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Related Concept Videos

Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

1.5K
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...
1.5K
Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

1
Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...
1
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

1
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...
1
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

1
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...
1
Heart Failure V: Nursing Interventions01:30

Heart Failure V: Nursing Interventions

1
The first step in nursing management of a patient with heart failure involves thoroughly assessing the patient's medical history.Subjective Data: Obtain the patient's medical history of coronary artery disease, hypertension, myocardial infarction, and symptoms like dyspnea, orthopnea, and paroxysmal nocturnal dyspnea.Objective Data: Conduct a physical examination to identify findings such as jugular vein distention, pulmonary crackles, tachycardia, murmurs, peripheral edema, and vital signs,...
1
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

1
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...
1

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Related Experiment Video

Updated: Jun 9, 2025

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

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Automated Identification of Heart Failure With Reduced Ejection Fraction Using Deep Learning-Based Natural Language

Arash A Nargesi1, Philip Adejumo2, Lovedeep Singh Dhingra2

  • 1Heart and Vascular Center, Brigham and Women's Hospital, Harvard School of Medicine, Boston, Massachusetts, USA.

JACC. Heart Failure
|October 25, 2024
PubMed
Summary

A new deep-learning model accurately identifies patients with heart failure with reduced ejection fraction (HFrEF) from clinical notes. This tool automates quality assessment for HFrEF care, improving patient outcomes.

Keywords:
deep learningelectronic heart recordsheart failure with reduced ejection fractionlongformernatural language processing

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Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Informatics
  • Cardiology Research

Background:

  • Automated tools for measuring care quality in heart failure with reduced ejection fraction (HFrEF) are lacking.
  • This gap hinders the implementation of national programs for assessing guideline-directed care in HFrEF.

Purpose of the Study:

  • To develop an automated method for identifying patients with HFrEF at hospital discharge.
  • To enable timely evaluation and improvement of care quality for HFrEF patients.

Main Methods:

  • A deep-learning language model was created to identify HFrEF from hospital discharge summaries.
  • HFrEF was defined as left ventricular ejection fraction <40% via echocardiography.
  • The model was validated using data from Yale New Haven Hospital, Northwestern Medicine, Yale community hospitals, and the MIMIC-III database.

Main Results:

  • The model achieved high performance in detecting HFrEF, with an AUROC of 0.97 and AUPRC of 0.97 on the held-out set.
  • External validation demonstrated strong performance across multiple institutions and datasets (AUROC ranging from 0.91 to 0.95).
  • Model predictions significantly improved HFrEF reclassification compared to diagnosis codes (60.2% ± 1.9% improvement, P < 0.001).

Conclusions:

  • A novel language model effectively identifies HFrEF from clinical notes with high precision and accuracy.
  • This automated approach is crucial for enhancing the quality assessment of HFrEF care.
  • The developed model represents a significant advancement in leveraging clinical notes for quality improvement initiatives.