Using machine learning analysis to describe patterns in tissue Doppler and speckle tracking echocardiography in patients with transposition of the great arteries after arterial switch operation

Aug 6, 2026

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Advanced echocardiographic techniques such as tissue Doppler imaging and speckle tracking echocardiography can reveal subtle ventricular changes. This study evaluates mid-term ventricular performance in patients with transposition of the great arteries after arterial switch operation using conventional echocardiographic parameters, TDI, STE, and unsupervised machine learning. Prospective analysis in 124 patients combines conventional statistics and machine learning to quantify systolic and diastolic function. Unsupervised clustering identifies three patient clusters, including a subgroup with older age at surgery and more impaired function alongside higher reoperation and intervention rates.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Background

Methods

Results

Conclusions

Using machine learning analysis to describe patterns in tissue Doppler and speckle tracking echocardiography in patients with transposition of the great arteries after arterial switch operation☆ Monteros, [C.T.E. de](C.T.E. de) los; Palen, R.L.F. van der; Eynde, J. van den; Rammeloo, L.; Hazekamp, M.G.; Blom, N.A.; ... ; Harkel, A.D.J.T. Citation Monteros, [C. T. E. de](C. T. E. de) los, Palen, R. L. F. van der, Eynde, J. van den, Rammeloo, L., Hazekamp, M. G., Blom, N. A.,…Harkel, A. D. J. T. (2025) . Using machine learning analysis to describe patterns in tissue Doppler and speckle tracking echocardiography inpatients with transposition of the great arteries after arterial switch operation☆ . International Journal Of Cardiology Congenital Heart Disease, 19. doi:10.1016/j.ijcchd.2024.100560 Version: Publisher's Version License: Creative Commons CC BY 4.0 license Downloaded from: [https://hdl.handle.net/1887/4247043](https://hdl.handle.net/1887/4247043) Note: To cite this publication please use the final published version (if applicable) . International Journal of Cardiology Congenital Heart Disease 19 (2025) 100560 Contents lists available at ScienceDirect International Journal of Cardiology Congenital Heart Disease journal [homepage:](homepage: www.journals.elsevier.com/international)[ www.journals.elsevier.com/international](homepage: www.journals.elsevier.com/international)journal-of-cardiology-congenital-heart-disease | Using machine learning analysis to describe patterns in tissue Doppler and speckle tracking echocardiography in patients with transposition of the great arteries after arterial switch operation☆
Covadonga Terol Espinosa de los Monterosa , Roel L.F. van der Palena, Jef Van den Eynde b, Lukas Rammelooc, Mark G. Hazekampd, Nico A. Bloma,c,* , Irene M. Kuipersc,
Arend D.J. ten Harkela
a Department of Pediatrics, Division of Pediatric Cardiology, Leiden University Medical Center, Leiden, the Netherlands b Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
c Department of Pediatrics, Division of Pediatric Cardiology, Amsterdam University Medical Center, Amsterdam, the Netherlands d Department of Pediatric Cardiac Surgery, Leiden University Medical Center, Leiden, the Netherlands | | | | | --- | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords:
Transposition of the great arteries Arterial switch operation
Cardiac function
Tissue Doppler imaging
Speckle tracking echocardiography | | Background: Advanced echocardiographic techniques such as Tissue Doppler imaging (TDI) and speckle tracking echocardiography (STE) can detect more subtle changes in ventricular performance. We aimed to study the ventricular performance in patients with transposition of the great arteries (TGA) at mid-term follow-up after the arterial switch operation (ASO) with advanced echocardiographic techniques. In addition, we sought to discover new clinical phenotypes using unsupervised machine learning.
Methods: Conventional, TDI and STE echocardiographic parameters were prospectively obtained from 124 TGA patients (66.1 % male, age 10.8 ± 5.1 years, 24.2 % with ventricular septal defect) in this observational study. The data was analyzed with conventional statistics and new machine learning techniques.
Results: TGA patients had reduced biventricular systolic (septal s’ Z-score − 2.28 ± 1.26; RV s’ Z-score − 2.16 ± 0.71; mean left ventricular longitudinal strain Z-score of the LV-2.49 ± 1.68) and RV diastolic performance (RVE/e’ Z-score 2.35 ± 1.70) mid-term after ASO. Unsupervised clustering within the TGA population revealed 3 clusters. Interestingly, cluster 3 defined a group of patients with older age at ASO, the most reduced ventricular performance as well as the highest rates of reoperations and interventions.
Conclusions: Assessment of ventricular performance with TDI and STE 10 years after ASO showed that TGA patients have decr

FAQs

What did unsupervised clustering show about patient subgroups?

Clustering within the TGA population revealed three clusters, and one cluster corresponded to patients with older age at surgery, the most reduced ventricular performance, and the highest rates of reoperations and interventions.

Aug 6, 2026

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