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. 2023 Aug 4;18(1):40.
doi: 10.5334/gh.1250. eCollection 2023.

Identifying Obstructive Hypertrophic Cardiomyopathy from Nonobstructive Hypertrophic Cardiomyopathy: Development and Validation of a Model Based on Electrocardiogram Features

Affiliations

Identifying Obstructive Hypertrophic Cardiomyopathy from Nonobstructive Hypertrophic Cardiomyopathy: Development and Validation of a Model Based on Electrocardiogram Features

Lanyan Guo et al. Glob Heart. .

Abstract

Background: The clinical presentation and prognosis of hypertrophic cardiomyopathy (HCM) are heterogeneous between nonobstructive HCM (HNCM) and obstructive HCM (HOCM). Electrocardiography (ECG) has been used as a screening tool for HCM. However, it is still unclear whether the features presented on ECG could be used for the initial classification of HOCM and HNCM.

Objective: We aimed to develop a pragmatic model based on common 12-lead ECG features for the initial identification of HOCM/HNCM.

Methods: Between April 1st and September 30th, 2020, 172 consecutive HCM patients from the International Cooperation Center for Hypertrophic Cardiomyopathy of Xijing Hospital were prospectively included in the training cohort. Between January 4th and February 30th, 2021, an additional 62 HCM patients were prospectively included in the temporal internal validation cohort. External validation was performed using retrospectively collected ECG data with definite classification (390 HOCM and 499 HNCM ECG samples) from January 1st, 2010 to March 31st, 2020. Multivariable backward logistic regression (LR) was used to develop the prediction model. The discrimination performance, calibration and clinical utility of the model were evaluated.

Results: Of all 30 acquired ECG parameters, 10 variables were significantly different between HOCM and HNCM (all P < 0.05). The P wave interval and SV1 were selected to construct the model, which had a clearly useful C-statistic of 0.805 (0.697, 0.914) in the temporal validation cohort and 0.776 (0.746, 0.806) in the external validation cohort for differentiating HOCM from HNCM. The calibration plot, decision curve analysis, and clinical impact curve indicated that the model had good fitness and clinical utility.

Conclusion: The pragmatic model constructed by the P wave interval and SV1 had a clearly useful ability to discriminate HOCM from HNCM. The model might potentially serve as an initial classification of HCM before referring patients to dedicated centers and specialists.

Highlights: What are the novel findings of this work? Evident differences exist in the ECG presentations between HOCM and HNCM.To the best of our knowledge, this study is the first piece of evidence to quantify the difference in the ECG presentations between HOCM and HNCM.Based on routine 12-lead ECG data, a probabilistic model was generated that might assist in the initial classification of HCM patients.

Keywords: classification; electrocardiogram; hypertrophic cardiomyopathy; nonobstructive hypertrophic cardiomyopathy; obstructive hypertrophic cardiomyopathy.

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Conflict of interest statement

The authors have no competing interests to declare.

Figures

Study flow chart
Figure 1
Study flow chart. AF, atrial fibrillation; PM, pacemaker; BBB, bundle branch block; HCM, hypertrophic cardiomyopathy; HOCM, obstructive hypertrophic cardiomyopathy; HNCM, non-obstructive hypertrophic cardiomyopathy; LR, logistic regression.
The calibration and ROC curve of the model
Figure 2
The calibration and ROC curve of the model in the training, temporal, and external validation cohorts. Calibration plots between the predicted and observed HOCM patients in the training (A), temporal (B), and external validation (C) cohorts. The 45° blue line represents a perfect prediction, and the red line represents the predictive performance of the model. ROC curves of the training (D), temporal (E), and external (F) validation cohorts.
The relationship between LVOTG and the prediction score value
Figure 3
The relationship between LVOTG and the prediction score value.
Examples illustration
Figure 4
Examples illustration. A, The ECG of the patient from case 1 (HNCM); B, the prediction result of case 1 with a low HOCM probability; C, Peak LVOTG (12 mmHg) of the patient from case 1; D, The ECG of the patient from case 2 (HOCM); E, the prediction result of case 2 with a high HOCM probability; F, Peak LVOTG (92 mmHg) of the patient from case 2.

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References

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Grants and funding

The present work is financially supported by the National Key R&D Program of China (Grant No. 2018YFA0107400), and Program for Chang-Jiang Scholars and Innovative Research Team in University (Grant No. PCSIRT-14R08), and program for National Science Funds of China (Grant No. 82170358).

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