Publications

BACKGROUND: Congenital heart disease (CHD) affects about 1% of births and is linked to differences in thinking and learning. Understanding how birth, genetic, clinical, and environmental factors together explain cognitive variability can inform monitoring and care. This study builds a multivariate model predicting cognition across multiple domains in adolescents and young adults with CHD.

METHODS: We studied 89 adolescents and young adults (AYAs; mean age 16 years) with CHD who completed structural and diffusion MRI and fifteen neurocognitive tests across seven domains. Using an enhanced forward-inclusion and backward-elimination strategy with cross-validation, we built multivariate models incorporating biological, socioeconomic, clinical, genetic, and brain imaging features. Performance was evaluated using Pearson correlation (r) between observed and inferred scores, mean absolute error (MAE), and inverse inferability score (IIS).

RESULTS: Here we show that models infer scores with moderate accuracy (r = 0.245-0.648; MAE = 1.6-12.0 points; mean MAE = 6.3). Highest correlations include Digit Span (r = 0.65; p < 0.001), Verbal Comprehension Index (r = 0.594; p < 0.001), and Matrix Reasoning (r = 0.574; p < 0.001). Domain ranking by IIS shows the best (lowest) scores for general intelligence (0.0886), followed by working memory (0.7100), and a higher (worse) score for perceptual reasoning (1.9199).

CONCLUSIONS: A multivariate approach combining brain imaging with genetic, clinical, and environmental factors provides clinically meaningful inference of individual cognitive performance in AYAs with CHD. These findings suggest complementary roles of brain, genetic, and contextual factors in shaping cognitive variability and motivate validation in larger cohorts.

With improvement in medical and surgical care, the number of adults with congenital heart disease (CHD) is soaring. Adults with CHD commonly have impairments in brain health. However, significant gaps in knowledge remain regarding the relevant types and prevalence of neurologic and psychiatric risk and their associated risk factors. We sought to review current evidence, identify gaps in knowledge, and develop key next steps to improve scientific understanding and clinical care. Three working groups-Genetics and Brain Health, Characterizing Neuropsychological and Psychological Outcomes, and Neuropsychological and Psychosocial Interventions-were composed of multidisciplinary experts in relevant clinical and research domains, as well as adults with CHD. Each group identified 5 key knowledge gaps and associated next investigations needed to address those gaps. For Genetics and Brain Health, 5 key knowledge gaps were identified: lack of a standardized neuroimaging protocol for adults with CHD, need to understand neuroradiological-pathological-neuropsychological correlates, role of gene-environment interactions, what can be learned from brain health risk models from other groups, and how existing multimodal approaches influence risk and neuroresilience. Adults with CHD can benefit from routine assessment of brain health, as well as increased clinical and basic research into the underlying factors that contribute to risk and neuroresilience for neurologic and psychiatric sequalae. Multidisciplinary collaborative efforts that incorporate adults with CHD across the research cycle are essential for all key next steps.

OBJECTIVE: Optimize use of rapid genomic sequencing (rGS) in a level IV NICU.

STUDY DESIGN: We designed interventions to improve patient identification, ordering processes, and provider education for rGS in our level IV NICU. We measured the percentage of infants eligible for rGS by internal criteria who had rGS sent, diagnostic yield of rGS (balancing measure), and days from genetics consult to rGS result (balancing measure).

RESULT: Our study included 560 infants undergoing genetics evaluation. The percentage of eligible infants who had rGS sent significantly increased from 37% pre-intervention (January 2019-March 2021) to 54% post-intervention (April 2021-September 2024) (p < 0.001). Diagnostic yield of rGS remained stable (32% vs 34%). Time from genetics consult to rGS result significantly decreased from median 32 to 27 days (p = 0.04).

CONCLUSION: Our quality improvement initiative increased rGS use with stable diagnostic yield and decreased time to rGS result for critically ill infants with suspected genetic disorders.

