Citations

Abstract Aims In catecholaminergic polymorphic ventricular tachycardia (CPVT), the exercise-stress test (EST) is the cornerstone for the diagnosis, risk stratification, and assessment of therapeutic efficacy, but its repeatability is unknown. We aimed to test the repeatability of ventricular arrhythmia characteristics on the EST in patients with CPVT. Methods and results EST-pairs (ESTs performed within 18 months between 2005 and 2021, on the same protocol, and without or on the exact same treatment) of patients with RYR2-mediated CPVT from two specialized centres were included. The primary endpoint was the repeatability of the maximum ventricular arrhythmia score [VAS: 0 for the absence of premature ventricular contractions (PVCs); 1 for isolated PVCs; 2 for bigeminal PVCs; 3 for couplets; and 4 for non-sustained ventricular tachycardia]. Secondary outcomes were the repeatability of the heart rate at the first PVC and the ΔVAS (the absolute difference in VAS between the EST-pairs). A total of 104 patients with 349 EST-pairs were included. The median duration between ESTs was 343 (interquartile range, 189–378) days. Sixty (17.2%) EST-pairs were off therapy. The repeatability of the VAS was moderate {Krippendorf α, 0.56 [95% confidence interval (CI), 0.48–0.64]}, and the repeatability of the heart rate at the first PVC was substantial [intra-class correlation coefficient, 0.78 (95% CI, 0.71–0.84)]. The use of medication was associated with a higher odds for a ΔVAS > 1 (odds ratio = 3.52; 95% CI, 2.46–4.57; P = 0.020). Conclusion The repeatability of ventricular arrhythmia characteristics was moderate to substantial. This underlines the need for multiple ESTs in CPVT patients and CPVT suspicious patients and it provides the framework for assessing the therapeutic efficacy of novel CPVT therapies.

Background: The study of hypertrophic cardiomyopathy (HCM) can yield insight into the mechanisms underlying the complex trait of cardiac hypertrophy. To date, most genetic variants associated with HCM have been found in sarcomeric genes. Here, we describe a novel HCM-associated variant in the noncanonical Wnt signaling interactor WTIP (Wilms tumor interacting protein) and provide evidence of a role for WTIP in complex disease. Methods: In a family affected by HCM, we used exome sequencing and identity-by-descent analysis to identify a novel variant in WTIP (p.Y233F). We knocked down WTIP in isolated neonatal rat ventricular myocytes with lentivirally delivered short hairpin ribonucleic acids and in Danio rerio via morpholino injection. We performed weighted gene coexpression network analysis for WTIP in human cardiac tissue, as well as association analysis for WTIP variation and left ventricular hypertrophy. Finally, we generated induced pluripotent stem cell–derived cardiomyocytes from patient tissue, characterized size and calcium cycling, and determined the effect of verapamil treatment on calcium dynamics. Results: WTIP knockdown caused hypertrophy in neonatal rat ventricular myocytes and increased cardiac hypertrophy, peak calcium, and resting calcium in D rerio . Network analysis of human cardiac tissue indicated WTIP as a central coordinator of prohypertrophic networks, while common variation at the WTIP locus was associated with human left ventricular hypertrophy. Patient-derived WTIP p.Y233F-induced pluripotent stem cell–derived cardiomyocytes recapitulated cellular hypertrophy and increased resting calcium, which was ameliorated by verapamil. Conclusions: We demonstrate that a novel genetic variant found in a family with HCM disrupts binding to a known Wnt signaling protein, misregulating cardiomyocyte calcium dynamics. Further, in orthogonal model systems, we show that expression of the gene WTIP is important in complex cardiac hypertrophy phenotypes. These findings, derived from the observation of a rare Mendelian disease variant, uncover a novel disease mechanism with implications across diverse forms of cardiac hypertrophy.

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.

Abstract According to the 2020 report of the American Heart Association’s Heart & Stroke Statistics report, nearly 1,000 people are dying daily because of sudden out-of-hospital cardiac arrests and unfortunately, their survival rate is as low as 10%. Hypertrophic Cardiomyopathy (HCM), a relatively rare genetic heart disease is one of these diseases but finding the right patient for the implantation of ICD is still a research question. Implantation of cardioverter-defibrillator (ICD) can save the life of some of these patients. Due to the complexity of the identification of HCM patients, financial burdens, and the clinical risks involved in the ICD implantation procedure, HCM patients will go into a monitoring state before reaching the implantation trigger. Our study cohort shows about 82% of HCM deaths, did not have an ICD, which highlights the need to improve the pre-screening algorithms. In the current paper, we have proposed a new deep learning-based unsupervised clustering technique to facilitate the prioritization of patients to undergo ICD device implantation. This model uses over 900 echocardiographic measurements to find patients who benefit more from the ICD implantation procedure. Our model was trained and tested over 6 years of echo reports collected at Mayo Clinic. This model can be used as a decision support assistant for cardiologists in finding the right HCM patient when decision-making is hard.

