ArticleJournal of arrhythmia2025
Electrocardiographic parameter profiles for differentiating hypertrophic cardiomyopathy stages.
Article in Journal of arrhythmia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Development and evaluation of an artificial intelligence-based electrocardiogram prediction model for emergency chest pain patients.Frontiers in medicine · 2026Article
- Electrocardiographic parameter profiles for differentiating hypertrophic cardiomyopathy stages.Journal of arrhythmia · 2025Article
- Electrocardiographic parameter profiles for differentiating hypertrophic cardiomyopathy stages.Journal of arrhythmia · 2025Article
- Critical evaluation of ECG parameter analysis in hypertrophic cardiomyopathy staging: A closer look.Journal of arrhythmia · 2025Article
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Authors and funding
14 authors.
Funding
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Abstract
Background: The efficacy of artificial intelligence (AI)-enhanced electrocardiography (ECG) for detecting hypertrophic cardiomyopathy (HCM) and its dilated phase (dHCM) has been developed, though specific ECG characteristics associated with these conditions remain insufficiently characterized. Methods: This retrospective study included 19,170 patients, with 140 HCM or dHCM cases, from the Shinken Database (2010-2017). The 140 cases (HCM-total) were categorized into basal-only HCM (HCM-basal, Results: In HCM-basal and HCM-apical, HPI was predominantly observed in the ST-T segment (49% and 51%, respectively), followed by the QRS complex (29% and 27%). For dHCM, HPI was lower in the ST-T segment (16%) and QRS complex (22%). The P-wave had low HPI across all subtypes. AUROCs for models with total ECG parameters were 0.925 for HCM-basal, 0.981 for HCM-apical, and 0.969 for dHCM. While AUROCs for the top 10 HPI models were lower than the total ECG parameter model for HCM total, they were comparable across HCM subtypes. Conclusions: As HCM progresses to dHCM, a shift in HPI from the ST-T segment to the QRS complex provides clinically relevant insights. For HCM subtypes, the top 10 ECG parameters yield predictive performance similar to the full parameter set, supporting efficient approaches for AI-based diagnostic models.
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