Congenital heart disease (CHD) is the most common major birth defect, affecting nearly 1% of live-born infants. There is a high prevalence of CHD among premature neonates, with prematurity and low birth weight compounding the risks associated with CHD and leading to increased morbidity and mortality. Despite advances in diagnosis, surgery, and intensive care, outcomes for preterm infants with CHD remain guarded, particularly in the earliest gestational age groups. These infants face heightened risks of neonatal decompensation, cardiac arrest, and early mortality, but also long-term complications including neurodevelopmental impairment. The interplay between maternal-fetal factors, perinatal environment, and the complex physiology of both prematurity and CHD underscores the need for multidisciplinary care. Prenatal diagnosis, careful delivery planning, specialized postnatal management, and tailored surgical timing are critical to optimizing outcomes. Neonatal and cardiac intensivists, cardiologists, surgeons, anesthesiologists, and allied professionals must collaborate closely to address diverse challenges including hemodynamic instability, respiratory support, nutrition, neuroprotection, and social disparities. This review synthesizes current evidence on the epidemiology, pathophysiology, and management of neonates with CHD with a focus on prematurity. We highlight evolving models of interdisciplinary care and outline priorities for research. A physiology-based, team-oriented approach is essential to improve both survival and long-term quality of life for this vulnerable population. What is Known: • CHD is the most common birth defect and a leading cause of neonatal morbidity and mortality. • Prematurity and low birthweight worsen outcomes, with complications and surgical risk inversely related to gestational age. What is New: • Pregnancies with CHD carry up to a threefold higher risk of preterm delivery. • Outcomes reflect maternal-fetal and neonatal factors, highlighting the need for tailored timing, evaluation, and surgical strategies, with a key role for multidisciplinary care.

BACKGROUND: Neural tube defects such as spina bifida (SB) are congenital anomalies associated with significant morbidity and mortality worldwide. Environmental factors, particularly folate, modify SB risk. Based on recurrence rates of SB within families, genetic risk also contributes to SB development. However, the effect of maternal folate intake on genetic risk for SB in Bangladesh has not been quantified.

METHODS: Genetic variants were imputed from array data of 112 infants with SB and 116 infants without SB. After quality filtering, genome-wide association was performed on 91 infants with SB and 97 without. Maternal folate intake and maternal nail arsenic concentration were included as covariates and interaction terms (SNP × Folate, SNP × Arsenic) along with maternal age, infant sex, and 10 principal components as covariates.

RESULTS: Two loci had variants nominally associated with SB: one within the coding region of WWOX, including rs7184417 (odds ratio [OR] = 6.20, p = 2.22E-06), and a second in the coding region of ISOC2 (rs4801638; OR = 0.24, p = 5.75E-06). With the gene-folate interaction, a locus in CNTN5 was associated with SB. After including the gene-arsenic interaction, the gene-folate interaction effect was nominally associated with a locus in CTNNA2.

CONCLUSIONS: Inclusion of maternal folate intake as a covariate and interaction term identified three genomic loci that could impact the risk for SB. A fourth locus was identified when maternal arsenic level was included. These nominal associations should be assessed in additional cohorts with larger sample sizes. Novel genes impacted by these loci may interact with previously reported genes for SB.

Umbilical arterial catheters (UAC) in neonates are used for blood pressure monitoring, blood sampling, administration of fluids, nutrition, and medications. As UAC applications evolve, enteral nutrition practices vary in neonates in the presence of a UAC. The theoretical concern for mesenteric ischemia when a UAC is in place led to early nil per os approaches, delaying the initiation of enteral nutrition. More contemporary practices have favored introducing enteral feeding in neonates with UACs. However, there remains a paucity of data to guide clinical practice approaches regarding enteral feeding in neonates with a UAC in place. In this perspective article, we examine the physiological effects of UACs and review existing literature on feeding practices in neonates with a UAC. We offer an approach to managing enteral feeding in neonates with a UAC, addressing the central question: Is routine feeding in neonates with a UAC in place justified in current clinical practice?

