Arrhythmogenic Cardiomyopathy: Evolving Diagnostic Criteria and Insight from Cardiac Magnetic Resonance Imaging.

Arrhythmogenic cardiomyopathy (ACM) is an umbrella term encompassing a wide variety of overlapping hereditary and nonhereditary disorders that can result in malignant ventricular arrhythmias and sudden cardiac death. Cardiac MRI plays a critical role in accurate diagnosis of various ACM entities and is increasingly showing promise in risk stratification that can further guide management particularly in decisions regarding use of implantable cardioverter defibrillator. Genotyping plays an important role in cascade testing but challenges remain due to incomplete penetrance and wide phenotypic variability of ACM as well as the presence of gene-elusive cases.

Association of Chest Pain Protocol-Discordant Discharge With Outcomes Among Emergency Department Patients With Modest Elevations of High-Sensitivity Troponin.

Importance: Accelerated diagnostic protocols (ADPs) for chest pain using high-sensitivity troponin (hsTn) levels have excellent sensitivity and negative predictive value for rapid risk stratification of patients with chest pain. However, little is known about the outcomes of patients who are discharged despite abnormal ADP results, ie, after "ruling-in" with a modest elevation of hsTn. Objective: To determine outcomes of patients discharged following ADP, including those who were ruled in with modestly elevated levels of hsTnT but discharged nonetheless. Design, Setting, and Participants: This retrospective cohort study included patients with chest pain who presented to the emergency departments (EDs) of a large multisite health system ED between January 2017 to September 2019. Patients were assessed using an ADP, had a peak hsTnT level measured between the limit of quantitation and 52 ng/L, were discharged, and had follow-up in the electronic medical record. Data analysis was conducted from January 2017 to September 2019. Exposures: Application of an hsTnT ADP. Main Outcomes and Measures: Thirty-day major adverse cardiac events (MACE), including myocardial infarction, urgent coronary revascularization, and all-cause death, comparing patients who were discharged following ADP-concordant vs ADP-discordant results. Results: Of 10 342 patients with chest pain (mean [SD] age 51 [17] years; 5902 [57%] women) discharged following ADP, 29 (0.28%) had MACE. Patients with MACE were older (median [IQR] age, 66 [53-75] years vs 50 [38-62] years; P < .001) and more likely to have prior CAD (12 [41.4%] vs 1805 [17.5%]; P = .002) and hyperlipidemia (13 [44.8%] vs 2248 [21.8%]; P = .006). Additionally, patients with MACE were 5-fold more likely to have been discharged despite ADP discordance (16 [55.2%] vs 1145 [11.1%]; P < .001). A multivariable logistic regression analysis revealed only ADP discordance was independently associated with MACE (odds ratio, 6.42 [95% CI, 2.94-14.0]; P < .001). When stratified by peak hsTnT level, there were no differences in MACE between ADP-concordant and -discordant discharges provided the peak hsTnT measured was less than 12 ng/L. In contrast, patients with peak hsTnT level between 12 and 51 ng/L were significantly more likely to have MACE if they were discharged after ADP-discordant vs -concordant hsTnT series (14 of 609 [2.30%] vs 5 of 1047 [0.48%]; P < .002). Notably, a HEART (history, electrocardiogram, age, risk factors, troponin) score of 4 or greater retrospectively identified the most ADP-discordant discharges (13 of 16 [81.3%]) who had MACE. Conclusions and Relevance: In this cohort study, an hsTnT ADP identified patients who could be discharged from the ED with low 30-day risk of MACE, provided the discharge was based on ADP-concordant "rule-out." Conversely, the rate of MACE was significantly higher among patients discharged despite ADP discordance. Most patients with ADP-discordant discharges who experienced MACE had a HEART score of 4 or greater, suggesting that application of this score may augment discharge decisions of patients despite ADP-discordant troponin series.

Loss-of-Function FLNC Variants Are Associated With Arrhythmogenic Cardiomyopathy Phenotypes When Identified Through Exome Sequencing of a General Clinical Population.

