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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

57
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...
57
Heart Failure VII: Nursing Interventions01:30

Heart Failure VII: Nursing Interventions

156
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,...
156
Heart Failure V: Medical Management01:30

Heart Failure V: Medical Management

36
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...
36
Pathophysiology of Heart Failure01:17

Pathophysiology of Heart Failure

1.9K
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...
1.9K
Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

47
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.
47
Heart Failure III: Clinical Manifestations01:26

Heart Failure III: Clinical Manifestations

67
Heart failure (HF) manifests primarily as dyspnea, fatigue, and fluid retention, resulting in peripheral and pulmonary edema. Symptoms may vary depending on which ventricle is more affected, left or right.Left-Sided Heart FailureAlso known as left ventricular failure, this condition results from the left ventricle's inability to fill or eject sufficient blood into the systemic circulation. It leads to pulmonary congestion, which occurs when the left ventricle fails to eject blood effectively...
67

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Related Experiment Video

Updated: Sep 28, 2025

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
05:16

Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure

Published on: June 10, 2025

261

Identifying Patients With Advanced Heart Failure Using Administrative Data.

Shannon M Dunlay1,2, Saul Blecker3, Phillip J Schulte4

  • 1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.

Mayo Clinic Proceedings. Innovations, Quality & Outcomes
|April 4, 2022
PubMed
Summary
This summary is machine-generated.

Algorithms using administrative data can identify patients with advanced heart failure (HF). These methods offer a practical approach for detecting advanced HF cases in large populations.

Keywords:
ED, emergency departmentEF, ejection fractionESC, European Society of CardiologyHF, heart failureNPV, negative predictive valuePPV, positive predictive valueREP, Rochester Epidemiology ProjectVA, ventricular arrhythmia

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Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Author Spotlight: Workflow for Integrating POCUS Data into EHR for Managing Heart Failure Patients
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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Area of Science:

  • Cardiology
  • Health Informatics
  • Clinical Research

Background:

  • Advanced heart failure (HF) requires accurate identification for timely intervention.
  • Administrative data offers a scalable solution for patient cohort identification.
  • Current methods for identifying advanced HF in administrative data are limited.

Purpose of the Study:

  • To develop and validate algorithms for identifying advanced heart failure (HF) using administrative data.
  • To assess the performance of these algorithms in a population-based cohort.
  • To improve the efficiency of advanced HF patient identification.

Main Methods:

  • A population-based cohort of 8657 patients with HF was established.
  • Advanced HF was defined using European Society of Cardiology criteria via manual medical review.
  • Candidate algorithms using billing codes and prescriptions were developed and tested.

Main Results:

  • A single hospitalization for HF or ventricular arrhythmias achieved 100% sensitivity but low positive predictive value (PPV) of 36.4%.
  • More stringent algorithms (e.g., 2 hospitalizations + 1 sign of advanced HF) improved PPV to 60.5% with 72.7% sensitivity.
  • All evaluated algorithms demonstrated high negative predictive values.

Conclusions:

  • Algorithms utilizing administrative data can effectively identify patients with advanced heart failure (HF).
  • These algorithms show promise for improving the identification of advanced HF in clinical practice.
  • Further refinement of algorithms can balance sensitivity and specificity for optimal performance.