Surviving the Shocks: A Contemporary of Short-coupled Ventricular Fibrillation.

A young man presented following successful cardiac resuscitation after an out-of-hospital cardiac arrest. During his admission, he had multiple runs of short-coupled ventricular fibrillation with a similar morphology premature ventricular complex (PVC) trigger. He was brought to the electrophysiology laboratory, and, with a high dose of isoprenaline, the PVC was localised to the moderator band. Ablation induced short runs of ventricular tachycardia before elimination of the PVC. He subsequently underwent subcutaneous implantable cardiac defibrillator implantation before his discharge.

Relationship between wait times and post-atrial fibrillation ablation outcomes: A population-based study.

BACKGROUND: Rhythm control is a cornerstone of atrial fibrillation (AF) management. Shorter time between diagnosis of AF and receipt of catheter ablation is associated with greater rates of therapy success. Previous work considered diagnosis-to-ablation time as a binary or categorical variable and did not consider the unique risk profile of patients after a referral for ablation was made. OBJECTIVE: The purpose of this study was to comprehensively assess the impact of diagnosis-to-ablation and referral-to-ablation time on postprocedural outcomes at a population level. METHODS: This observational cohort study included patients who received catheter ablation to treat AF in Ontario, Canada. Patient demographics, medical comorbidities, AF diagnosis date, ablation referral date, and ablation date were collected. The primary outcomes of interest included a composite of death and hospitalization/emergency department visit for AF, heart failure, or ischemic stroke. Multivariable Cox models assessed the impact of diagnosis-to-ablation and referral-to-ablation times on the primary outcome. RESULTS: Our cohort included 7472 patients who received ablation for de novo AF between April 1, 2016, and March 31, 2022. Median [interquartile range] diagnosis-to-ablation time was 718 [399-1274] days and median referral-to-ablation time was 221 [117-363] days. Overall, 911 patients (12.2%) had the composite endpoint within 1 year of ablation. Increasing diagnosis-to-ablation time was associated with a greater incidence for the primary outcome (hazard ratio [HR]1.02; 95% confidence interval [CI] 1.01-1.02 per month). Increasing referral-to-ablation time did not impact the primary outcome (HR 1.00; 95% CI 0.98-1.01 per month). CONCLUSION: Delays between AF diagnosis and ablation referral may contribute to adverse postprocedural outcomes and provide an opportunity for health system quality improvements.

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes.

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical gaps remain, particularly within case ascertainment, access to genetic testing, and risk stratification. Artificial intelligence (AI), specifically machine learning and its subset deep learning, present promising solutions to these challenges. The capacity of AI to process vast amounts of patient data and identify disease patterns differentiates them from traditional methods, which are time- and resource-intensive. To date, AI models have shown immense potential in condition detection (including asymptomatic/concealed disease) and genotype and phenotype identification, exceeding expert cardiologists in these tasks. Additionally, they have exhibited applicability for general population screening, improving case ascertainment in a set of conditions that are often asymptomatic such as left ventricular dysfunction. Third, models have shown the ability to improve testing protocols; through model identification of disease and genotype, specific clinical testing (eg, drug challenges or further diagnostic imaging) can be avoided, reducing health care expenses, speeding diagnosis, and possibly allowing for more incremental or targeted genetic testing approaches. These significant benefits warrant continued investigation of AI, particularly regarding the development and implementation of clinically applicable screening tools. In this review we summarize key developments in AI, including studies in long QT syndrome, Brugada syndrome, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathies, and provide direction for effective future AI implementation in clinical practice.

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

Importance: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG). Objective: To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG. Design, Setting, and Participants: This diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals. Exposures: Convolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results. Main Outcomes and Measures: The main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection. Results: A total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN's high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval-based detection (AUC, 0.74; 95% CI, 0.69-0.78). Conclusions and Relevance: The deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.