Background: The Subcutaneous ICD (S-ICD) is safe and effective for sudden cardiac death prevention. However, patients in previous S-ICD studies had fewer comorbidities, less left ventricular dysfunction and received more inappropriate shocks (IAS) than in typical transvenous (TV)-ICD trials. The UNTOUCHED trial was designed to evaluate the IAS rate in a more typical, contemporary ICD patient population implanted with the S-ICD using standardized programming and enhanced discrimination algorithms. Methods: Primary prevention patients with left ventricular ejection fraction (LVEF) ≤ 35% and no pacing indications were included. Generation 2 or 3 S-ICD devices were implanted and programmed with rate-based therapy delivery for rates ≥ 250 beats per minute (bpm) and morphology discrimination for rates ≥200 and < 250 bpm. Patients were followed for 18 months. The primary endpoint was the IAS free rate compared to a 91.6% performance goal, derived from the results for the ICD-only patients in the MADIT-RIT study. Kaplan-Meier analyses were performed to evaluate event-free rates for IAS, all cause shock, and complications. Multivariable proportional hazard analysis was performed to determine predictors of endpoints. Results: S-ICD implant was attempted in 1116 patients and 1111 patients were included in post-implant follow-up analysis. The cohort had a mean age of 55.8±12.4 years, 25.6% women, 23.4% black race, 53.5% with ischemic heart disease, 87.7% with symptomatic heart failure and a mean LVEF of 26.4±5.8%. Eighteen-month freedom from IAS was 95.9% (Lower confidence limit LCL 94.8%). Predictors of reduced incidence of IAS were implanting the most recent generation of device, using the three-incision technique, no history of atrial fibrillation, and ischemic etiology. The 18-month all cause shock free rate was 90.6% (LCL 89.0%), meeting the prespecified performance goal of 85.8%. Conversion success rate for appropriate, discrete episodes was 98.4%. Complication free rate at 18 months was 92.7%. Conclusions: This study demonstrates high efficacy and safety with contemporary S-ICD devices and programming despite the relatively high incidence of co-morbidities in comparison to earlier S-ICD trials. The inappropriate shock rate (3.1% at one year) is the lowest reported for the S-ICD and lower than many TV ICD studies using contemporary programming to reduce IAS. Clinical Trial Registration: URL https://clinicaltrials.gov Unique Identifier NCT02433379
BackgroundThere are limited data describing sex specificities regarding implantable cardioverter defibrillators (ICDs) in the real‐world European setting.Methods and ResultsUsing a large multicenter cohort of consecutive patients referred for ICD implantation for primary prevention (2002–2012), in ischemic and nonischemic cardiomyopathy, we examined the sex differences in subjects' characteristics and outcomes. Of 5539 patients, only 837 (15.1%) were women and 53.8% received cardiac resynchronization therapy. Compared to men, women presented a significantly higher proportion of nonischemic cardiomyopathy (60.2% versus 36.2%, P<0.001), wider QRS complex width (QRS >120 ms: 74.6% versus 68.5%, P=0.003), higher New York Heart Association functional class (≥III in 54.2%♀ versus 47.8%♂, P=0.014), and lower prevalence of atrial fibrillation (18.7% versus 24.9%, P<0.001). During a 16 786 patient‐years follow‐up, overall, fewer appropriate therapies were observed in women (hazard ratio=0.59, 95% CI 0.45–0.76; P<0.001). By contrast, no sex‐specific interaction was observed for inappropriate shocks (odds ratio ♀=0.84, 95% CI 0.50–1.39, P=0.492), early complications (odds ratio=1.00, 95% CI 0.75–1.32, P=0.992), and all‐cause mortality (hazard ratio=0.87 95% CI 0.66–1.15, P=0.324). Analysis of sex‐by‐ cardiac resynchronization therapy interaction shows than female cardiac resynchronization therapy recipients experienced fewer appropriate therapies than men (hazard ratio=0.62, 95% CI 0.50–0.77; P<0.001) and lower mortality (hazard ratio=0.68, 95% CI 0.47–0.97; P=0.034).ConclusionsIn our real‐life registry, women account for the minority of ICD recipients and presented with a different clinical profile. Whereas female cardiac resynchronization therapy recipients had a lower incidence of appropriate ICD therapies and all‐cause death than their male counterparts, the observed rates of inappropriate shocks and early complications in all ICD recipients were comparable.Clinical Trial RegistrationURL: https://www.clinicaltrials.gov/. Unique identifier: NCT01992458.
