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Electrocardiographic parameter profiles for differentiating hypertrophic cardiomyopathy stages

Naomi Hirota1, Shinya Suzuki1, Takuto Arita1

  • 1Department of Cardiovascular Medicine The Cardiovascular Institute Tokyo Japan.

Journal of Arrhythmia
|March 6, 2025
PubMed

Insights

Artificial intelligence (AI) can detect hypertrophic cardiomyopathy (HCM) using electrocardiography (ECG). As HCM progresses to dilated HCM (dHCM), ECG analysis shows a shift in importance from the ST-T segment to the QRS complex.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Artificial intelligence (AI)-enhanced electrocardiography (ECG) shows promise for detecting hypertrophic cardiomyopathy (HCM) and its dilated phase (dHCM).
  • Specific ECG characteristics linked to HCM and dHCM require further characterization for improved diagnostic accuracy.

Purpose of the Study:

  • To identify specific ECG parameters with high importance for detecting different subtypes of HCM and dHCM.
  • To evaluate the diagnostic performance of AI-ECG models using comprehensive and reduced feature sets.

Main Methods:

  • Retrospective analysis of 19,170 patients, including 140 with HCM or dHCM, from the Shinken Database (2010-2017).
  • Analysis of 438 ECG parameters across P-wave, QRS complex, and ST-T segments.
  • Utilized univariate and multivariate logistic regression to determine parameter importance (HPI) and diagnostic accuracy (AUROC).

Main Results:

  • For HCM subtypes (basal and apical), high parameter importance (HPI) was concentrated in the ST-T segment and QRS complex.
  • In dilated HCM (dHCM), HPI shifted towards the QRS complex, with lower importance in the ST-T segment.
  • Area under the receiver operating characteristic curves (AUROC) for models using all ECG parameters were high across HCM subtypes (0.925–0.981).
  • Models using the top 10 HPI parameters demonstrated comparable predictive performance to models using all parameters for HCM subtypes.

Conclusions:

  • A shift in ECG parameter importance from the ST-T segment to the QRS complex correlates with HCM progression to dHCM.
  • Efficient AI-based diagnostic models for HCM can be developed using a reduced set of top ECG parameters, achieving performance similar to comprehensive models.
Abstract

Keywords:
diagnostic modelingdilated phase hypertrophic cardiomyopathydisease progressionelectrocardiogram parametershypertrophic cardiomyopathy

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