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

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Abstract

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.This study aims to investigate whether an explainable deep learning-based approach allows for risk prediction using only electrocardiogram (ECG) data.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.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).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.Copyright © 2024. Published by Elsevier Inc.