Chahal, Choudhary Anwar A

Abstract Cardiac disease affects the heart non-uniformly. Examples include focal septal or apical hypertrophy with reduced strain in hypertrophic cardiomyopathy, replacement fibrosis with akinesia in an infarct-related coronary artery territory, and a pattern of scarring in dilated cardiomyopathy. The detail and versatility of cardiovascular magnetic resonance (CMR) imaging mean it contains a wealth of information imperceptible to the naked eye and not captured by standard global measures. CMR-derived heterogeneity biomarkers could facilitate early diagnosis, better risk stratification, and a more comprehensive prediction of treatment response. Small cohort and case–control studies demonstrate the feasibility of proof-of-concept structural and functional heterogeneity measures. Detailed radiomic analyses of different CMR sequences using open-source software delineate unique voxel patterns as hallmarks of histopathological changes. Meanwhile, measures of dispersion applied to emerging CMR strain sequences describe variable longitudinal, circumferential, and radial function across the myocardium. Two of the most promising heterogeneity measures are the mean absolute deviation of regional standard deviations on native T1 and T2 and the standard deviation of time to maximum regional radial wall motion, termed the tissue synchronization index in a 16-segment left ventricle model. Real-world limitations include the non-standardization of CMR imaging protocols across different centres and the testing of large numbers of radiomic features in small, inadequately powered patient samples. We, therefore, propose a three-step roadmap to benchmark novel heterogeneity biomarkers, including defining normal reference ranges, statistical modelling against diagnosis and outcomes in large epidemiological studies, and finally, comprehensive internal and external validations.

BACKGROUND: With genetic testing advancements, the burden of incidentally identified cardiac disease-associated gene variants is rising. These variants may carry a risk of sudden cardiac death, highlighting the need for accurate diagnostic interpretation. We sought to identify pathogenic hotspots in sudden cardiac death-associated genes using amino acid-level signal-to-noise (S:N) analysis and develop a web-based precision medicine tool, DiscoVari , to improve variant evaluation. METHODS: The minor allele frequency of putatively pathogenic variants was derived from cohort-based cardiomyopathy and channelopathy studies in the literature. We normalized disease-associated minor allele frequencies to rare variants in an ostensibly healthy population (Genome Aggregation Database) to calculate amino acid-level S:N. Amino acids with S:N above the gene-specific threshold were defined as hotspots. DiscoVari was built using JavaScript ES6 and using open-source JavaScript library ReactJS, web development framework Next.js, and JavaScript runtime NodeJS. We validated the ability of DiscoVari to identify pathogenic variants using variants from ClinVar and individuals clinically evaluated at the Duke University Hospitals with cardiac genetic testing. RESULTS: We developed DiscoVari as an internet-based tool for S:N-based variant hotspots. Upon validation, a higher proportion of ClinVar likely pathogenic/pathogenic variants localized to DiscoVari hotspots (43.1%) than likely benign/benign variants (17.8%; P< 0.0001). Further, 75.3% of ClinVar variants reclassified to likely pathogenic/pathogenic were in hotspots, compared with 41.3% of those reclassified as variants of uncertain significance ( P< 0.0001) and 23.4% of those reclassified as likely benign/benign ( P <0.0001). Of the clinical cohort variants, 73.1% of likely pathogenic/pathogenic were in hotspots, compared with 0.0% of likely benign/benign ( P< 0.01). CONCLUSIONS: DiscoVari reliably identifies disease-susceptible amino acid residues to evaluate variants by searching amino acid-specific S:N ratios.

Abstract Aims Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification. Methods and results We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P &lt; 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models. Conclusion A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.

AbstractIntroductionThe use of intracardiac echocardiography (ICE) is beneficial during the ablation of atrial fibrillation (AF). Evidence is conflicting regarding the clinical impact of using ICE on arrhythmia recurrence and mortality.MethodsPatients undergoing catheter ablation of AF during 2010–2017 were identified using the International Classification of Diseases‐9th and 10th Revision‐Clinical Modification (ICD‐9‐CM and ICD‐10‐CM) from the Nationwide Readmissions Database. Propensity matching was used to generate a control group. Patient demographics, Charlson comorbidity indexes, time from discharge to readmission, and the reason of readmission were extracted.ResultsFrom 2010 to 2017, 51 129 patients were included in the analysis out of which ICE was used in 8005 (15.7%) patients. The in‐hospital mortality at readmission was significantly higher in the patients without ICE use (2.9% vs. 1.7%, p = .02). The length of stay (LOS) at readmission was significantly higher in non‐ICE arm (median [interquartile range, IQR]: 3 [2–6] vs. 2 [3–5] days, p < .0001) with similar healthcare‐associated cost (HAC) in both the groups (median [IQR]: US$7507.3 [4057.8–15 474.2] vs. 7339.4 [4024.8–15 191.6], p = .43). Freedom from readmission was 12% higher (hazard ratio [HR] [95% confidence interval, CI]: 0.88 [0.83–0.94], p < .0001) with the use of ICE at 90‐day follow‐up, which was driven by 24% reduction in heart failure (HF) at follow‐up (HR [95% CI]: 0.76 [0.60–0.96], p = .02).ConclusionsICE use during AF ablation procedure reduces readmissions at 90 days by 12%, driven by a 24% decrease in HF‐related admissions. The non‐ICE arm showed a significantly higher LOS which offsets marginally higher HAC in the ICE arm.

