Key result
A deep neural network successfully identified PLN mutation carriers from 12-lead ECGs with a c-statistic of 0.95 (95% CI, 0.91-0.99) and discovered novel disease-specific features.
Why the study?
ECG interpretation in rare cardiac diseases is challenging, and deep neural networks may discover complex novel features that could play a pathophysiological role.
Does a deep neural network improve the detection and discovery of disease-specific ECG features in PLN p.Arg14del mutation carriers?
Case-Control (n=1,806)
Does a deep neural network improve the detection and discovery of disease-specific ECG features in PLN p.Arg14del mutation carriers?
Effect estimate: c-statistic 0.95 (95% CI 0.91-0.99)
A deep learning approach successfully identified and visualized both established and novel disease-specific ECG features in PLN mutation carriers, potentially improving diagnostic capabilities.
Background: ECG interpretation requires expertise and is mostly based on physician recognition of specific patterns, which may be challenging in rare cardiac diseases. Deep neural networks (DNNs) can discover complex features in ECGs and may facilitate the detection of novel features which possibly play a pathophysiological role in relatively unknown diseases. Using a cohort of PLN (phospholamban) p.Arg14del mutation carriers, we aimed to investigate whether a novel DNN-based approach can identify established ECG features, but moreover, we aimed to expand our knowledge on novel ECG features in these patients. Methods: A DNN was developed on 12-lead median beat ECGs of 69 patients and 1380 matched controls and independently evaluated on 17 patients and 340 controls. Differentiating features were visualized using Guided Gradient Class Activation Mapping++. Novel ECG features were tested for their diagnostic value by adding them to a logistic regression model including established ECG features. Results: The DNN showed excellent discriminatory performance with a c-statistic of 0.95 (95% CI, 0.91–0.99) and sensitivity and specificity of 0.82 and 0.93, respectively. Visualizations revealed established ECG features (low QRS voltages and T-wave inversions), specified these features (eg, R- and T-wave attenuation in V2/V3) and identified novel PLN-specific ECG features (eg, increased PR-duration). The logistic regression baseline model improved significantly when augmented with the identified features ( P <0.001). Conclusions: A DNN-based feature detection approach was able to discover and visualize disease-specific ECG features in PLN mutation carriers and revealed yet unidentified features. This novel approach may help advance diagnostic capabilities in daily practice.
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Leur et al. (2021) conducted a case-control in PLN (phospholamban) p.Arg14del mutation (n=1,806). Deep neural network (DNN) based ECG feature detection vs. Matched controls was evaluated on Discriminatory performance (c-statistic) for detecting PLN mutation carriers (c-statistic 0.95, 95% CI 0.91-0.99). A deep neural network successfully identified PLN mutation carriers from 12-lead ECGs with a c-statistic of 0.95 (95% CI, 0.91-0.99) and discovered novel disease-specific features.
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