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Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach's efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.

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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.

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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.

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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.

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Dorsal root ganglion stimulation (DRG-S) is a relatively new neuromodulation technique that has shown promising results in the treatment of chronic pain conditions. We present a case of a difficult lead extraction during the explantation of a DRG-S device. The lead was unable to be removed despite multiple attempts until a sheath and stylet were used to facilitate extraction. As DRG-S utilization becomes more widespread, DRG-S device explantation will inevitably become more common. The technique described in this report may be beneficial in certain cases of difficult DRG-S lead extraction.

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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.

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Cardiovascular conditions are among the most frequent causes of impairment to drive, because they might induce unpredictable mental state alterations via diverse mechanisms like myocardial ischemia, cardiac arrhythmias, and vascular dysfunction. Accordingly, health professionals are often asked to assess patients' fitness to drive (FTD). The Canadian Cardiovascular Society previously published FTD guidelines in 2003-2004; herein, we present updated FTD guidelines. Because there are no randomized trials on FTD, observational studies were used to estimate the risk of driving impairment in each situation, and recommendations made on the basis of Canadian Cardiovascular Society Risk of Harm formula. More restrictive recommendations were made for commercial drivers, who spend longer average times behind the wheel, use larger vehicles, and might transport a larger number of passengers. We provide guidance for individuals with: (1) active coronary artery disease; (2) various forms of valvular heart disease; (3) heart failure, heart transplant, and left ventricular assist device situations; (4) arrhythmia syndromes; (5) implantable devices; (6) syncope history; and (7) congenital heart disease. We suggest appropriate waiting times after cardiac interventions or acute illnesses before driving resumption. When short-term driving cessation is recommended, recommendations are on the basis of expert consensus rather than the Risk of Harm formula because risk elevation is expected to be transient. These recommendations, although not a substitute for clinical judgement or governmental regulations, provide specialists, primary care providers, and allied health professionals with a comprehensive list of a wide range of cardiac conditions, with guidance provided on the basis of the level of risk of impairment, along with recommendations about ability to drive and the suggested duration of restrictions.

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BACKGROUND: The prognostic association between dysnatremia and outcomes in out-of-hospital cardiac arrest (OHCA) is not well understood. Given hypernatremia is associated with poor outcomes in critical illness and hyponatremia may exacerbate cerebral edema, we hypothesized that dysnatremia on OHCA hospital admission would be associated with worse neurological outcomes. METHODS: We studied adults (≥19 years) with non-traumatic OHCA between 2009 and 2016 who were enrolled in the British Columbia Cardiac Arrest Registry and survived to hospital admission at 2 quaternary urban hospitals. We stratified cases by admission serum sodium into hyponatremic (<135 mmol/L), normonatremic (135-145 mmol/L), and hypernatremic (>145 mmol/L) groups. We used logistic regression models, adjusted for age, sex, shockable rhythm, admission serum lactate, and witnessed arrest, to estimate the association between admission sodium and favorable neurological outcome (cerebral performance category 1-2 or modified Rankin scale 0-3). RESULTS: Of 414 included patients, 63 were hyponatremic, 330 normonatremic, and 21 hypernatremic. In each respective group, 21 (33.3%), 159 (48.2%), and 3 (14.3%) experienced good neurological outcomes. In univariable models, hyponatremia (OR 0.53, 95% CI 0.30-0.93) and hypernatremia (OR 0.19, 95% CI 0.05-0.65) were associated with lower odds of good neurological outcomes compared to the normonatremia group. After adjustment, only hypernatremia was associated with lower odds of good neurological outcomes (OR 0.22, 95% CI 0.05-0.98). CONCLUSIONS: Hypernatremia at admission was independently associated with decreased probability of good neurological outcomes at discharge post-OHCA. Future studies should focus on elucidating the pathophysiology of dysnatremia following OHCA.

