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Identifying Obstructive Hypertrophic Cardiomyopathy from Nonobstructive Hypertrophic Cardiomyopathy: Development and
Lanyan Guo1, Zhiling Ma1, Weiping Yang1
1Department of Cardiology, Xijing Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, China.
Insights
A new electrocardiogram (ECG) model using P wave interval and SV1 can differentiate obstructive hypertrophic cardiomyopathy (HOCM) from nonobstructive hypertrophic cardiomyopathy (HNCM). This tool aids in initial HCM patient classification.
Area of Science:
- Cardiology
- Medical Diagnostics
- Biomedical Engineering
Background:
- Hypertrophic cardiomyopathy (HCM) presents heterogeneously in obstructive (HOCM) and nonobstructive (HNCM) forms.
- Electrocardiography (ECG) is a common screening tool, but its utility for initial HOCM/HNCM classification is unclear.
Purpose of the Study:
- To develop a pragmatic model using standard 12-lead ECG features for initial HOCM/HNCM identification.
- To assess the model's diagnostic performance, calibration, and clinical utility.
Main Methods:
- Prospective data from 172 HCM patients for training and 62 for temporal validation.
- External validation using 889 retrospectively collected ECG samples (390 HOCM, 499 HNCM).
- Multivariable logistic regression to build the prediction model.
Main Results:
- Ten ECG parameters differed significantly between HOCM and HNCM (P < 0.05).
- A model using P wave interval and SV1 achieved a C-statistic of 0.805 (temporal) and 0.776 (external) for HOCM/HNCM differentiation.
- Model demonstrated good calibration and clinical utility.
Conclusions:
- The P wave interval and SV1 model effectively discriminates HOCM from HNCM.
- This pragmatic ECG-based model can assist in the initial classification of HCM patients.
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.
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