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Background: Congenital heart disease (CHD) is the most common congenital anomaly. Almost 90% of isolated cases have an unexplained genetic etiology after clinical testing. Non-canonical splice variants that disrupt mRNA splicing through the loss or creation of exon boundaries are not routinely captured and/or evaluated by standard clinical genetic tests. Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms., Methods: We used genome sequencing (GS) (n = 1101 CHD probands) and myocardial RNA-Sequencing (RNA-Seq) (n = 154 CHD and n = 43 cardiomyopathy probands) to identify and validate splice disrupting variants, and to develop a heart-specific model for canonical and non-canonical splice variants that can be applied to patients with CHD and cardiomyopathy. Two thousand five hundred seventy GS samples from the Medical Genome Reference Bank were analyzed as healthy controls., Results: Of 8583 rare DNA splice-disrupting variants initially identified using SpliceAI, 100 were associated with altered splice junctions in the corresponding patient myocardium affecting 95 genes. Using strength of myocardial gene expression and genome-wide DNA variant features that were confirmed to affect splicing in myocardial RNA, we trained a machine learning model for predicting cardiac-specific splice-disrupting variants (AUC 0.86 on internal validation). In a validation set of 48 CHD probands, the cardiac-specific model outperformed a SpliceAI model alone (AUC 0.94 vs 0.67 respectively). Application of this model to an additional 947 CHD probands with only GS data identified 1% patients with canonical and 11% patients with non-canonical splice-disrupting variants in CHD genes. Forty-nine percent of predicted splice-disrupting variants were intronic and > 10 bp from existing splice junctions. The burden of high-confidence splice-disrupting variants in CHD genes was 1.28-fold higher in CHD cases compared with healthy controls., Conclusions: A new cardiac-specific in silico model was developed using complementary GS and RNA-Seq data that improved genetic yield by identifying a significant burden of non-canonical splice variants associated with CHD that would not be detectable through panel or exome sequencing., (© 2024. The Author(s).)

Background: The agreement between the commonly used ambulatory blood pressure (ABP) thresholds to diagnose ambulatory hypertension in children (patient's 24-h mean ABP classified by 24-h 95th ABP percentile threshold, American Heart Association [AHA] threshold, or patient's day and night mean ABP classified by day-night 95th ABP percentile thresholds) is not known. We evaluated the agreement among 24-h ABP threshold, AHA threshold, and day-night ABP thresholds to diagnose ambulatory hypertension, white coat hypertension (WCH) and masked hypertension (MH)., Methods: In a cross-sectional study design, we analyzed ABP recordings from 450 participants with suspected hypertension from a tertiary care outpatient hypertension clinic. The American Academy of Pediatrics thresholds were used to diagnose office hypertension., Results: The 24-h ABP threshold and day-night ABP thresholds classified 19% ABP (95% confidence interval [CI], 0.15-0.23) differently into ambulatory normotension/hypertension (kappa [κ], 0.58; 95% CI, 0.51-0.66). Ambulatory hypertension diagnosed by 24-h ABP threshold in 27% participants (95% CI, 0.22-0.32) was significantly lower than that by day-night ABP thresholds in 44% participants (95% CI, 0.37-0.50; P < 0.001). The AHA threshold had a stronger agreement with 24-h ABP threshold than with day-night ABP thresholds for classifying ABP into ambulatory normotension/hypertension (k 0.94, 95% CI 0.91-0.98 vs. k 0.59, 95% CI 0.52-0.66). The diagnosis of ambulatory hypertension by the AHA threshold (26%; 95% CI, 0.21-0.31) was closer to that by 24-h ABP threshold (27%, P = 0.73) than by day-night ABP thresholds (44%, P < 0.001). Similar agreement pattern persisted among these ABP thresholds for diagnosing WCH and MH., Conclusions: The 24-h ABP threshold classifies a lower proportion of ABP as ambulatory hypertension than day-night ABP thresholds. The AHA threshold exhibits a stronger agreement with 24-h ABP than with day-night ABP thresholds for diagnosing ambulatory hypertension, WCH and MH. Our findings are relevant for a consistent interpretation of hypertension by these ABP thresholds in clinical practice., (© 2022. The Author(s).)

Background: Assess process, uptake, validity and resource needs for return of actionable research findings to biobank participants., Methods: Participants were prospectively enrolled in a multicenter biorepository of childhood onset heart disease. Clinically actionable research findings were reviewed by a Return of Research Results Committee (RRR) and returned to the physician or disclosed directly to the participant through a research genetic counselor. Action taken following receipt of this information was reviewed., Results: Genetic data was generated in 1963 of 7408 participants. Fifty-nine new findings were presented to the RRR committee; 20 (34%) were deemed reportable. Twelve were returned to the physician, of which 7 were disclosed to participants (median time to disclosure, 192 days). Seven findings were returned to the research genetic counselor; all have been disclosed (median time to disclosure, 19 days). Twelve families (86%) opted for referral to clinical genetics after disclosure of findings; 7 results have been validated, 5 results are pending. Average cost of return and disclosure per reportable finding incurred by the research program was $750 when utilizing a research genetic counselor; clinical costs associated with return were not included., Conclusions: Return of actionable research findings was faster if disclosed directly to the participant by a research genetic counselor. There was a high acceptability amongst participants for receiving the findings, for referral to clinical genetics, and for clinical validation of research findings, with all referred cases being clinically confirmed.

Background: Both obesity and congenital heart disease (CHD) are risk factors for the long-term cardiovascular health of children and adolescents. The addition of smart mobile technology to conventional lifestyle counseling for weight management offers great potential to appeal to technologically literate youth and can address a large geographical area with minimal burden to participants. This pilot study seeks to examine the influence of a 1-year lifestyle intervention on nutrition and physical activity-related health outcomes in overweight or obese children and adolescents with CHD., Methods: This is a pilot and feasibility study which utilizes a single-arm, prospective design with a goal to recruit 40 overweight and obese patients. The feasibility metrics will evaluate the integrity of the study protocol, data collection and questionnaires, recruitment and consent, and acceptability of the intervention protocol and primary outcome measures. The primary clinical outcome metrics are anthropometry, body composition, and cardiorespiratory exercise capacity. The secondary clinical metrics include quality of life, nutrition and physical activity behavior, lung and muscle function, and cardio-metabolic risk factors. Outcomes are assessed at baseline, 6 months, and 1 year. To date, a total of 36 children and youth (11 girls), aged 7-17 years (mean = 14.4 years), have commenced the intervention. Recruitment for the study was initiated in June 2012 and is currently ongoing., Discussion: The information provided in this paper is intended to help researchers and health professionals with the development and evaluation of similar lifestyle intervention programs. Since the application of smartphones to pediatric cardiac health and obesity management is a novel approach, and continued research in this area is warranted, this paper may serve as a foundation for further exploration of this health frontier and inform the development of a broader strategy for obesity management in pediatric cardiology., Trial Registration: This pilot study was retrospectively registered at the www.ClinicalTrials.gov registry as NCT02980393 in November 2016, with the study commencing in May 2012. Study protocol version 15OCT2014.