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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

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Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
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A Machine Learning-Derived Score to Effectively Identify Heart Failure With Preserved Ejection Fraction.

Kevin C Bermea1, Jana P Lovell1, Allison G Hays1

  • 1Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.

JACC. Advances
|August 12, 2024
PubMed
Summary

Diagnosing heart failure with preserved ejection fraction (HFpEF) in obese patients is challenging. A new score using BMI, kidney function, and heart measurements shows improved accuracy over existing methods.

Keywords:
HFpEF diagnosischronic kidney diseaseleft atrial enlargementleft ventricular hypertrophyobesity

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Area of Science:

  • Cardiology
  • Medical Diagnostics
  • Obesity Medicine

Background:

  • Diagnosing heart failure with preserved ejection fraction (HFpEF) presents clinical challenges, particularly in individuals with obesity.
  • Obesity is a significant comorbidity that complicates HFpEF diagnosis and management.

Purpose of the Study:

  • To identify novel predictors for diagnosing HFpEF specifically tailored for patients with obesity.
  • To develop and validate a new diagnostic score for HFpEF in an obese cohort.

Main Methods:

  • Retrospective analysis of a cohort of 404 obese patients with HFpEF and 67 controls.
  • Utilized Gradient Boosting Machine (GBM) for identifying key predictors.
  • Developed a multivariate logistic model incorporating significant predictors.

Main Results:

  • GBM identified body mass index (BMI), estimated glomerular filtration rate (eGFR), left ventricular mass index (LVMI), and left atrial to left ventricular volume ratio (LAVi/LVi) as strongest HFpEF predictors.
  • The novel score achieved high diagnostic performance (AUC 0.88, sensitivity 0.83, specificity 0.82).
  • The new score significantly outperformed the H2FPEF score (AUC 0.88 vs. 0.74).

Conclusions:

  • BMI, eGFR, LVMI, and LAVi/LVi are key predictors for HFpEF in obese patients.
  • The developed HFpEF-JH score demonstrates superior diagnostic accuracy compared to the H2FPEF score.
  • Further validation is recommended for the HFpEF-JH score to enhance HFpEF diagnosis in obese populations.