Critical evaluation of ECG parameter analysis in hypertrophic cardiomyopathy staging: A closer look

We commend the authors for their comprehensive study, “Electrocardiographic Parameter Profiles for Differentiating Hypertrophic Cardiomyopathy Stages” (Hirota et al.) in the Journal of Arrhythmia.1 While the study provides valuable insights into ECG parameter variations across various stages of hypertrophic cardiomyopathy (HCM), we believe that some aspects of the study's design and interpretation require further examination. Below are several critical points that challenge the findings or methodological choices presented in the article. Hirota et al. attempt to define a set of ECG parameters that can differentiate HCM from its dilated phase (dHCM), yet they fail to account for the well-established heterogeneity of ECG manifestations across HCM subtypes. For example, in HCM-apical cases, T-wave inversions are often observed, whereas dHCM cases may present more subtle ECG changes because of the progression of left ventricular dysfunction. A study by Hughes et al. emphasizes that distinct ECG abnormality patterns are typically seen in the apical variant compared to the basal form.2 The authors' grouping of all HCM types may lead to overlooking crucial features that could impact differential diagnosis. A more subtype-specific analysis would provide clearer, actionable insights for clinicians. While the authors rely on AI-enhanced ECG analysis, they do not adequately discuss inter-observer variability, which is a known issue in ECG interpretation. AI models, while promising, can be susceptible to errors in clinical environments where interpretations by multiple clinicians may vary. A study by Sharma et al. shows that ECG interpretation in HCM is highly dependent on the experience of the practitioner, with significant inter-observer variability leading to inconsistent results.3 Although AI can help mitigate some of these issues, the authors' study does not sufficiently address the limitations of their model in handling such clinical variability or the real-world challenges of using AI models across different healthcare settings. The study overlooks the fact that coexisting conditions, such as hypertension, atrial fibrillation, and diabetes, which are prevalent in patients with HCM, can significantly influence ECG readings. For example, left ventricular hypertrophy because of hypertension may present similarly to HCM on an ECG, particularly in terms of QRS complex alterations, but these conditions require different management strategies. Hwang et al. and Mekhaimar et al. argue that failure to account for such comorbidities in diagnostic models for HCM can lead to incorrect classification, reducing diagnostic accuracy.4, 5 The absence of adjustment for these confounding factors in the authors' analysis diminishes the clinical applicability and external validity of their findings. In conclusion, while Hirota et al.'s study offers valuable insights into ECG parameters for differentiating hypertrophic cardiomyopathy stages, it overlooks critical aspects such as the heterogeneity of ECG presentations across subtypes, inter-observer variability in ECG interpretation, and the impact of coexisting conditions. A more nuanced and detailed approach, incorporating these factors, would significantly enhance the study's clinical applicability and accuracy. Further research should aim to refine these models and address these gaps for more reliable and effective diagnosis in real-world clinical settings. Sincerely, Brijesh Sathian No funding was received for the preparation or submission of this letter. The authors declare no conflicts of interest related to this letter. As this is a commentary on a published study and no new data were collected or analyzed, ethics approval was not required. All data referenced in this letter are publicly available in the original publication and cited sources. No new data was generated or analyzed in the preparation of this letter.