BACKGROUND: The FLNC gene has recently garnered attention as a likely cause of arrhythmogenic cardiomyopathy, which is considered an actionable genetic condition. However, the association with disease in an unselected clinical population is unknown. We hypothesized that individuals with loss-of-function variants in FLNC (FLNCLOF) would have increased odds for arrhythmogenic cardiomyopathy-associated phenotypes versus variant-negative controls in the Geisinger MyCode cohort. METHODS: We identified rare, putative FLNCLOF among 171 948 individuals with exome sequencing linked to health records. Associations with arrhythmogenic cardiomyopathy phenotypes from available diagnoses and cardiac evaluations were investigated. RESULTS: Sixty individuals (0.03%; median age 58 years [47-70 interquartile range], 43% male) harbored 27 unique FLNCLOF. These individuals had significantly increased odds ratios for dilated cardiomyopathy (odds ratio, 4.9 [95% CI, 2.6-7.6]; P<0.001), supraventricular tachycardia (odds ratio, 3.2 [95% CI, 1.1-5.6]; P=0.048), and left-dominant arrhythmogenic cardiomyopathy (odds ratio, 4.2 [95% CI, 1.4-7.9]; P=0.03). Echocardiography revealed reduced left ventricular ejection fraction (52±13% versus 57±9%; P=0.001) associated with FLNCLOF. Overall, at least 9% of FLNCLOF patients demonstrated evidence of penetrant disease. CONCLUSIONS: FLNCLOF variants are associated with increased odds of ventricular arrhythmia and dysfunction in an unselected clinical population. These findings support genomic screening of FLNC for actionable secondary findings.

Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical dataset.

Use of machine learning (ML) for automated annotation of heart structures from echocardiographic videos is an active research area, but understanding of comparative, generalizable performance among models is lacking. This study aimed to (1) assess the generalizability of five state-of-the-art ML-based echocardiography segmentation models within a large Geisinger clinical dataset, and (2) test the hypothesis that a quality control (QC) method based on segmentation uncertainty can further improve segmentation results. Five models were applied to 47,431 echocardiography studies that were independent from any training samples. Chamber volume and mass from model segmentations were compared to clinically-reported values. The median absolute errors (MAE) in left ventricular (LV) volumes and ejection fraction exhibited by all five models were comparable to reported inter-observer errors (IOE). MAE for left atrial volume and LV mass were similarly favorable to respective IOE for models trained for those tasks. A single model consistently exhibited the lowest MAE in all five clinically-reported measures. We leveraged the tenfold cross-validation training scheme of this best-performing model to quantify segmentation uncertainty. We observed that removing segmentations with high uncertainty from 14 to 71% studies reduced volume/mass MAE by 6-10%. The addition of convexity filters improved specificity, efficiently removing < 10% studies with large MAE (16-40%). In conclusion, five previously published echocardiography segmentation models generalized to a large, independent clinical dataset-segmenting one or multiple cardiac structures with overall accuracy comparable to manual analyses-with variable performance. Convexity-reinforced uncertainty QC efficiently improved segmentation performance and may further facilitate the translation of such models.

Prevalence and Predictors of Obstructive Coronary Artery Disease in Nonlow-risk Acute Chest Pain Patients Who Rule Out for Myocardial Infarction in the High-sensitivity Troponin Era.

OBJECTIVES: The best management approach for chest pain patients who rule out for myocardial infarction (MI) in the high-sensitivity troponin (hsTn) era remains elusive. Patients, especially those with nonlow clinical risk scores, are often referred for inpatient ischemic testing to uncover obstructive coronary artery disease (CAD). Whether the prevalence of obstructive CAD in this cohort is high enough to justify routine testing is not known. METHODS: We conducted a retrospective cohort analysis of 1517 emergency department chest pain patients who ruled out for MI by virtue of a stable high-sensitivity troponin T (hsTnT) levels (defined as <5 ng/L intermeasurements increase) and were admitted for inpatient testing. RESULTS: Abnormal ischemia evaluation (including 5.9% with evidence of fixed wall motion or perfusion defects) was 11.9%. Of those undergoing invasive angiography (n = 292), significant coronary stenoses (≥70% or unstable lesions) and multivessel CAD occurred in 16.8% and 5.5%, respectively. In a multivariate logistic regression model, known CAD, prior MI, chest pain character, mildly elevated hsTnT, and left ventricular ejection fraction <40% were predictive of an abnormal ischemia evaluation result, whereas electrocardiography findings and the modified History, EKG, Age, Risk factors, and troponin (HEART) score were not. Of note, 30-day adverse cardiac events were strikingly low at 0.4% with no deaths despite an overwhelming majority (>90%) of patients scoring intermediate or high on the modified HEART score. CONCLUSIONS: A considerable percentage of acute chest pain patients who rule out for MI by hsTn had evidence of obstructive CAD, and the modified HEART score was not predictive of an abnormal ischemia evaluation.

Clinical Findings and Diagnostic Yield of Arrhythmogenic Cardiomyopathy Through Genomic Screening of Pathogenic or Likely Pathogenic Desmosome Gene Variants.