Background Marfan syndrome (MFS) is an autosomal dominant connective tissue disorder affecting multiple systems, particularly the cardiovascular system. The leading causes of death in MFS are aortopathies and valvular disease. We wanted to identify the frequency of arrhythmia and postural orthostatic tachycardia syndrome, length of hospital stay, health care–associated costs (HAC), and in‐hospital mortality in patients with MFS. Methods and Results The National Inpatient Sample database from 2005 to 2014 was queried using International Classification of Diseases, Ninth Revision, Clinical Modification ( ICD‐9‐CM ) codes for MFS and arrhythmias. Patients were classified into subgroups: supraventricular tachycardia, ventricular tachycardia (VT), atrial fibrillation, atrial flutter, and without any type of arrhythmia. Data about length of stay, HAC, and in‐hospital mortality were also abstracted from National Inpatient Sample database. Adjusted HAC was calculated as multiplying HAC and cost‐to‐charge ratio; 12 079 MFS hospitalizations were identified; 1893 patients (15.7%) had an arrhythmia; and 4.9% of the patients had postural orthostatic tachycardia syndrome. Median values of length of stay and adjusted HAC in VT group were the highest among the groups (VT: 6 days, $18 975.8; supraventricular tachycardia: 4 days, $11 906.6; atrial flutter: 4 days, $11 274.5; atrial fibrillation: 5 days, $10431.4; without any type of arrhythmia: 4 days, $8336.6; both P =0.0001). VT group had highest in‐patient mortality (VT: 5.3%, atrial fibrillation: 4.1%, without any type of arrhythmia: 2.1%, atrial flutter: 1.7%, supraventricular tachycardia: 0%; P <0.0001) even after adjustment for potential confounders (without any type of arrhythmia versus VT; odds ratio [95% CI]: 3.18 [1.62–6.24], P =0.001). Conclusions Arrhythmias and postural orthostatic tachycardia syndrome in MFS were high and associated with increased length of stay, HAC, and in‐hospital mortality especially in patients with VT.

Abstract Recent technological advances have facilitated and diversified the options available for the diagnosis of cardiac arrhythmias. Ranging from simple resting or exercise electrocardiograms to more sophisticated and expensive smartphones and implantable cardiac monitors. These tests and devices may be used for varying periods of time depending on symptom frequency. The choice of the most appropriate heart rhythm test should be guided by clinical evaluation and optimized following accurate characterization of underlying symptoms, ‘red flags’, risk factors, and consideration of cost-effectiveness of the different tests. This review provides evidence-based guidance for assessing suspected arrhythmia in patients who present with symptoms or in the context of screening, such as atrial fibrillation or advanced conduction disturbances following transcatheter aortic valve implantation in high-risk groups. This is intended to help clinicians choose the most appropriate diagnostic tool to facilitate the management of patients with suspected arrhythmias.

Background Sarcoidosis is an inflammatory, noncaseating, granulomatous disorder of unknown cause that can affect any body system and is associated with cardiovascular disease including sudden cardiac death (SCD). Cardiac involvement in sarcoidosis is associated with higher risk of SCD, but the SCD risk in the general sarcoidosis population is unknown. We aimed to determine the risk of SCD in people with sarcoidosis versus the matched general population. Methods and Results A population‐based cohort of sarcoidosis and age‐ and sex‐matched comparators from January 1, 1976 to December 31, 2013 was used; presence of other comorbidities in the comparator group was not an exclusion criterion. Mortality, including time, place, and cause of death were measured and manually adjudicated for SCD events. Incidence rates are reported per 100 000 person‐years, and Cox models were used for group comparisons. Of the 345 incident cases of sarcoidosis (171 men; 50%) there were 58 reported deaths; 10 were definite/probable SCD versus 57 all‐cause and 9 SCDs in comparators. Median follow‐up was 12.9 years (interquartile range, 6.0–23.4 years) . Incidence rate of SCD in sarcoidosis was 192 (95% CI, 92–352) versus 155 (95% CI, 71–294) in comparators (hazard ratio [HR], 1.28 (95% CI, 0.52–3.17). Nocturnal deaths were more frequent in sarcoidosis 57 (95% CI, 12–168) versus 17 (95% CI, 0.4–95) (HR, 3.76 [95% CI, 0.39–36.47]). No significant differences were detected between the groups by sex, age, calendar year of diagnosis, or disease duration. Conclusions In a population‐based cohort of patients with sarcoidosis, the risk for SCD compared with matched comparators was not increased. There were more nocturnal deaths among patients with sarcoidosis, yet this was statistically insignificant.

Background: There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features. Methods: UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative. Results: Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes. Conclusions: In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.