failure (HF) is a progressive syndrome involving ventricles that leads to inadequate cardiac output. It can be classified based on location and output or ejection fraction. Ejection fraction (EF) is an essential measurement in the diagnosis and surveillance of HF. Reduced EF corresponds to systolic heart failure (HFrEF). However, HF with preserved ejection fraction (HFpEF) is becoming increasingly prevalent. Also known as diastolic HF, this form of HF is related to aging. The...
3.7K
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

886
Heart failure refers to a clinical syndrome caused by structural or functional cardiac disorders that prevent the heart from pumping an adequate amount of blood to meet the body's metabolic needs. This condition often arises from myocardial infarction or ischemia, leading to decreased cardiac output, reduced tissue perfusion, impaired gas exchange, fluid volume imbalance, and decreased functional ability.Heart failure can result from disruptions in the mechanisms that regulate cardiac output...
886
Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

340
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.
340
Heart Failure Drugs: Diuretics01:22

Heart Failure Drugs: Diuretics

971
Heart failure and kidney perfusion are interconnected in a complex way. Reduced renal perfusion and venous congestion are two significant factors that contribute to renal dysfunction in heart failure. The kidneys, primarily responsible for fluid balance in the body, are adversely affected due to compromised cardiac output and increased venous pressure. In response to reduced renal perfusion, the kidneys activate neurohumoral mechanisms to restore balance. However, these mechanisms can be...
971
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

319
Medical Management of Acute Decompensated Heart Failure (ADHF)The primary goals of therapy for patients hospitalized with acute decompensated heart failure (ADHF) include:Relieving symptomsOptimizing volume statusSupporting oxygenation and ventilationMaintaining cardiac output (CO) and end-organ perfusionIdentifying and addressing the cause of ADHFPreventing complicationsProviding patient education on factors precipitating HF exacerbationPlanning for dischargeOngoing monitoring and assessment...
319

You might also read

Related Articles

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

Sort by
Same author

Chromatin topology control by a muscle-specific ribosomal protein.

bioRxiv : the preprint server for biology·2026
Same author

Clinical Patterns and Appropriateness of Apixaban Dosing in Patients With Atrial Fibrillation.

JACC. Advances·2026
Same author

Correction: Estimating the Hawthorne Effect in Real-World Blood Pressure Control Trials: An Analysis of the BP Home Trial.

Journal of general internal medicine·2026
Same author

Nightly sleep as a predictor of next-day arrhythmias in ambulatory adults.

Journal of electrocardiology·2026
Same author

Acute effects of caffeine withdrawal on headache among regular caffeinated coffee drinkers.

Scientific reports·2026
Same author

Automated echocardiographic detection of mitral valve prolapse and mitral regurgitation with video-based artificial intelligence algorithms.

European heart journal. Digital health·2026
Same journal

Medical students' use of large language models: a national survey.

International journal of medical informatics·2026
Same journal

BlockFedMed: A blockchain-federated learning framework for privacy-preserving mortality prediction across heterogeneous intensive care units.

International journal of medical informatics·2026
Same journal

Integrating clinical decision support systems in pediatric oncology: A scoping review of applications, implementation gaps, and management Implications.

International journal of medical informatics·2026
Same journal

Understanding digital health capability of allied health professionals - a mixed-methods study with content validity analysis.

International journal of medical informatics·2026
Same journal

On-premises open-source large language models for privacy-preserving multimodal depression screening.

International journal of medical informatics·2026
Same journal

Data mining methods, tasks, and algorithms for adverse drug reaction analysis in pharmacovigilance: A scoping review.

International journal of medical informatics·2026
See all related articles

Related Experiment Video

Updated: Feb 2, 2026

Author Spotlight: Investigating HR-Dependent Cardiac Function in Mouse Models Through a Novel Atrial-Pacing Approach
07:49

Author Spotlight: Investigating HR-Dependent Cardiac Function in Mouse Models Through a Novel Atrial-Pacing Approach

Published on: July 21, 2023

2.0K

Identifying heart failure using EMR-based algorithms.

Geoffrey H Tison1, Alanna M Chamberlain2, Mark J Pletcher3

  • 1Division of Cardiology, University of California, San Francisco, USA.

International Journal of Medical Informatics
|November 10, 2018
PubMed
Summary
This summary is machine-generated.

Developing reliable algorithms to identify heart failure (HF) patients using electronic medical records (EMR) is crucial for large-scale research. These computable phenotypes offer a practical approach for HF patient identification in clinical research.

Keywords:
Cohort studiesElectronic health recordsHeart failureLearning health systemOutcomes assessment

More Related Videos

Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients
03:47

Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients

Published on: July 12, 2024

1.2K
Gene Transfer for Ischemic Heart Failure in a Preclinical Model
07:35

Gene Transfer for Ischemic Heart Failure in a Preclinical Model

Published on: May 15, 2011

13.4K

Related Experiment Videos

Last Updated: Feb 2, 2026

Author Spotlight: Investigating HR-Dependent Cardiac Function in Mouse Models Through a Novel Atrial-Pacing Approach
07:49

Author Spotlight: Investigating HR-Dependent Cardiac Function in Mouse Models Through a Novel Atrial-Pacing Approach

Published on: July 21, 2023

2.0K
Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients
03:47

Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients

Published on: July 12, 2024

1.2K
Gene Transfer for Ischemic Heart Failure in a Preclinical Model
07:35

Gene Transfer for Ischemic Heart Failure in a Preclinical Model

Published on: May 15, 2011

13.4K

Area of Science:

  • Biomedical Informatics
  • Cardiovascular Research
  • Health Services Research

Background:

  • Heart failure (HF) presents a significant clinical and public health challenge.
  • Large-scale pragmatic research using electronic medical records (EMR) can improve HF management.
  • Reliable algorithms are essential for identifying HF patients within EMR data.

Purpose of the Study:

  • To develop and validate computable phenotype algorithms for identifying HF patients.
  • To utilize standardized data elements from the Patient Centered Outcomes Research Network (PCORnet) Common Data Model (CDM).

Main Methods:

  • HF computable phenotypes were constructed using HF diagnosis codes, HF-related medications, and N-terminal B-type natriuretic peptide (NT-proBNP) levels.
  • Algorithms were validated in a large cohort (n=76,254) from Olmsted County, MN (2010-2012).
  • Manual review of a subset of records confirmed HF status based on Framingham criteria.

Main Results:

  • Tested algorithms demonstrated trade-offs between sensitivity and positive predictive value (PPV).
  • The highest sensitivity algorithm (78.7%) used a single HF diagnosis code but had a lower PPV (68.5%).
  • Incorporating additional components like more diagnosis codes, medications, or elevated NT-proBNP (>450 pg/mL) improved PPV at the cost of reduced sensitivity.

Conclusions:

  • Algorithms derived from PCORnet CDM elements can identify HF patients without manual review, offering reasonable sensitivity and PPV.
  • The selection of an appropriate algorithm should be guided by the specific research objectives.