Risk Factors and Outcomes Associated with Gaps in Care in Children with Congenital Heart Disease

Adults with congenital heart disease (CHD) benefit from cardiology follow-up at recommended intervals of ≤ 2 years. However, benefit for children is less clear given limited studies and unclear current guidelines. We hypothesize there are identifiable risks for gaps in cardiology follow-up in children with CHD and that gaps in follow-up are associated with differences in healthcare utilization. Our cohort included children < 10 years old with CHD and a healthcare encounter from 2008 to 2013 at one of four North Carolina (NC) hospitals. We assessed associations between cardiology follow-up and demographics, lesion severity, healthcare access, and educational isolation (EI). We compared healthcare utilization based on follow-up. Overall, 60.4% of 6,969 children received cardiology follow-up within 2 years of initial encounter, including 53.1%, 58.1%, and 79.0% of those with valve, shunt, and severe lesions, respectively. Factors associated with gaps in care included increased drive time to a cardiology clinic (Hazard Ratio (HR) 0.92/15-min increase), EI (HR 0.94/0.2-unit increase), lesion severity (HR 0.48 for shunt/valve vs severe), and older age (HR 0.95/month if < 1 year old and 0.94/year if > 1 year old; p < 0.05). Children with a care gap subsequently had more emergency department (ED) visits (Rate Ratio (RR) 1.59) and fewer inpatient encounters and procedures (RR 0.51, 0.35; p < 0.05). We found novel factors associated with gaps in care for cardiology follow-up in children with CHD and altered health care utilization with a gap. Our findings demonstrate a need to mitigate healthcare barriers and generate clear cardiology follow-up guidelines for children with CHD.

Underrepresentation of Diverse Ancestries Drives Uncertainty in Genetic Variants Found in Cardiomyopathy-Associated Genes

BACKGROUND

Thousands of genetic variants have been identified in cardiomyopathy-associated genes. Diagnostic genetic testing is key for evaluation of individuals with suspected cardiomyopathy. While accurate variant pathogenicity assignment is important for diagnosis, the frequency of and factors associated with clinically relevant assessment changes are unclear.

OBJECTIVES

The authors aimed to characterize pathogenicity assignment change in cardiomyopathy-associated genes and to identify factors associated with this change.

METHODS

We identified 10 sarcomeric and 6 desmosomal genetic cardiomyopathy-associated genes along with comparison gene sets. We analyzed clinically meaningful changes in pathogenicity assignment between any of the following: pathogenic/likely pathogenic (P/LP), conflicting interpretations of pathogenicity or variant of unknown significance (C/VUS), and benign/likely benign. We explored association of minor allele frequency (MAF) differences between well, and traditionally poorly, represented ancestries in genetic studies with assessment stability. Analyses were performed using ClinVar and GnomAD data.

RESULTS

Of the 30,975 cardiomyopathy-associated gene variants in ClinVar, 2,276 of them (7.3%) had a clinically meaningful change in pathogenicity assignment over the study period, 2011 to 2021. Sixty-seven percent of variants that underwent a clinically significant change moved from P/LP or benign/likely benign to C/VUS. Among cardiomyopathy variants downgraded from P/LP, 35% had a MAF above 1 × 10 -4 in non-Europeans and below 1 × 10 -4 in Europeans.

CONCLUSIONS

Over the past 10 years, 7.3% of cardiomyopathy gene variants underwent a clinically meaningful change in pathogenicity assignment. Over 30% of downgrades from P/LP may be attributable to higher MAF in Non-Europeans than Europeans. This finding suggests that low ancestral diversity in genetic studies has increased diagnostic uncertainty in cardiomyopathy gene variants.

DiscoVari: A Web-Based Precision Medicine Tool for Predicting Variant Pathogenicity in Cardiomyopathy- and Channelopathy-Associated Genes

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.

PRDM16 Deletion Is Associated With Sex-dependent Cardiomyopathy and Cardiac Mortality: A Translational, Multi-Institutional Cohort Study

BACKGROUND

1p36 deletion syndrome can predispose to pediatric-onset cardiomyopathy. Deletion breakpoints are variable and may delete the transcription factor PRDM16. Early studies suggest that deletion of PRDM16 may underlie cardiomyopathy in patients with 1p36 deletion; however, the prognostic impact of PRDM16 loss is unknown.