Background: Symptomatic children with catecholaminergic polymorphic ventricular tachycardia (CPVT) are at risk for recurrent arrhythmic events. β-Blockers decrease this risk, but studies comparing individual β-blockers in sizeable cohorts are lacking. We aimed to assess the association between risk for arrhythmic events and type of β-blocker in a large cohort of symptomatic children with CPVT. Methods: From 2 international registries of patients with CPVT, RYR2 variant–carrying symptomatic children (defined as syncope or sudden cardiac arrest before β-blocker initiation and age at start of β-blocker therapy <18 years), treated with a β-blocker were included. Cox regression analyses with time-dependent covariates for β-blockers and potential confounders were used to assess the hazard ratio (HR). The primary outcome was the first occurrence of sudden cardiac death, sudden cardiac arrest, appropriate implantable cardioverter-defibrillator shock, or syncope. The secondary outcome was the first occurrence of any of the primary outcomes except syncope. Results: We included 329 patients (median age at diagnosis, 12 [interquartile range, 7–15] years, 35% females). Ninety-nine (30.1%) patients experienced the primary outcome and 74 (22.5%) experienced the secondary outcome during a median follow-up of 6.7 (interquartile range, 2.8–12.5) years. Two-hundred sixteen patients (66.0%) used a nonselective β-blocker (predominantly nadolol [n=140] or propranolol [n=70]) and 111 (33.7%) used a β1-selective β-blocker (predominantly atenolol [n=51], metoprolol [n=33], or bisoprolol [n=19]) as initial β-blocker. Baseline characteristics did not differ. The HRs for both the primary and secondary outcomes were higher for β1-selective compared with nonselective β-blockers (HR, 2.04 [95% CI, 1.31–3.17]; and HR, 1.99 [95% CI, 1.20–3.30], respectively). When assessed separately, the HR for the primary outcome was higher for atenolol (HR, 2.68 [95% CI, 1.44–4.99]), bisoprolol (HR, 3.24 [95% CI, 1.47–7.18]), and metoprolol (HR, 2.18 [95% CI, 1.08–4.40]) compared with nadolol, but did not differ from propranolol. The HR of the secondary outcome was only higher in atenolol compared with nadolol (HR, 2.68 [95% CI, 1.30–5.55]). Conclusions: β1-selective β-blockers were associated with a significantly higher risk for arrhythmic events in symptomatic children with CPVT compared with nonselective β-blockers, specifically nadolol. Nadolol, or propranolol if nadolol is unavailable, should be the preferred β-blocker for treating symptomatic children with CPVT.

Abstract Background Interval duration measurements (IDMs) were compared between standard 12‐lead electrocardiograms (ECGs) and 6‐lead ECGs recorded with AliveCor's KardiaMobile 6L, a hand‐held mobile device designed for use by patients at home. Methods Electrocardiograms were recorded within, on average, 15 min from 705 patients in Mayo Clinic's Windland Smith Rice Genetic Heart Rhythm Clinic. Interpretable 12‐lead and 6‐lead recordings were available for 685 out of 705 (97%) eligible patients. The most common diagnosis was congenital long QT syndrome (LQTS, 343/685 [50%]), followed by unaffected relatives and patients (146/685 [21%]), and patients with other genetic heart diseases, including hypertrophic cardiomyopathy (36 [5.2%]), arrhythmogenic cardiomyopathy (23 [3.4%]), and idiopathic ventricular fibrillation (14 [2.0%]). IDMs were performed by a central ECG laboratory using lead II with a semi‐automated technique. Results Despite differences in patient position (supine for 12‐lead ECGs and sitting for 6‐lead ECGs), mean IDMs were comparable, with mean values for the 12‐lead and 6‐lead ECGs for QTcF, heart rate, PR, and QRS differing by 2.6 ms, −5.5 beats per minute, 1.0 and 1.2 ms, respectively. Despite a modest difference in heart rate, intervals were close enough to allow a detection of clinically meaningful abnormalities. Conclusions The 6‐lead hand‐held device is potentially useful for a clinical follow‐up of remote patients, and for a safety follow‐up of patients participating in clinical trials who cannot visit the investigational site. This technology may extend the use of 12‐lead ECG recordings during the current COVID‐19 pandemic as remote patient monitoring becomes more common in virtual or hybrid‐design clinical studies.

Background We aimed to provide personalized risk estimates for cardiac events (CEs) and life‐threatening events in women with either type 1 or type 2 long QT. Methods and Results The prognostic model was derived from the Rochester Long QT Syndrome Registry, comprising 767 women with type 1 long QT (n=404) and type 2 long QT (n=363) from age 15 through 60 years. The risk prediction model included the following variables: genotype/mutation location, QTc‐specific thresholds, history of syncope, and β‐blocker therapy. A model was developed with the end point of CEs (syncope, aborted cardiac arrest, or long QT syndrome–related sudden cardiac death), and was applied with the end point of life‐threatening events (aborted cardiac arrest, sudden cardiac death, or appropriate defibrillator shocks). External validation was performed with data from the Mayo Clinic Genetic Heart Rhythm Clinic (N=467; type 1 long QT [n=286] and type 2 long QT [n=181]). The cumulative follow‐up duration among the 767 enrolled women was 22 243 patient‐years, during which 323 patients (42%) experienced ≥1 CE. Based on genotype‐phenotype data, we identified 3 risk groups with 10‐year projected rates of CEs ranging from 15%, 29%, to 51%. The corresponding 10‐year projected rates of life‐threatening events were 2%, 5%, and 14%. C statistics for the prediction model for the 2 respective end points were 0.68 (95% CI 0.65–0.71) and 0.71 (95% CI 0.66–0.76). Corresponding C statistics for the model in the external validation Mayo Clinic cohort were 0.65 (95% CI 0.60–0.70) and 0.77 (95% CI 0.70–0.84). Conclusions This is the first risk prediction model that provides absolute risk estimates for CEs and life‐threatening events in women with type 1 or type 2 long QT based on personalized genotype‐phenotype data. The projected risk estimates can be used to guide female‐specific management in long QT syndrome.

Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.