The ventricular arrhythmia Torsades de Pointes (TdP) is a common form of drug-induced cardiotoxicity, but prediction of this arrhythmia remains an unresolved issue in drug development. Current assays to evaluate arrhythmia risk are limited by poor specificity and a lack of mechanistic insight. We addressed this important unresolved issue through a novel computational approach that combined simulations of drug effects on dynamics with statistical analysis and machine-learning. Drugs that blocked multiple ion channels were simulated in ventricular myocyte models, and metrics computed from the action potential and intracellular (Ca(2+) ) waveform were used to construct classifiers that distinguished between arrhythmogenic and nonarrhythmogenic drugs. We found that: (1) these classifiers provide superior risk prediction; (2) drug-induced changes to both the action potential and intracellular (Ca(2+) ) influence risk; and (3) cardiac ion channels not typically assessed may significantly affect risk. Our algorithm demonstrates the value of systematic simulations in predicting pharmacological toxicity.

An unsupervised assessment of echocardiographic variables for assessing LV DD revealed unique patterns of grouping. These natural patterns of clustering may better identify patient groups who have similar risk, and their incorporation into clinical practice may help eliminate indeterminate results and improve clinical outcome prediction.

There is increasing evidence regarding the prevalence of genetic cardiomyopathies, for which arrhythmias may be the first presentation. Ventricular and atrial arrhythmias presenting in the absence of known myocardial disease are often labelled as idiopathic, or lone. While ventricular arrhythmias are well-recognized as presentation for arrhythmogenic cardiomyopathy in the right ventricle, the scope of arrhythmogenic cardiomyopathy has broadened to include those with dominant left ventricular involvement, usually with a phenotype of dilated cardiomyopathy. In addition, careful evaluation for genetic cardiomyopathy is also warranted for patients presenting with frequent premature ventricular contractions, conduction system disease, and early onset atrial fibrillation, in which most detected genes are in the cardiomyopathy panels. Sudden death can occur early in the course of these genetic cardiomyopathies, for which risk is not adequately tracked by left ventricular ejection fraction. Only a few of the cardiomyopathy genotypes implicated in early sudden death are recognized in current indications for implantable cardioverter defibrillators which otherwise rely upon a left ventricular ejection fraction ≤0.35 in dilated cardiomyopathy. The genetic diagnoses impact other aspects of clinical management such as exercise prescription and pharmacological therapy of arrhythmias, and new therapies are coming into clinical investigation for specific genetic cardiomyopathies. The expansion of available genetic information and implications raises new challenges for genetic counseling, particularly with the family member who has no evidence of a cardiomyopathy phenotype and may face a potentially negative impact of a genetic diagnosis. Discussions of risk for both probands and relatives need to be tailored to their numeric literacy during shared decision-making. For patients presenting with arrhythmias or cardiomyopathy, extension of genetic testing and its implications will enable cascade screening, intervention to change the trajectory for specific genotype-phenotype profiles, and enable further development and evaluation of emerging targeted therapies.

Introduction BNP elevation in patients with AF is observed in the absence of heart failure; however, prior mechanistic studies have not included direct left atrial pressure measurements. This study sought to understand how emptying function of the left atrial appendage (LAA) and LAA dimension contributes to brain‐natriuretic peptide elevations (BNP) in atrial fibrillation (AF) accounting for left atrial pressure (LAP). Methods 132 patients referredfor left atrial appendage occlusion (LAAO) were prospectively enrolled in this study. BNP levels and LAP were measured just before LAAO. Statistical analysis considered BNP, rhythm at time of procedure, LAP, LAA morphology, LAA size (ostial diameter, depth, volume), LAA emptying velocity, CHADS2‐VASc score, body mass index (BMI), left ventricular ejection fraction (LVEF), estimated glomerular filtration rate (eGFR), and obstructive sleep apnea (OSA) diagnosis as covariates. Results Bivariate statistical analysis demonstrated positive associations with age, LAA ostial diameter, depth, and volume, LAP, AF status at time of measurement, OSA, and CHADS2‐VASc score. BNP was negatively associated with LVEF, eGFR, LAA emptying velocity and BMI. With multivariate logistic regression including LAP as covariate, significant relationships between BNP and AF/AFL(OR 1.99 [1.03, 3.85]), LAP (OR 1.13 [1.06, 1.20]), LAA diameter (OR 1.14 [1.03, 1.27]), LAA depth (OR 1.14 [1.07, 1.22]), and LAA emptying velocity (OR 0.97 [0.96,0.99]) were observed; however, no significant associations were seen with LAA morphology or CHADS2‐VASc score. Conclusions BNP elevations in AF are associated with LAA size and function, but not CHADS2‐VASc score or appendage morphology after accounting for changes in LAP.

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