BACKGROUND: Genomic screening holds great promise for presymptomatic identification of hidden disease, and prevention of dramatic events, including sudden cardiac death associated with arrhythmogenic cardiomyopathy (ACM). Herein, we present findings from clinical follow-up of carriers of ACM-associated pathogenic/likely pathogenic desmosome variants ascertained through genomic screening. METHODS: Of 64 548 eligible participants in Geisinger MyCode Genomic Screening and Counseling program (2015-present), 92 individuals (0.14%) identified with pathogenic/likely pathogenic desmosome variants by clinical laboratory testing were referred for evaluation. We reviewed preresult medical history, patient-reported family history, and diagnostic testing results to assess both arrhythmogenic right ventricular cardiomyopathy and left-dominant ACM. RESULTS: One carrier had a prior diagnosis of dilated cardiomyopathy with arrhythmia; no other related diagnoses or diagnostic family history criteria were reported. Fifty-nine carriers (64%) had diagnostic testing in follow-up. Excluding the variant, 21/59 carriers satisfied at least one arrhythmogenic right ventricular cardiomyopathy task force criterion, 11 (52%) of whom harbored DSP variants, but only 5 exhibited multiple criteria. Six (10%) carriers demonstrated evidence of left-dominant ACM, including high rates of atypical late gadolinium enhancement by magnetic resonance imaging and nonsustained ventricular tachycardia. Two individuals received new cardiomyopathy diagnoses and received defibrillators for primary prevention. CONCLUSIONS: Genomic screening for pathogenic/likely pathogenic variants in desmosome genes can uncover both left- and right-dominant ACM. Findings of overt cardiomyopathy were limited but were most common in DSP-variant carriers and notably absent in PKP2-variant carriers. Consideration of the pathogenic/likely pathogenic variant as a major criterion for diagnosis is inappropriate in the setting of genomic screening.

Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality.

Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here we show that a convolutional neural network trained on raw pixel data in 812,278 echocardiographic videos from 34,362 individuals provides superior predictions of one-year all-cause mortality. The model's predictions outperformed the widely used pooled cohort equations, the Seattle Heart Failure score (measured in an independent dataset of 2,404 patients with heart failure who underwent 3,384 echocardiograms), and a machine learning model involving 58 human-derived variables from echocardiograms and 100 clinical variables derived from electronic health records. We also show that cardiologists assisted by the model substantially improved the sensitivity of their predictions of one-year all-cause mortality by 13% while maintaining prediction specificity. Large unstructured datasets may enable deep learning to improve a wide range of clinical prediction models.

Acquired aortopulmonary fistula: a case report.

BACKGROUND: Aortopulmonary fistula is a rare complication of ascending aorta pathology. Presentation is most commonly dramatic with acute onset chest pain or heart failure secondary to left to right shunting. We describe a patient with acquired aortopulmonary fistula who had an insidious onset of heart failure as his presenting complaint. We also highlight the utility of multimodality cardiac imaging in establishing the diagnosis. CASE SUMMARY: A 79-year-old male patient with a history of coronary artery bypass graft surgery and mechanical aortic valve replacement, 23 years prior, presented with exertional dyspnoea of 7 months duration. An initial workup that included transthoracic and transoesophageal echocardiography as well as coronary and bypass graft angiography failed to diagnose an acquired aortopulmonary fistula complicating an ascending aortic pseudoaneurysm. Upon referral to our institution, the correct diagnosis was suspected on off-axis transthoracic echocardiography. The fistula was subsequently confirmed, and the extent of ascending aorta pathology defined via a multimodality imaging approach that consisted of transoesophageal echocardiography and cardiac computed tomography. The patient underwent successful surgical repair and was discharged in a stable condition. DISCUSSION: Acquired aortopulmonary fistula is a rare clinical entity. We describe a patient who had an insidious presentation of heart failure and found to have a large ascending aortic aneurysm that eroded into the main pulmonary artery creating a fistulous communication. The diagnosis was delayed and required a high index of suspicion and multimodality cardiac imaging.

A comparative 30-day outcome analysis of inpatient evaluation vs outpatient testing in patients presenting with chest pain in the high-sensitivity troponin era. A propensity score matched case-control retrospective study.

