ECG-only Explainable Deep Learning Algorithm Predicts the Risk for Malignant Ventricular Arrhythmia in Phospholamban Cardiomyopathy.

BACKGROUND: Phospholamban (PLN) p.(Arg14del) variant carriers are at risk of developing malignant ventricular arrhythmias (MVA). Accurate risk stratification allows for timely implantation of intracardiac defibrillators (ICD) and is currently performed using a multimodality prediction model.

OBJECTIVE: This study aims to investigate whether an explainable deep learning-based approach allows for risk prediction using only electrocardiogram (ECG) data.

METHODS: A total of 679 PLN p.(Arg14del) carriers without MVA at baseline were identified. A deep learning-based variational auto-encoder, trained on 1.1 million ECGs, was used to convert the 12-lead baseline ECG into its FactorECG, a compressed version of the ECG which summarizes it into 32 explainable factors. Prediction models were developed using Cox regression.

RESULTS: The deep learning-based ECG-only approach was able to predict MVA with a c-statistic of 0.79 [95% CI 0.76 - 0.83], comparable to the current prediction model (c-statistic 0.83 [95% CI 0.79 - 0.88], p = 0.064) and outperforming a model based on conventional ECG parameters (low voltage ECG and negative T waves; c-statistic 0.65 [95% CI 0.58 - 0.73], p < 0.001). Clinical simulations showed that a two-step approach, with ECG-only screening followed by a full work-up, resulted in 60% less additional diagnostics, while outperforming the use of the multimodal prediction model in all patients. A visualization tool was created to provide interactive visualizations (https://pln.ecgx.ai).

CONCLUSION: Our deep learning-based algorithm based on ECG data only accurately predicts the occurrence of MVA in PLN p.(Arg14del) carriers, enabling more efficient stratification of patients that need additional diagnostic testing and follow-up.