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 for development of malignant ventricular arrhythmia (MVA). Accurate risk stratification allows timely implantation of intracardiac defibrillators and is currently performed with a multimodality prediction model. Objective: This study aimed to investigate whether an explainable deep learning–based approach allows risk prediction with 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 that summarizes it into 32 explainable factors. Prediction models were developed by 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 = .054) 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 &lt; .001). Clinical simulations showed that a 2-step approach, with ECG-only screening followed by a full workup, resulted in 60% less additional diagnostics while outperforming 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 who need additional diagnostic testing and follow-up.</p

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia:a landmark study

Aims:Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive. Our aim is to evaluate the diagnostic performance of the PLN p.(Arg14del) risk model at 3-year follow-up. Methods:We performed a landmark analysis 3 years after presentation and selected only patients with no prior major VA. Data were and results collected of 268 PLN p.(Arg14del)-positive subjects, aged 43.5 ± 16.3 years, 38.9% male. After the 3 years landmark, subjects had a mean follow-up of 4.0 years (± 3.5 years) and 28 (10%) subjects experienced major VA with an annual event rate of 2.6% [95% confidence interval (CI) 1.6–3.6], defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. The PLN p.(Arg14del) risk score yielded good discrimination in the 3 years landmark cohort with a C-statistic of 0.83 (95% CI 0.79–0.87) and calibration slope of 0.97. Conclusion:The PLN p.(Arg14del) risk model has sustained good model performance up to 3 years follow-up in PLN p.(Arg14del)positive subjects with no history of major VA. It may therefore be used to support decision-making for primary prevention ICD implantation not merely at presentation but also up to at least 3 years of follow-up.</p

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia:a landmark study

Aims:Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive. Our aim is to evaluate the diagnostic performance of the PLN p.(Arg14del) risk model at 3-year follow-up. Methods:We performed a landmark analysis 3 years after presentation and selected only patients with no prior major VA. Data were and results collected of 268 PLN p.(Arg14del)-positive subjects, aged 43.5 ± 16.3 years, 38.9% male. After the 3 years landmark, subjects had a mean follow-up of 4.0 years (± 3.5 years) and 28 (10%) subjects experienced major VA with an annual event rate of 2.6% [95% confidence interval (CI) 1.6–3.6], defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. The PLN p.(Arg14del) risk score yielded good discrimination in the 3 years landmark cohort with a C-statistic of 0.83 (95% CI 0.79–0.87) and calibration slope of 0.97. Conclusion:The PLN p.(Arg14del) risk model has sustained good model performance up to 3 years follow-up in PLN p.(Arg14del)positive subjects with no history of major VA. It may therefore be used to support decision-making for primary prevention ICD implantation not merely at presentation but also up to at least 3 years of follow-up.</p

Prediction of ventricular arrhythmia in phospholamban p.Arg14del mutation carriers-reaching the frontiers of individual risk prediction

AIMS: This study aims to improve risk stratification for primary prevention implantable cardioverter defibrillator (ICD) implantation by developing a new mutation-specific prediction model for malignant ventricular arrhythmia (VA) in phospholamban (PLN) p.Arg14del mutation carriers. The proposed model is compared to an existing PLN risk model. METHODS AND RESULTS: Data were collected from PLN p.Arg14del mutation carriers with no history of malignant VA at baseline, identified between 2009 and 2020. Malignant VA was defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. A prediction model was developed using Cox regression. The study cohort consisted of 679 PLN p.Arg14del mutation carriers, with a minority of index patients (17%) and male sex (43%), and a median age of 42 years [interquartile range (IQR) 27–55]. During a median follow-up of 4.3 years (IQR 1.7–7.4), 72 (10.6%) carriers experienced malignant VA. Significant predictors were left ventricular ejection fraction, premature ventricular contraction count/24 h, amount of negative T waves, and presence of low-voltage electrocardiogram. The multivariable model had an excellent discriminative ability {C-statistic 0.83 [95% confidence interval (CI) 0.78–0.88]}. Applying the existing PLN risk model to the complete cohort yielded a C-statistic of 0.68 (95% CI 0.61–0.75). CONCLUSION: This new mutation-specific prediction model for individual VA risk in PLN p.Arg14del mutation carriers is superior to the existing PLN risk model, suggesting that risk prediction using mutation-specific phenotypic features can improve accuracy compared to a more generic approach

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 for development of malignant ventricular arrhythmia (MVA). Accurate risk stratification allows timely implantation of intracardiac defibrillators and is currently performed with a multimodality prediction model. Objective: This study aimed to investigate whether an explainable deep learning–based approach allows risk prediction with 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 that summarizes it into 32 explainable factors. Prediction models were developed by 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 = .054) 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 < .001). Clinical simulations showed that a 2-step approach, with ECG-only screening followed by a full workup, resulted in 60% less additional diagnostics while outperforming 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 who need additional diagnostic testing and follow-up

New risk factors for atrial fibrillation:causes of 'not-so-lone atrial fibrillation'

Atrial fibrillation (AF) is a prevalent arrhythmia in patients with cardiovascular disease. The classical risk factors for developing AF include hypertension, valvular disease, (ischaemic) cardiomyopathy, diabetes mellitus, and thyroid disease. In some patients with AF, no underlying (cardiovascular) pathology is present and the aetiology remains unknown. This condition is known as lone AF. However, in recent years, other factors playing a role in the genesis of AF have gained attention, including obesity, sleep apnoea, alcohol abuse and other intoxications, excessive sports practice, latent hypertension, genetic factors, and inflammation. In this review, we address these 'new risk factors' (i.e. as opposed to the classical risk factors) and the mechanisms by which they lead to AF

Long-term outcome of the atrioventricular node ablation and pacemaker implantation for symptomatic refractory atrial fibrillation

AIMS: To investigate long-term outcome and to determine predictors of development of heart failure (HF) in patients with atrioventricular (AV) node ablation and permanent right ventricular pacing because of symptomatic refractory atrial fibrillation (AF). BACKGROUND: Atrioventricular node ablation and subsequent permanent pacing is a well-established therapy for patients with AF. Long-term right ventricular pacing may induce HF. METHODS AND RESULTS: In 121 (45 with previous HF) patients with drug refractory AF, AV node ablation and implantation of a pacemaker was performed. At baseline and after a mean follow-up of 4.3 +/- 3.3 years, New York Heart Association (NYHA) functional class for HF and left ventricular (LV) and atrial diameters were assessed. During and at the end of follow-up, hospitalizations for HF, mortality, and quality of life were assessed using the SF-36 and an AVN-specific questionnaire. No significant changes in NYHA functional class (87 vs. 77% in NYHA I/II at baseline vs. end of follow-up) and LV end diastolic diameter (51 +/- 7 vs. 52 +/- 8 mm) were observed. Left ventricular end systolic diameter decreased (from 37 +/- 9 to 34 +/- 7 mm, P = 0.03) and fractional shortening improved (from 28 +/- 10 to 34 +/- 9, P = 0.02) in all patients and in patients with previous HF, but not in patients without previous HF. Hospitalizations for HF occurred in 24 patients (20%), predominantly those with previous HF. All-cause mortality occurred in 31 (26%) patients. At the end of follow-up, quality of life was comparable with the control group. CONCLUSION: Long-term outcome of AV node ablation and permanent pacing is good. Atrioventricular node ablation remains a treatment option for AF