METHODS

This retrospective cohort included subjects with 1p36 deletion syndrome from 4 hospitals. Prevalence of cardiomyopathy and freedom from death, cardiac transplantation, or ventricular assist device were analyzed. A systematic review cohort was derived for further analysis. A cardiac-specific Prdm16 knockout mouse (Prdm16 conditional knockout) was generated. Echocardiography was performed at 4 and 6 to 7 months. Histology staining and qPCR were performed at 7 months to assess fibrosis.

RESULTS

The retrospective cohort included 71 patients. Among individuals with PRDM16 deleted, 34.5% developed cardiomyopathy versus 7.7% of individuals with PRDM16 not deleted (P=0.1). In the combined retrospective and systematic review cohort (n=134), PRDM16 deletion-associated cardiomyopathy risk was recapitulated and significant (29.1% versus 10.8%, P=0.03). PRDM16 deletion was associated with increased risk of death, cardiac transplant, or ventricular assist device (P=0.04). Among those PRDM16 deleted, 34.5% of females developed cardiomyopathy versus 16.7% of their male counterparts (P=0.2). We find sex-specific differences in the incidence and the severity of contractile dysfunction and fibrosis in female Prdm16 conditional knockout mice. Further, female Prdm16 conditional knockout mice demonstrate significantly elevated risk of mortality (P=0.0003).

CONCLUSIONS

PRDM16 deletion is associated with a significantly increased risk of cardiomyopathy and cardiac mortality. Prdm16 conditional knockout mice develop cardiomyopathy in a sex-biased way. Patients with PRDM16 deletion should be assessed for cardiac disease.

Pathogenicity Assignment of Variants in Genes Associated With Cardiac Channelopathies Evolve Toward Diagnostic Uncertainty

BACKGROUND

Accurately determining variant pathogenicity is critical in the diagnosis of cardiac channelopathies; however, it remains unknown how variant pathogenicity status changes over time. Our aim is to use a comprehensive analysis of ClinVar to understand the mutability of variant evaluation in channelopathy-associated genes to inform clinical decision-making around variant calling.

METHODS

We identified 10 genes (RYR2, CASQ2, KCNQ1, KCNH2, SCN5A, CACNA1C, CALM1, CALM2, CALM3, TRDN) strongly associated with cardiac channelopathies, as well as 3 comparison gene sets (disputed long QT syndrome, sudden unexpected death in epilepsy, and all ClinVar). We comprehensively analyzed variant pathogenicity calls over time using the ClinVar database with Rstudio. Analyses focused on the frequency and directionality of clinically meaningful changes in disease association, defined as a change from one of the following three categories to another: likely benign/benign, conflicting evidence of pathogenicity/variant of uncertain significance, and likely pathogenic/pathogenic.

RESULTS

In total, among channelopathy-associated genes, there were 9975 variants in ClinVar and 8.4% had a clinically meaningful change in disease association at least once over the past 10 years, as opposed to 4.9% of all ClinVar variants. The 3 channelopathy-associated genes with the most variants undergoing a clinically significant change were KCNQ1 (20.9%), SCN5A (11.2%), and KCNH2 (10.1%). Ten of the 12 included genes had variant evaluations that trended toward diagnostic uncertainty over time. Specifically, channelopathy-associated gene variants with either pathogenic/likely pathogenic or benign/likely benign assignments were 5.6× and 2×, respectively, as likely to be reevaluated to conflicting/variant of uncertain significance compared to the converse.

CONCLUSIONS

Over the past 10 years, 8.4% of variants in channelopathy-associated genes have changed pathogenicity status with a decline in overall diagnostic certainty. Ongoing clinical and genetic variant follow-up is needed to account for presence of clinically meaningful change in variant pathogenicity assignment over time.

GENESIS: Gene-Specific Machine Learning Models for Variants of Uncertain Significance Found in Catecholaminergic Polymorphic Ventricular Tachycardia and Long QT Syndrome-Associated Genes

BACKGROUND

Cardiac channelopathies such as catecholaminergic polymorphic tachycardia and long QT syndrome predispose patients to fatal arrhythmias and sudden cardiac death. As genetic testing has become common in clinical practice, variants of uncertain significance (VUS) in genes associated with catecholaminergic polymorphic ventricular tachycardia and long QT syndrome are frequently found. The objective of this study was to predict pathogenicity of catecholaminergic polymorphic ventricular tachycardia-associated RYR2 VUS and long QT syndrome-associated VUS in KCNQ1, KCNH2, and SCN5A by developing gene-specific machine learning models and assessing them using cross-validation, cellular electrophysiological data, and clinical correlation.

