A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography.

Authors

Antoine Olivier, Auriane Riou, Thomas d'Humières, Maxime Touzot, Kévin Elgui, Benoît Sauty, Paul Trichelair, Valérie Ducret, Maria Telenczuk, Antoine Simon, Felix Balazard, Mariann Micsinai Balan, Arnaud Bastien, Amy Sehnert, Faiez Zannad, Philippe Gabriel Steg, Perry Blizard, Lauren Turvey-Haigh, Tomas Ripoll, William Bradlow, Philippe Charron

Published in

Frontiers in cardiovascular medicine. Volume 13. Pages 1875305. Epub Aug 03, 2026.

Abstract

Echocardiography is critical for the diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM). However, its value to predict disease progression in pre-symptomatic HCM remains to be fully explored. This study aims to assess the prognostic value of echocardiography in pre-symptomatic HCM using a fully automated machine learning (ML) pipeline to predict disease progression.
Echocardiographic data (B-mode acquisitions of apical 4-chamber sequences) from 260 NYHA I HCM patients, collected retrospectively from two centers, was used. An ML pipeline was built on the discovery cohort ( $N=212$ patients) to predict disease progression, defined as a composite of NYHA class worsening and unplanned cardiovascular-related hospitalizations. A fully automated deep learning segmentation pipeline was used to delineate cardiac chambers in apical 4-chamber views and identify end-diastolic frames. Shape-based radiomic features extracted from these segmentations were used to train a survival model based on gradient-boosted trees. The ML model and a derived actionable echocardiographic marker were validated on an external cohort ( $N=48$ patients).
The 3-year risk of disease progression was 12% in the discovery cohort and 15% in the validation cohort. The ML model achieved a C-index of $0.66$ (95% CI [0.54, 0.77], nested cross-validation folds) in the discovery cohort and $0.67$ (95% CI [0.46, 0.88], 100 bootstrapped samples) in the validation cohort. Following model interpretation, the left atrioventricular coupling index (area-derived LACI) at end-diastole was derived, and used as a risk score, achieving a C-index of $0.67$ (95% CI [0.58, 0.77]) and $0.72$ (95% CI [0.56, 0.88]) in the discovery and validation cohorts, respectively (100 bootstrapped samples). The high-risk group, with $\mathrm{LACI}>0.51$ , had a 3-year risk of disease progression of 19% (95% CI [13%, 36%]) compared to 8% (95% CI [5%, 15%]) for the low-risk group LACI $\le 0.51$ in the discovery cohort.
A fully automated ML model identifies area-derived LACI at end-diastole as a robust feature associated with disease progression, providing improved risk stratification for pre-symptomatic HCM.

PMID:
42609278
Bibliographic data and abstract were imported from PubMed on 18 Aug 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.