{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 21,
  "authors": [
    {
      "name": "Antoine Olivier"
    },
    {
      "name": "Auriane Riou"
    },
    {
      "name": "Thomas D’humières"
    },
    {
      "name": "Maxime Touzot"
    },
    {
      "name": "Kévin Elgui"
    },
    {
      "name": "Benoît Sauty"
    },
    {
      "name": "Paul Trichelair"
    },
    {
      "name": "Valérie Ducret"
    },
    {
      "name": "Maria Telenczuk"
    },
    {
      "name": "Antoine Simon"
    },
    {
      "name": "Felix Balazard"
    },
    {
      "name": "Mariann Micsinai Balan"
    },
    {
      "name": "Arnaud Bastien"
    },
    {
      "name": "Amy Sehnert"
    },
    {
      "name": "Faiez Zannad"
    },
    {
      "name": "Philippe Gabriel Steg"
    },
    {
      "name": "Perry Blizard"
    },
    {
      "name": "Lauren Turvey-Haigh"
    },
    {
      "name": "Tomas Ripoll"
    },
    {
      "name": "William Bradlow"
    },
    {
      "name": "Philippe Charron"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ej4x7fp4610pn.bib",
  "cited_by": 0,
  "doi": "10.3389/fcvm.2026.1875305",
  "fulltext_url": null,
  "id": "ej4x7fp4610pn",
  "issns": [
    "2297-055X"
  ],
  "landing_url": "https://hal.univ-lorraine.fr/hal-05732817v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
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      "openalex",
      "2705|Cardiomyopathy and Myosin Studies"
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  "pdf_url": "https://hal.univ-lorraine.fr/hal-05732817/document",
  "pmcid": null,
  "pmid": "42609278",
  "published": "2026-08-03",
  "publisher": "Frontiers Media",
  "ris": "https://research.draconitic.com/paper/ej4x7fp4610pn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "hal-05732817",
      "source": "hal",
      "url": "https://hal.univ-lorraine.fr/hal-05732817v1"
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  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2705|Cardiomyopathy and Myosin Studies",
      "subfield": "cardiovascular"
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  ],
  "title": "A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography",
  "title_text": "A fully automated machine learning assisted pipeline to predict disease progression for early-stage hypertrophic cardiomyopathy from echocardiography",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/ej4x7fp4610pn",
  "venue": "Frontiers in Cardiovascular Medicine",
  "versions": []
}