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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

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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...
48
Heart Failure VII: Nursing Interventions01:30

Heart Failure VII: Nursing Interventions

149
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,...
149
Heart Failure II: Pathophysiology01:29

Heart Failure II: Pathophysiology

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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...
69
Heart Failure I: Introduction01:27

Heart Failure I: Introduction

88
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...
88
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 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...
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Related Experiment Video

Updated: Sep 22, 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

231

Predictive Model for Heart Failure Readmission Using Nationwide Readmissions Database.

Lillian Zheng1, Nathan J Smith2, Bi Qing Teng3

  • 1Department of Medicine, Medical College of Wisconsin, Milwaukee.

Mayo Clinic Proceedings. Innovations, Quality & Outcomes
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

This study developed a heart failure readmission prediction model using a large database. The model showed moderate accuracy, aiming to improve patient management and reduce hospital readmissions.

Keywords:
AUC, area under the curveHF, heart failureNRD, Nationwide Readmissions DatabaseROC, receiver operating characteristicSID, State Inpatient Database

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Area of Science:

  • Cardiology
  • Health Informatics
  • Predictive Analytics

Background:

  • Heart failure (HF) is a leading cause of hospital readmissions.
  • Effective prediction models are needed to manage HF patients and reduce readmission rates.

Purpose of the Study:

  • To develop and validate a predictive model for heart failure (HF) readmission.
  • To guide clinical management and reduce HF readmissions.

Main Methods:

  • Retrospective analysis of Nationwide Readmissions Database (2010-2014).
  • Cox proportional hazards model for prediction.
  • Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) used for accuracy assessment.

Main Results:

  • Over 1.8 million HF admissions analyzed.
  • Model AUC was 0.58 (derivation) and 0.59 (validation).
  • Identified common comorbidities in readmitted HF patients.

Conclusions:

  • The developed HF readmission prediction model has potential clinical utility.
  • It can aid in optimizing patient management at the point of care.
  • The model aims to reduce HF readmissions effectively.