Importance of genetic testing in unexplained cardiac arrest.

AIMS: Genetic testing is recommended in specific inherited heart diseases but its role remains unclear and it is not currently recommended in unexplained cardiac arrest (UCA). We sought to assess the yield and clinical utility of genetic testing in UCA using whole-exome sequencing (WES). METHODS AND RESULTS: Survivors of UCA requiring external defibrillation were included from the Cardiac Arrest Survivor with Preserved Ejection fraction Registry. Whole-exome sequencing was performed, followed by assessment of rare variants in previously reported cardiovascular disease genes. A total of 228 UCA survivors (mean age at arrest 39 ± 13 years) were included. The majority were males (66%) and of European ancestry (81%). Following advanced clinical testing at baseline, the likely aetiology of cardiac arrest was determined in 21/228 (9%) cases. Whole-exome sequencing identified a pathogenic or likely pathogenic (P/LP) variant in 23/228 (10%) of UCA survivors overall, increasing the proportion of 'explained' cases from 9% only following phenotyping to 18% when combining phenotyping with WES. Notably, 13 (57%) of the 23 P/LP variants identified were located in genes associated with cardiomyopathy, in the absence of a diagnosis of cardiomyopathy at the time of arrest. CONCLUSIONS: Genetic testing identifies a disease-causing variant in 10% of apparent UCA survivors. The majority of disease-causing variants was located in cardiomyopathy-associated genes, highlighting the arrhythmogenic potential of such variants in the absence of an overt cardiomyopathy diagnosis. The present study supports the use of genetic testing including assessment of arrhythmia and cardiomyopathy genes in survivors of UCA.

Missense variants in the spectrin repeat domain of DSP are associated with arrhythmogenic cardiomyopathy: A family report and systematic review.

Rare loss of function variants in DSP, which codes for the desmosomal protein desmoplakin, have been implicated in dilated and arrhythmogenic right ventricular cardiomyopathies. We present a family with arrhythmogenic cardiomyopathy associated with a novel missense variant in DSP (NM_004415.4): c.877G>A, p.(Glu293Lys). The phenotype is characterized by predominant involvement of the left ventricle with systolic dysfunction, fibrosis, and life-threatening arrhythmias. We performed a systematic review of literature collecting all cardiomyopathy cases with rare missense variants in DSP. We demonstrate that the distribution of missense variants across the protein domains in cardiomyopathy cases differs from that in gnomAD (p = .04), with a case enrichment of rare missense variants in the spectrin repeat domain (36/78 [46%] in cases vs. 449/1495 [30%] in gnomAD; p = .004). Our findings highlight the predominance of cardiac arrhythmia and left ventricular involvement in desmoplakin cardiomyopathy and pinpoint to a potential mutation hotspot in DSP thereby facilitating missense variant interpretation in the diagnostic setting.

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

Importance: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG). Objective: To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG. Design, Setting, and Participants: This diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals. Exposures: Convolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results. Main Outcomes and Measures: The main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection. Results: A total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN's high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval-based detection (AUC, 0.74; 95% CI, 0.69-0.78). Conclusions and Relevance: The deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

Diagnostic and Therapeutic Misconception: Parental Expectations and Perspectives Regarding Genetic Testing for Developmental Disorders.

Parents' understanding/expectations regarding genetic testing for children with developmental disorders were explored. Within a month of testing, interviews were conducted with 57 parents. Many (74%) could not recall the nature of testing. Parents expected genetic testing to have positive impacts for the child (93%) and the family (98%), mainly to find the etiology and/or an intervention. Many parents (40%) reported not knowing their child's clinical diagnosis. They expected genetic testing would establish the diagnosis. Parents anticipated potential negative impacts of testing for children (78%) and families (87%), mainly finding another illness or not finding potential interventions. Abnormal results explaining the disorder were found in 9% of children. In summary, genetic results for developmental disorders are unlikely to meet parental expectations.