BACKGROUND: The best disposition of chest pain patients who rule out for myocardial infarction (MI) but have non-low clinical risk scores in the high-sensitivity troponin era is not well studied. HYPOTHESIS: In carefully selected patients who rule out for MI, and have a high-sensitivity troponin T ≤ 50 ng/L with an absolute increase less than 5 ng/L on repeat measurements, early emergency room (ER) discharge might be equivalent to inpatient evaluation in regards to 30-day incidence of adverse cardiac events (ACEs) regardless of the clinical risk score. METHODS: A total of 12 847 chest pain patients presenting to our health system ERs from January 2017 to September 2019 were retrospectively investigated. A propensity score matching algorithm was used to account for baseline differences between admitted and discharged cohorts. We then estimated and compared the incidence of 30-day and 1-year composite ACEs (MI, urgent revascularization, or cardiovascular death) between both groups. A multivariate Cox regression model was used to evaluate the effect of admission on outcomes. RESULTS: A total of 2060 patients were matched in 1:1 fashion. The primary endpoint of 30-day composite ACEs occurred in 0.6% and 0.4% of the admission and the discharged cohorts, respectively (P = .76). One-year composite ACEs was also similar between both groups (4% vs 3.7%, P = .75). In a multivariate Cox regression model, the effect of inpatient evaluation was neutral (hazard ratio 1.1, confidence interval 0.62-1.9, P = .75). CONCLUSIONS: Inpatient evaluation was not associated with better outcomes in our selected group of patients. Larger-scale randomized trials are needed to confirm our findings.

Comparative outcome analysis of stable mildly elevated high sensitivity troponin T in patients presenting with chest pain. A single-center retrospective cohort study.

BACKGROUND: The ideal high-sensitivity troponin (hsTn) cutoff for identifying those at low risk of 30 days events is debated; however, the 99th percentile overall or gender-specific upper reference limit (URL) is most commonly used. The magnitude of risk and the best management strategy for those with low-level hsTn elevation hasn't been extensively studied. METHODS: We conducted a retrospective cohort analysis including 4396 chest pain patients (542 with low-level hsTn elevation) who ruled out for myocardial infarction (MI), had a stable high-sensitivity troponin T (hsTnT) levels (defined as < 5 ng/l inter-measurements increase in hsTnT levels), and were discharged from the emergency department without further ischemic testing. The aim of the study was to compare the 30-day incidence of adverse cardiac events (ACE) between patients with undetectable high-sensitivity troponin T (hsTnT) (group 1), patients with hsTnT within the 99th percentile sex-specific URL (group 2), and patients with low-level hsTnT elevation (between the 99th percentile URL and ≤ 50 ng/l) (group 3). RESULTS: 30-day event rates were very low 0.1%, 0.6%, and 0.4% for hsTnT groups 1, 2, and 3 respectively (overall P = 0.041, for groups 2 & 3 interaction P = 0.74). 30-day all-cause mortality, as well as 1-year all-cause and cardiovascular mortalities, occurred more frequently in those with low-level hsTnT elevation as did 1-year composite ACE. CONCLUSION: In conclusion, 30-day adverse event rates were very low in those with stable low-level hsTnT elevation who ruled out for MI and were discharged from the emergency department without further inpatient testing.

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart1. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage-time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (n = 45,285) with ECGs interpreted as 'normal' by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (P < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians.

Prevalence and Electronic Health Record-Based Phenotype of Loss-of-Function Genetic Variants in Arrhythmogenic Right Ventricular Cardiomyopathy-Associated Genes.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is associated with variants in desmosome genes. Secondary findings of pathogenic/likely pathogenic variants, primarily loss-of-function (LOF) variants, are recommended for clinical reporting; however, their prevalence and associated phenotype in a general clinical population are not fully characterized. METHODS: From whole-exome sequencing of 61 019 individuals in the DiscovEHR cohort, we screened for putative loss-of-function variants in PKP2, DSC2, DSG2, and DSP. We evaluated measures from prior clinical ECG and echocardiograms, manually over-read to evaluate ARVC diagnostic criteria, and performed a PheWAS (phenome-wide association study). Finally, we estimated expected penetrance using Bayesian inference. RESULTS: One hundred forty individuals (0.23%; 59±18 years old at last encounter; 33% male) had an ARVC variant (G+). None had an existing diagnosis of ARVC in the electronic health record, nor significant differences in prior ECG or echocardiogram findings compared with matched controls without variants. Several G+ individuals satisfied major repolarization (n=4) and ventricular function (n=5) criteria, but this prevalence matched controls. PheWAS showed no significant associations of other heart disease diagnoses. Combining our best genetic and disease prevalence estimates yields an estimated penetrance of 6.0%. CONCLUSIONS: The prevalence of ARVC loss-of-function variants is ≈1:435 in a general clinical population of predominantly European descent, but with limited electronic health record-based evidence of phenotypic association in our population, consistent with a low penetrance estimate. Prospective deep phenotyping and longitudinal follow-up of a large sequenced cohort is needed to determine the true clinical relevance of an incidentally identified ARVC loss-of-function variant.