METHODS

The GENe-specific EnSemble grId Search framework was developed to identify high-performing machine learning models for RYR2, KCNQ1, KCNH2, and SCN5A using variant- and protein-specific inputs. Final models were applied to datasets of VUS identified from ClinVar and exome sequencing. Whole cell patch clamp and clinical correlation of selected VUS was performed.

RESULTS

The GENe-specific EnSemble grId Search models outperformed alternative methods, with area under the receiver operating characteristics up to 0.87, average precisions up to 0.83, and calibration slopes as close to 1.0 (perfect) as 1.04. Blinded voltage-clamp analysis of HEK293T cells expressing 2 predicted pathogenic variants in KCNQ1 each revealed an ≈80% reduction of peak Kv7.1 current compared with WT. Normal Kv7.1 function was observed in KCNQ1-V241I HEK cells as predicted. Though predicted benign, loss of Kv7.1 function was observed for KCNQ1-V106D HEK cells. Clinical correlation of 9/10 variants supported model predictions.

CONCLUSIONS

Gene-specific machine learning models may have a role in post-genetic testing diagnostic analyses by providing high performance prediction of variant pathogenicity.

ATP1A3-Encoded Sodium-Potassium ATPase Subunit Alpha 3 D801N Variant Is Associated With Shortened QT Interval and Predisposition to Ventricular Fibrillation Preceded by Bradycardia

Background

Pathogenic variation in the ATP1A3‐encoded sodium‐potassium ATPase, ATP1A3, is responsible for alternating hemiplegia of childhood (AHC). Although these patients experience a high rate of sudden unexpected death in epilepsy, the pathophysiologic basis for this risk remains unknown. The objective was to determine the role of ATP1A3 genetic variants on cardiac outcomes as determined by QT and corrected QT (QTc) measurements.

Methods and Results

We analyzed 12‐lead ECG recordings from 62 patients (male subjects=31, female subjects=31) referred for AHC evaluation. Patients were grouped according to AHC presentation (typical versus atypical), ATP1A3 variant status (positive versus negative), and ATP1A3 variant (D801N versus other variants). Manual remeasurements of QT intervals and QTc calculations were performed by 2 pediatric electrophysiologists. QTc measurements were significantly shorter in patients with positive ATP1A3 variant status (P<0.001) than in patients with genotype‐negative status, and significantly shorter in patients with the ATP1A3‐D801N variant than patients with other variants (P<0.001). The mean QTc for ATP1A3‐D801N was 344.9 milliseconds, which varied little with age, and remained <370 milliseconds throughout adulthood. ATP1A3 genotype status was significantly associated with shortened QTc by multivariant regression analysis. Two patients with the ATP1A3‐D801N variant experienced ventricular fibrillation, resulting in death in 1 patient. Rare variants in ATP1A3 were identified in a large cohort of genotype‐negative patients referred for arrhythmia and sudden unexplained death.

Conclusions

Patients with AHC who carry the ATP1A3‐D801N variant have significantly shorter QTc intervals and an increased likelihood of experiencing bradycardia associated with life‐threatening arrhythmias. ATP1A3 variants may represent an independent cause of sudden unexplained death. Patients with AHC should be evaluated to identify risk of sudden death.

Risk Factors for Sudden Infant Death in North Carolina

Background: Sudden infant death syndrome (SIDS) is the sudden, unexplained death of infants <1 year old. SIDS remains a leading cause of death in US infants. We aim to identify associations between SIDS and race/ethnicity, birth weight/gestational age, and socioeconomic/environmental factors in North Carolina (NC) to help identify infants at risk for SIDS. Methods and Results: In this IRB-approved study, infant mortality 2007-2016 and death certificate-linked natality 2007-2014 were obtained from the NC Department of Health and Human Services. General, NC natality statistics 2007-2016 were obtained from CDC Wonder. Association between SIDS/total infant death and covariates (below) were calculated. Total infant mortality decreased 2007-2016 by an average of 14 deaths/100,000 live births per year, while SIDS incidence remained constant. Risk ratios of SIDS/total infant deaths, standardized to Non-Hispanic White, were 1.76/2.41 for Non-Hispanic Black and 0.49/0.97 for Hispanic infants. Increased SIDS risk was significantly and independently associated with male infant sex, Non-Hispanic Black maternal race/ethnicity, young maternal age, low prenatal care, gestational age <39 weeks, birthweight <2500 g, low maternal education, and maternal tobacco use (p < 0.01). Maternal previous children now deceased also trended toward association with increased SIDS risk. Conclusions: A thorough SIDS risk assessment should include maternal, socioeconomic, and environmental risk factors as these are associated with SIDS in our population.