Electrocardiography Screening for Hypertrophic Cardiomyopathy
Matthew J Campbell1, Xuefu Zhou2, Chia Han2
1Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio.
Insights
Automated electrocardiogram (ECG) screening software shows promise for detecting hypertrophic cardiomyopathy (HCM) in athletes, offering high sensitivity and specificity. Further evaluation in diverse populations is recommended.
Area of Science:
- Cardiology
- Medical Diagnostics
- Computational Biology
Background:
- Hypertrophic cardiomyopathy (HCM) is a primary cause of sudden cardiac death in athletes.
- Current electrocardiogram (ECG) screening methods face criticism due to cost-effectiveness issues and high false-positive rates.
Purpose of the Study:
- To evaluate a highly automated software algorithm for high-throughput, population-based screening of HCM.
- To address limitations associated with traditional population-based screening programs.
Main Methods:
- A proprietary computed algorithm was developed using voltage and Seattle ECG criteria.
- Various cut points for Q-wave depth, Q-wave duration, ST depression, T-wave inversion, and left ventricular voltage were analyzed.
- Receiver operating characteristic curves were used to optimize algorithm sensitivity and specificity.
Main Results:
- The automated algorithm achieved 88.6% sensitivity and 98% specificity for HCM detection via ECG.
- Physician interpretation using Seattle Criteria yielded 90.2% sensitivity and 96% specificity.
- Optimized automated software demonstrated 98% sensitivity and 96% specificity, with improved sensitivity when voltage criteria were included.
Conclusions:
- Computer-automated ECG screening for HCM is a feasible approach.
- Larger-scale evaluations of automated ECG algorithms in diverse populations are necessary.
Background:
Hypertrophic cardiomyopathy (HCM) is the leading cause of sudden cardiac death in athletes. However, preparticipation electrocardiogram (ECG) screening has been criticized for failing to meet cost-effectiveness thresholds, in part because of high false-positive rates. We sought to evaluate whether a highly automated software algorithm could be used for a high throughput, population-based screening program and address several of the limitations seen with population-based screening
Methods:
A proprietary computed algorithm was created based on both voltage- as well as Seattle-based ECG criteria. Different cut points for Q-wave depth, Q-wave length, the degree of ST depression, the degree of T-wave inversion, and left ventricular voltage were analyzed for optimum sensitivity and specificity. After developing receiver operating characteristic curves for each criterion, different cut points were trialed together on our data set to obtain settings to optimize sensitivity and specificity.
Results:
The automated algorithm was capable of identifying patients with HCM based on ECG with 88.6% sensitivity and 98% specificity, compared to a sensitivity of 90.2% and specificity of 96% when the ECGs were read by physicians according to the Seattle Criteria. Adding voltage criteria improved the sensitivity of the algorithm with a mild decrease in specificity. Optimum sensitivity with this automated software was 98%; optimum specificity was 96%.
Conclusion:
Computer-automated ECG screening for HCM is feasible. Evaluation of automated ECG algorithms in larger and more diverse populations is warranted.
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