Literature DB >> 28109936

Precision Phenotyping in Heart Failure and Pattern Clustering of Ultrasound Data for the Assessment of Diastolic Dysfunction.

Alaa Mabrouk Salem Omar1, Sukrit Narula2, Mohamed Ahmed Abdel Rahman3, Gianni Pedrizzetti4, Hala Raslan5, Osama Rifaie3, Jagat Narula2, Partho P Sengupta6.   

Abstract

OBJECTIVES: The aim of this study was to investigate whether cluster analysis of left atrial and left ventricular (LV) mechanical deformation parameters provide sufficient information for Doppler-independent assessment of LV diastolic function.
BACKGROUND: Medical imaging produces substantial phenotyping data, and superior computational analyses could allow automated classification of repetitive patterns into patient groups with similar behavior.
METHODS: The authors performed a cluster analysis and developed a model of LV diastolic function from an initial exploratory cohort of 130 patients that was subsequently tested in a prospective cohort of 44 patients undergoing cardiac catheterization. Patients in both study groups had standard echocardiographic examination with Doppler-derived assessment of diastolic function. Both the left ventricle and the left atrium were tracked simultaneously using speckle-tracking echocardiography (STE) for measuring simultaneous changes in left atrial and ventricular volumes, volume rates, longitudinal strains, and strain rates. Patients in the validation group also underwent invasive measurements of pulmonary capillary wedge pressure and LV end diastolic pressure immediately after echocardiography. The similarity between STE and conventional 2-dimensional and Doppler methods of diastolic function was investigated in both the exploratory and validation cohorts.
RESULTS: STE demonstrated strong correlations with the conventional indices and independently clustered the patients into 3 groups with conventional measurements verifying increasing severity of diastolic dysfunction and LV filling pressures. A multivariable linear regression model also allowed estimation of E/e' and pulmonary capillary wedge pressure by STE in the validation cohort.
CONCLUSIONS: Tracking deformation of the left-sided cardiac chambers from routine cardiac ultrasound images provides accurate information for Doppler-independent phenotypic characterization of LV diastolic function and noninvasive assessment of LV filling pressures.
Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  big-data analytics; diastolic dysfunction; left ventricular filling pressures; speckle-tracking echocardiography

Mesh:

Year:  2017        PMID: 28109936     DOI: 10.1016/j.jcmg.2016.10.012

Source DB:  PubMed          Journal:  JACC Cardiovasc Imaging        ISSN: 1876-7591


  12 in total

Review 1.  Left ventricular strain and twisting in heart failure with preserved ejection fraction: an updated review.

Authors:  Marijana Tadic; Elisabeth Pieske-Kraigher; Cesare Cuspidi; Martin Genger; Daniel A Morris; Kun Zhang; Nina Alexandra Walther; Burket Pieske
Journal:  Heart Fail Rev       Date:  2017-05       Impact factor: 4.214

Review 2.  Artificial Intelligence in Cardiovascular Imaging: JACC State-of-the-Art Review.

Authors:  Damini Dey; Piotr J Slomka; Paul Leeson; Dorin Comaniciu; Sirish Shrestha; Partho P Sengupta; Thomas H Marwick
Journal:  J Am Coll Cardiol       Date:  2019-03-26       Impact factor: 24.094

3.  Longitudinal Study on Sustained Attention to Response Task (SART): Clustering Approach for Mobility and Cognitive Decline.

Authors:  Rossella Rizzo; Silvin P Knight; James R C Davis; Louise Newman; Eoin Duggan; Rose Anne Kenny; Roman Romero-Ortuno
Journal:  Geriatrics (Basel)       Date:  2022-04-22

4.  Will Artificial Intelligence Replace the Human Echocardiographer?

Authors:  Partho P Sengupta; Donald A Adjeroh
Journal:  Circulation       Date:  2018-10-16       Impact factor: 29.690

5.  AI tracks a beating heart's function over time.

Authors:  Partho P Sengupta; Donald A Adjeroh
Journal:  Nature       Date:  2020-04       Impact factor: 49.962

6.  Relationship of Transmural Variations in Myofiber Contractility to Left Ventricular Ejection Fraction: Implications for Modeling Heart Failure Phenotype With Preserved Ejection Fraction.

Authors:  Yaghoub Dabiri; Kevin L Sack; Semion Shaul; Partho P Sengupta; Julius M Guccione
Journal:  Front Physiol       Date:  2018-08-24       Impact factor: 4.566

Review 7.  Left Ventricular Diastolic Dysfunction in Type 2 Diabetes-Progress and Perspectives.

Authors:  Elena-Daniela Grigorescu; Cristina-Mihaela Lacatusu; Mariana Floria; Bogdan-Mircea Mihai; Ioana Cretu; Laurentiu Sorodoc
Journal:  Diagnostics (Basel)       Date:  2019-09-17

8.  Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility.

Authors:  Amitava Banerjee; Suliang Chen; Ghazaleh Fatemifar; Mohamad Zeina; R Thomas Lumbers; Johanna Mielke; Simrat Gill; Dipak Kotecha; Daniel F Freitag; Spiros Denaxas; Harry Hemingway
Journal:  BMC Med       Date:  2021-04-06       Impact factor: 11.150

Review 9.  A manifesto for cardiovascular imaging: addressing the human factor.

Authors:  Alan G Fraser
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2017-12-01       Impact factor: 6.875

10.  Prediction of Left Ventricular Mechanics Using Machine Learning.

Authors:  Yaghoub Dabiri; Alex Van der Velden; Kevin L Sack; Jenny S Choy; Ghassan S Kassab; Julius M Guccione
Journal:  Front Phys       Date:  2019-09-06
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