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Related Experiment Video

Updated: Dec 12, 2025

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism

Published on: September 1, 2014

Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.

Yvonne E Kaptein^1,2, Ilya Karagodin³, Hongquan Zuo⁴

¹Aurora Cardiovascular Services, Aurora St Luke's Medical Center, Milwaukee, WI, USA. yvonne.kaptein@gmail.com.

BMC Cardiovascular Disorders

|August 16, 2020

Summary

This summary is machine-generated.

Subclinical diastolic dysfunction can lead to heart failure with preserved ejection fraction (HFpEF), but not all patients progress. Cluster analysis identified distinct patient subgroups with varying HFpEF risk and outcomes, highlighting the need for personalized prediction models.

Keywords:

Clinical studies Diastolic dysfunction Echocardiography Heart failure Heart failure with preserved ejection fraction Hierarchical clustering Risk factors Unsupervised machine learning

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Echocardiographic Assessment Using Subxiphoid-Only Examination for Hypotensive Patients

Echocardiographic Assessment Using Subxiphoid-Only Examination for Hypotensive Patients

Published on: April 18, 2025

Area of Science:

Cardiology
Medical Informatics
Biostatistics

Background:

Subclinical diastolic dysfunction precedes heart failure with preserved ejection fraction (HFpEF).
Not all individuals with diastolic dysfunction progress to HFpEF.
Identifying predictors of HFpEF progression is crucial for patient management.

Purpose of the Study:

To identify clinical and echocardiographic variables that predict the development of HFpEF from subclinical diastolic dysfunction.
To stratify patients with diastolic dysfunction into distinct subgroups based on progression to HFpEF.

Main Methods:

Retrospective data collection from 162 patients (81 HFpEF, 81 controls).
Utilized unsupervised clustering (density-based and hierarchical) on 65 variables.
Logistic regression analysis performed on the overall cohort and individual clusters.

Main Results:

Three distinct patient subgroups were identified through clustering.
Subgroups differed in gender, cardiac hypertrophy, aortic stenosis severity, NT-proBNP levels, and HFpEF progression timing.
Predictors of HFpEF varied across subgroups, with common factors including diabetes, chronic kidney disease, atrial fibrillation, and diuretic use.

Conclusions:

Cluster analysis effectively identifies phenotypically distinct subgroups of patients with diastolic dysfunction.
These subgroups exhibit differential HFpEF and mortality outcomes.
Predictive variables for HFpEF outcomes are cluster-specific, underscoring the heterogeneity of the disease.