Key result
Elevated LACI linked to ~138% greater disease progression risk in pre-symptomatic HCM.
Why the study?
The prognostic value of echocardiography to predict disease progression in pre-symptomatic hypertrophic cardiomyopathy remained to be fully explored.
Does an automated machine learning model using echocardiography-derived left atrioventricular coupling index predict disease progression in pre-symptomatic hypertrophic cardiomyopathy?
Observational (n=260)
Yes
Does an automated machine learning model using echocardiography-derived left atrioventricular coupling index predict disease progression in pre-symptomatic hypertrophic cardiomyopathy?
An automated machine learning model using echocardiography-derived left atrioventricular coupling index (LACI) successfully predicts disease progression in pre-symptomatic hypertrophic cardiomyopathy patients.
ML-based echo analysis may aid progression risk stratification in pre-symptomatic HCM; hypothesis-generating and requires prospective validation before practice change.
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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Olivier et al. (2026) conducted an observational in Early-stage hypertrophic cardiomyopathy (n=260). Area-derived left atrioventricular coupling index (LACI) > 0.51 vs. Area-derived LACI ≤ 0.51 was evaluated on Disease progression (composite of NYHA class worsening and unplanned cardiovascular-related hospitalizations) at 3 years (p=<0.001). An area-derived left atrioventricular coupling index (LACI) > 0.51 was associated with a 19% 3-year risk of disease progression compared to 8% for patients with LACI ≤ 0.51 in pre-symptomatic hypertrophic cardiomyopathy.
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