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BACKGROUND: There is growing evidence that mitral valve prolapse (MVP) is associated with otherwise unexplained cardiac arrest (UCA). However, reports are hindered by the absence of a systematic ascertainment of alternative diagnoses. OBJECTIVES: This study reports the prevalence and characteristics of MVP in a large cohort of patients with UCA. METHODS: Patients were enrolled following an UCA, defined as cardiac arrest with no coronary artery disease, preserved left ventricular ejection fraction, and no apparent explanation on electrocardiogram. A comprehensive evaluation was performed, and patients were diagnosed with idiopathic ventricular fibrillation (IVF) if no cause was found. Echocardiography reports were reviewed for MVP. Patients with MVP were divided into 2 groups: those with IVF (AMVP) and those with an alternative diagnosis (nonarrhythmic MVP). Patient characteristics were then compared. The long-term outcomes of AMVP were reported. RESULTS: Among 571 with an initially UCA, 34 patients had MVP (6%). The prevalence of definite MVP was significantly higher in patients with IVF than those with an alternative diagnosis (24 of 366 [6.6%] vs 5 of 205 [2.4%]; P = 0.03). Bileaflet prolapse was significantly associated with AMVP (18 of 23 [78%] vs 1 of 8 [12.5%]; P = 0.001; OR: 25.2). The proportion of patients with AMVP who received appropriate implantable cardioverter-defibrillator therapies over a median follow-up of 42 months was 21.1% (4 of 19). CONCLUSIONS: MVP is associated with otherwise UCA (IVF), with a prevalence of 6.6%. Bileaflet prolapse appears to be a feature of AMVP, although future studies need to ascertain its independent association. A significant proportion of patients with AMVP received appropriate implantable cardioverter-defibrillator therapies during follow-up.

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BACKGROUND: High-power, short duration (HPSD) radiofrequency ablation (RFA) is a commonly used strategy for pulmonary vein isolation (PVI). OBJECTIVES: This study sought to compare HPSD with standard power, standard duration (SPSD) RFA in patients undergoing PVI. METHODS: Patients with paroxysmal or persistent (<1 year) atrial fibrillation (AF) were randomized to HPSD (50 W) or SPSD (25-30 W) RFA to achieve PVI. Outcomes assessed included time to achieve PVI (primary), left atrial dwell time, total procedure time, first-pass isolation, PV reconnection with adenosine, procedure complications including asymptomatic cerebral emboli (ACE), and freedom from atrial arrhythmias. RESULTS: Sixty patients (median age 66 years; 75% male) with paroxysmal (57%) or persistent (43%) AF were randomized to HPSD (n = 29) or SPSD (n = 31). Median time to achieve PVI was shorter with HPSD vs SPSD (87 minutes vs 126 minutes; P = 0.003), as was left atrial dwell time (157 minutes vs 180 minutes; P = 0.04). There were no differences in first-pass isolation (79% vs 76%; P = 0.65) or PV reconnection with adenosine (12% vs 20%; P = 0.26) between groups. At 12 months, recurrent atrial arrhythmias occurred less in the HPSD group compared with the SPSD group (n = 3 of 29 [10%] vs n = 11 of 31 [35%]; HR: 0.26; P = 0.027). There was a trend toward more ACE with HPSD RFA (40% HPSD vs 17% SPSD; P = 0.053). CONCLUSIONS: In patients undergoing AF ablation, HPSD compared with SPSD RFA results in shorter time to achieve PVI, greater freedom from AF at 12 months, and a trend toward increased ACE.

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Iatrogenic atrioventricular (AV) block can occur in the context of cardiac surgery, percutaneous transcatheter, or electrophysiologic procedures. In cardiac surgery, patients undergoing aortic and/or mitral valve surgery are at the highest risk for developing perioperative AV block requiring permanent pacemaker implantation. Similarly, patients undergoing transcatheter aortic valve replacement are also at increased risk for developing AV block. Electrophysiologic procedures, including catheter ablation of AV nodal re-entrant tachycardia, septal accessory pathways, para-Hisian atrial tachycardia, or premature ventricular complexes, are also associated with risk of AV conduction system injury. In this article, we summarize the common causes for iatrogenic AV block, predictors for AV block, and general management considerations.

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