Utilizing echocardiography and unsupervised machine learning for heart failure risk identification

Jakob Øystein Simonsen*, Daniel Modin, Kristoffer Skaarup, Kasper Djernæs, Mats Christian Højbjerg Lassen, Niklas Dyrby Johansen, Jacob Louis Marott, Magnus Thorsten Jensen, Gorm B. Jensen, Peter Schnohr, Sergio Sánchez Martínez, Brian Lee Claggett, Rasmus Møgelvang, Tor Biering-Sørensen

*Corresponding author af dette arbejde

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Abstract

Background: Global longitudinal strain (GLS) is recognized as a powerful predictor of heart failure (HF). However, the entire strain curve may entail important prognostic information regarding HF risk that might be undiscovered by only focusing on the peak strain value. Objective: The hypothesis of the present study was, that analysis of the entire strain curve using unsupervised machine learning (uML) would reveal novel ventricular deformation patterns capable of predicting incident HF independently of GLS. Methods: Longitudinal strain curves from 3710 subjects from the general population without prevalent HF were analyzed using uML. Results: Mean age was 56 years and 43 % were male. During a median follow-up of 5.3 years, 92 subjects (2.5 %) developed HF. The uML algorithm generated a hierarchical clustering tree (HCT) resulting in 10 different clusters. Generally, the strain curves displayed reduced early diastolic strain to peak-strain ratio with an increasing incidence rate of HF. In multivariable Cox regressions, cluster 9 was significantly associated with increased risk of HF when compared to cluster 2–5, and 7–8 [For cluster 3: HR 8.95, 95 %CI: 2.08;38.48, P = 0.003] even though the subjects of cluster 9 were younger, displayed healthier clinical baseline characteristics, and only had slightly reduced GLS. The mean strain curve of cluster 9 displayed an early systolic lengthening followed by a late and reduced contraction specifically related to the basal lateral segment. Conclusion: The unsupervised machine learning algorithm identified unknown strain patterns beyond GLS presumably related to increased risk of HF.

OriginalsprogEngelsk
Artikelnummer132636
TidsskriftInternational Journal of Cardiology
Vol/bind418
Antal sider10
ISSN0167-5273
DOI
StatusUdgivet - 2025

Bibliografisk note

Funding Information:
The Copenhagen City Heart Study is funded by The Danish Heart Foundation and The Metropolitan Region of Denmark . Jakob \u00D8ystein Simonsen has not received funding for the present project.

Publisher Copyright:
© 2024 The Authors

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