Phenotypic Clustering of Left Ventricular Diastolic Function Parameters: Patterns and Prognostic Relevance.
Aged
Aged, 80 and over
Cause of Death
Cluster Analysis
Diastole
Disease Progression
Echocardiography, Doppler, Color
Echocardiography, Doppler, Pulsed
Female
Heart Ventricles
/ diagnostic imaging
Hospitalization
Humans
Image Interpretation, Computer-Assisted
Machine Learning
Male
Middle Aged
Pattern Recognition, Automated
/ methods
Phenotype
Predictive Value of Tests
Progression-Free Survival
Retrospective Studies
Risk Assessment
Risk Factors
Ventricular Dysfunction, Left
/ diagnostic imaging
Ventricular Function, Left
big-data analytics
cluster analysis
diastolic dysfunction
machine learning
Journal
JACC. Cardiovascular imaging
ISSN: 1876-7591
Titre abrégé: JACC Cardiovasc Imaging
Pays: United States
ID NLM: 101467978
Informations de publication
Date de publication:
07 2019
07 2019
Historique:
received:
02
01
2018
revised:
05
02
2018
accepted:
06
02
2018
pubmed:
24
4
2018
medline:
18
3
2020
entrez:
23
4
2018
Statut:
ppublish
Résumé
This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significance. Assessment of LV DD is important in the management and prognosis of cardiovascular diseases. Data-driven approaches such as cluster analysis may be useful in segregating similar cases without the constraint of an a priori algorithm for risk stratification. The study included a convenience sample of 866 consecutive patients referred for myocardial function assessment (age 65 ± 17 years; 55.3% women; ejection fraction 60 ± 9%) for whom echocardiographic parameters of DD assessment were obtained per conventional guideline recommendations. Unsupervised, hierarchical cluster analysis of these parameters was conducted using the Ward linkage method. Major adverse cardiovascular events, hospitalization, and mortality were compared between conventional and cluster-based classifications. Clustering algorithms for screening the presence of DD in 559 of 866 patients identified 2 distinct groups and revealed modest agreement with conventional classification (kappa = 0.41, p < 0.001). Further cluster analysis in 387 patients with DD helped to classify the severity of DD into 2 groups, with good agreement with conventional classification (kappa = 0.619, p < 0.001). Survival analyses of patients assessed by both clustering algorithms for screening and grading DD showed improved prediction of event-free survival by clusters over conventional classification for all-cause mortality and cardiac mortality, even after accounting for a multivariable, balanced propensity score. An unsupervised assessment of echocardiographic variables for assessing LV DD revealed unique patterns of grouping. These natural patterns of clustering may better identify patient groups who have similar risk, and their incorporation into clinical practice may help eliminate indeterminate results and improve clinical outcome prediction.
Sections du résumé
OBJECTIVES
This study sought to explore the natural clustering of echocardiographic variables used for assessing left ventricular (LV) diastolic dysfunction (DD) in order to isolate high-risk phenotypic patterns and assess their prognostic significance.
BACKGROUND
Assessment of LV DD is important in the management and prognosis of cardiovascular diseases. Data-driven approaches such as cluster analysis may be useful in segregating similar cases without the constraint of an a priori algorithm for risk stratification.
METHODS
The study included a convenience sample of 866 consecutive patients referred for myocardial function assessment (age 65 ± 17 years; 55.3% women; ejection fraction 60 ± 9%) for whom echocardiographic parameters of DD assessment were obtained per conventional guideline recommendations. Unsupervised, hierarchical cluster analysis of these parameters was conducted using the Ward linkage method. Major adverse cardiovascular events, hospitalization, and mortality were compared between conventional and cluster-based classifications.
RESULTS
Clustering algorithms for screening the presence of DD in 559 of 866 patients identified 2 distinct groups and revealed modest agreement with conventional classification (kappa = 0.41, p < 0.001). Further cluster analysis in 387 patients with DD helped to classify the severity of DD into 2 groups, with good agreement with conventional classification (kappa = 0.619, p < 0.001). Survival analyses of patients assessed by both clustering algorithms for screening and grading DD showed improved prediction of event-free survival by clusters over conventional classification for all-cause mortality and cardiac mortality, even after accounting for a multivariable, balanced propensity score.
CONCLUSIONS
An unsupervised assessment of echocardiographic variables for assessing LV DD revealed unique patterns of grouping. These natural patterns of clustering may better identify patient groups who have similar risk, and their incorporation into clinical practice may help eliminate indeterminate results and improve clinical outcome prediction.
Identifiants
pubmed: 29680357
pii: S1936-878X(18)30186-4
doi: 10.1016/j.jcmg.2018.02.005
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1149-1161Commentaires et corrections
Type : ErratumIn
Type : CommentIn
Type : CommentIn
Type : CommentIn
Informations de copyright
Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.