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Related Experiment Video

Updated: Feb 11, 2026

Echocardiographic Measurement of Right Ventricular Diastolic Parameters in Mouse
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Phenotypic Clustering of Left Ventricular Diastolic Function Parameters: Patterns and Prognostic Relevance.

Megan Cummins Lancaster1, Alaa Mabrouk Salem Omar2, Sukrit Narula1

  • 1Department of Cardiology, Icahn School of Medicine at Mount Sinai, New York, New York.

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|April 23, 2018
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Summary

Unsupervised clustering of echocardiographic data identified distinct patient groups for left ventricular diastolic dysfunction (LVDD). These novel patterns improve risk stratification and prediction of cardiovascular events compared to traditional methods.

Keywords:
big-data analyticscluster analysisdiastolic dysfunctionmachine learning

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Area of Science:

  • Cardiology
  • Echocardiography
  • Data Science

Background:

  • Left ventricular diastolic dysfunction (LVDD) assessment is crucial for cardiovascular disease management and prognosis.
  • Data-driven cluster analysis offers a method to stratify risk without predefined algorithms.

Purpose of the Study:

  • To explore natural clustering of echocardiographic variables for left ventricular (LV) diastolic dysfunction (DD).
  • To identify high-risk phenotypic patterns and assess their prognostic significance.

Main Methods:

  • Unsupervised hierarchical cluster analysis of echocardiographic parameters in 866 patients.
  • Comparison of cluster-based classifications with conventional methods for risk stratification and event prediction.

Main Results:

  • Clustering identified 2 distinct groups for LVDD screening (kappa = 0.41) and 2 groups for severity grading (kappa = 0.619).
  • Cluster-based assessment improved prediction of event-free survival for mortality outcomes compared to conventional classification.

Conclusions:

  • Unsupervised clustering reveals unique echocardiographic patterns for LVDD.
  • These natural groupings can better identify at-risk patients, potentially improving clinical outcomes and reducing indeterminate results.