A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography
Abstract
Introduction 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. Methods 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). Results 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 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 ≤ 0.51 in the discovery cohort. Conclusion 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.
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BibTeX
@article{olivier2026fully,
title = {{A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography}},
author = {Antoine Olivier and Auriane Riou and Thomas D’humières and Maxime Touzot and Kévin Elgui and Benoît Sauty and Paul Trichelair and Valérie Ducret and Maria Telenczuk and Antoine Simon and Felix Balazard and Mariann Micsinai Balan and Arnaud Bastien and Amy Sehnert and Faiez Zannad and Philippe Gabriel Steg and Perry Blizard and Lauren Turvey-Haigh and Tomas Ripoll and William Bradlow and Philippe Charron},
journal = {Frontiers in Cardiovascular Medicine},
year = {2026},
month = aug,
doi = {10.3389/fcvm.2026.1875305},
url = {https://hal.univ-lorraine.fr/hal-05732817v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography
AU - Antoine Olivier
AU - Auriane Riou
AU - Thomas D’humières
AU - Maxime Touzot
AU - Kévin Elgui
AU - Benoît Sauty
AU - Paul Trichelair
AU - Valérie Ducret
AU - Maria Telenczuk
AU - Antoine Simon
AU - Felix Balazard
AU - Mariann Micsinai Balan
AU - Arnaud Bastien
AU - Amy Sehnert
AU - Faiez Zannad
AU - Philippe Gabriel Steg
AU - Perry Blizard
AU - Lauren Turvey-Haigh
AU - Tomas Ripoll
AU - William Bradlow
AU - Philippe Charron
PY - 2026
DA - 2026/08/03
JO - Frontiers in Cardiovascular Medicine
DO - 10.3389/fcvm.2026.1875305
UR - https://hal.univ-lorraine.fr/hal-05732817v1
AB - Introduction 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. Methods 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). Results 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 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 ≤ 0.51 in the discovery cohort. Conclusion 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.
SN - 2297-055X
ER -