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Related Concept Videos

Heart Failure IV: Classification and Diagnostic Evaluation01:30

Heart Failure IV: Classification and Diagnostic Evaluation

33
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...
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Heart Failure VI: Adjunct Therapies01:22

Heart Failure VI: Adjunct Therapies

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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.
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Updated: Sep 14, 2025

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05:16

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Developing a predictive nomogram for AMI in elderly patients with AHF: a retrospective analysis.

Qili Yu1, Tingting Song1, Rui Cui1

  • 1Department of Cardiology, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China.

Frontiers in Medicine
|July 24, 2025
PubMed
Summary

This study developed a prediction model to identify elderly patients at high risk of acute myocardial infarction (AMI) during acute heart failure (AHF) hospitalization. The model aids early detection and clinical decision-making for better patient outcomes.

Keywords:
acute heart failureacute myocardial infarctionelderlynomogramprediction model

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Area of Science:

  • Cardiology
  • Geriatrics
  • Medical Informatics

Background:

  • Elderly patients with acute heart failure (AHF) experiencing acute myocardial infarction (AMI) face severe conditions and poor prognoses.
  • Early identification of risk factors is crucial for timely intervention in this vulnerable population.

Purpose of the Study:

  • To analyze risk factors associated with AMI in elderly patients hospitalized with AHF.
  • To develop and validate a clinical prediction model for early AMI risk assessment in this demographic.

Main Methods:

  • Retrospective analysis of 1,904 elderly AHF patients hospitalized between October 2019 and December 2023.
  • Utilized LASSO and logistic regression to identify independent risk factors for AMI.
  • Constructed a nomogram model and validated its predictive performance using AUC, ROC, decision curve analysis, and clinical impact curves.

Main Results:

  • Identified age, coronary heart disease, diabetes, pulmonary infection, ventricular arrhythmia, hyperlipidemia, hypoalbuminemia, left ventricular diastolic diameter (LVDD), and left ventricular ejection fraction (LVEF) as independent risk factors for AMI.
  • The developed prediction model demonstrated strong performance with an AUC of 0.780, accuracy of 91.3%, and specificity of 91.4%.

Conclusions:

  • A robust multivariate prediction model for AMI risk in elderly hospitalized AHF patients was successfully developed.
  • This model serves as a valuable tool for clinicians to facilitate early risk identification and intervention, improving patient management.