Aims Deep neural networks (DNNs) perform excellently in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction (EF). Despite these promising developments, implementation is hampered by the lack of trustworthy techniques to explain the algorithms to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods and results We present a novel pipeline consisting of a variational auto-encoder (VAE) to learn the underlying factors of variation of the median beat ECG morphology (the FactorECG), which are subsequently used in common and interpretable prediction models. As the ECG factors can be made explainable by generating and visualizing ECGs on both the model and individual level, the pipeline provides improved explainability over heatmap-based methods. By training on a database with 1.1 million ECGs, the VAE can compress the ECG into 21 generative ECG factors, most of which are associated with physiologically valid underlying processes. Performance of the explainable pipeline was similar to ‘black box’ DNNs in conventional ECG interpretation (AUROC 0.94 vs 0.96), detection of reduced EF (AUROC 0.90 vs 0.91) and prediction of one-year mortality (AUROC 0.76 vs 0.75). Contrary to the ‘black box’ DNNs, our pipeline provided explainability on which morphological ECG changes were important for prediction. Results were confirmed in a population-based external validation dataset. Conclusions Future studies on DNNs for ECGs should employ pipelines that are explainable to facilitate clinical implementation by gaining confidence in artificial intelligence and making it possible to identify biased models.
Aims Wearable devices are transforming the ECG into a ubiquitous medical test. This study assesses the association between premature ventricular and atrial contractions (PVCs and PACs) detected on wearable-format ECGs (15-second single-lead) and cardiovascular outcomes in individuals without cardiovascular disease (CVD). Methods and results PACs and PVCs were identified in 15-second single-lead ECGs from N=54,016 UK Biobank participants (median age, interquartile range, age 58, 50-63 years, 54% female). Cox regression models adjusted for traditional risk factors were used to determine associations with atrial fibrillation (AF), heart failure (HF), myocardial infarction (MI), stroke, life-threatening ventricular arrhythmias (LTVA), and mortality over a period of 11.5 (11.4-11.7) years. The strongest associations were found between PVCs (prevalence 2.2%) and HF (hazard ratio, HR, 95% confidence interval =1.88, 1.36-2.59) and between PACs (prevalence 1.9%) and AF (HR= 2.52, 2.11-3.01), with shorter prematurity further increasing risk. PVCs and PACs were also associated with LTVA (p<0.05). Associations with MI, stroke and mortality were only significant in unadjusted models. In a separate UK Biobank sub-study sample (UKB-2, N=29,324, age 64, 58-60 years, 54% female, follow-up 3.5 (2.6-4.8) years) used for independent validation, after adjusting for risk factors, PACs were associated with AF (HR= 1.80, 1.12-2.89), and PVCs with HF (HR= 2.32, 1.28-4.22). Conclusion In middle-aged individuals without CVD, premature contractions identified in 15-second single-lead ECGs are strongly associated with increased risk of AF and HF. These data warrant further investigation to assess the role of wearable ECGs for early cardiovascular risk stratification.
The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality1,2. We performed multi-ancestry (N=293,051) and European only (N=271,570) genome-wide association (GWAS) meta-analyses for the PR interval, discovering 210 loci of which 149 are novel. Variants at all loci nearly doubled the percentage of heritability explained, from 33.5% to 62.6%. We observed enrichment for genes involved in cardiac muscle development/contraction and the cytoskeleton highlighting key regulation processes for atrioventricular conduction. Additionally, 19 novel loci harbour genes underlying inherited monogenic heart diseases suggesting the role of these genes in cardiovascular pathology in the general population. We showed that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease risk, including distal conduction disease, AF, atrioventricular pre-excitation, non-ischemic cardiomyopathy, and coronary heart disease. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.
Background Deep neural networks (DNNs) show excellent performance in interpreting electrocardiograms (ECGs), both for conventional ECG interpretation and for novel applications such as detection of reduced ejection fraction and prediction of one-year mortality. Despite these promising developments, clinical implementation is severely hampered by the lack of trustworthy techniques to explain the decisions of the algorithm to clinicians. Especially, currently employed heatmap-based methods have shown to be inaccurate. Methods We present a novel approach that is inherently explainable and uses an unsupervised variational auto-encoder (VAE) to learn the underlying factors of variation of the ECG (the FactorECG) in a database with 1.1 million ECG recordings. These factors are subsequently used in a pipeline with common and interpretable statistical methods. As the ECG factors are explainable by generating and visualizing ECGs on both the model- and individual patient-level, the pipeline becomes fully explainable. The performance of the pipeline is compared to a state-of-the-art black box DNN in three tasks: conventional ECG interpretation with 35 diagnostic statements, detection of reduced ejection fraction and prediction of one-year mortality. Results The VAE was able to compress the ECG into 21 generative ECG factors, which are associated with physiologically valid underlying anatomical and (patho)physiological processes. When applying the novel pipeline to the three tasks, the explainable FactorECG pipeline performed similar to state-of-the-art black box DNNs in conventional ECG interpretation (AUROC 0.94 vs 0.96), detection of reduced ejection fraction (AUROC 0.90 vs 0.91) and prediction of one-year mortality (AUROC 0.76 vs 0.75). Contrary to state-of-the-art, our pipeline provided inherent explainability on which morphological ECG features were important for prediction or diagnosis. Conclusion Future studies should employ DNNs that are inherently explainable to facilitate clinical implementation by gaining confidence in artificial intelligence, and more importantly, making it possible to identify biased or inaccurate models.
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