Haploinsufficiency of the RNA splicing regulator, RBFOX2 , is linked to congenital heart disease (CHD), yet its pathogenic mechanisms remain unclear. Here, we demonstrate that RBFOX2 is essential for progressing cardiomyocyte (CM) differentiation by shifting exon usage profiles to more mature patterns in sarcomere, cytoskeletal, and focal adhesion genes, including alpha-actinin-2 ( ACTN2 ). This maturation program is initiated by critical levels of RBFOX2 that facilitate autoregulatory splicing at mutually exclusive exons encoding early and late isoforms with distinct functional roles. In heterozygous CMs, autoregulation is disrupted, which skews isoform ratios and generates a dominant-negative product caused by exon co-inclusion. Finally, we demonstrate that overexpression of ACTN2 rescues heterozygous, but not null, phenotypes by restoring contractility, which triggers a mechanosensing feedback loop involving upregulation of RBFOX2 from the wildtype allele and transcriptome maturation. Our data suggest that decreased RBFOX2 dosage and autoregulation impair CM differentiation, contributing to CHD pathogenesis and heart failure susceptibility.

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Crossview Local-and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-of-the-art infection detection techniques.

BACKGROUND: Congenital heart disease (CHD) is an important cause of childhood mortality as well as morbidity in children and adults. While genetic risk contributes to the majority of CHD, most individuals with CHD do not have an identified genetic diagnosis. Short tandem repeat (TR) elements are composed of repeated base pair motifs for 2-6 basepairs that are highly polymorphic in length between individuals. These regions had been difficult to study with short read sequencing, and they have not been studied at a large scale in the context of CHD. New software and sequencing platforms have allowed for more accurate TR element genotyping. Therefore, we aimed to identify TR element variants that could impact the expression of known CHD genes.

RESULTS: We identified de novo and inherited TR element variants near known CHD genes in participants with CHD (n = 1,899) in the Pediatric Cardiac Genomics Consortium cohort as well as unaffected participants (n = 1,932) from the Simons Foundation Autism Research Initiative using short-read sequencing followed by variant calling with the gangSTR pipeline. Comparison with long-read sequencing confirmed proband genotypes for 75% (91/120) of the TR element variants identified using short read sequencing. 114 TR element regions had 3 or more de novo TR element variants, compared to an expectation of 74 TR element regions (1.54-fold enrichment, p < 1.5E-5). CHD genes CACNA1C and EVC2 had the strongest enrichment of TR element variants in the CHD cohort, determined by a higher frequency of nearby de novo TR length variants in the CHD cohort compared to the non-CHD cohort. Within CHD trios, there was over-transmission of a TR element variant near Tab 2.

CONCLUSIONS: In a targeted analysis of de novo and transmitted TR element variants in a large cohort of CHD probands, each individual had   1 de novo TR element variant near a CHD gene, and participants with CHD demonstrate clustering of variants within TR element regions. Long-read sequencing confirmed the majority of TR element variants identified using the gangSTR pipeline. De novo variants in known CHD genes were enriched in participants with CHD, with specific enrichment in TR elements near CACNA1C, EVC2, and Tab 2 in the CHD cohort. Many individual TR element variants were in known regulatory regions, but further work is needed to determine their functional impact.

While exome and whole genome sequencing have transformed medicine by elucidating the genetic underpinnings of both rare and common complex disorders, its utility to predict clinical outcomes remains understudied. Here, we use artificial intelligence (AI) technologies to explore the predictive value of whole exome sequencing in forecasting clinical outcomes following surgery for congenital heart defects (CHD). We report results for a prospective observational cohort study of 2,253 CHD patients from the Pediatric Cardiac Genomics Consortium with a broad range of complex heart defects, pre- and post-operative clinical variables and exome sequencing. Damaging genotypes in chromatin-modifying and cilia-related genes are associated with an elevated risk of adverse post-operative outcomes, including mortality, cardiac arrest and prolonged mechanical ventilation. The impact of damaging genotypes is further amplified in the context of specific CHD phenotypes, surgical complexity and extra-cardiac anomalies. The absence of a damaging genotype in chromatin-modifying and cilia-related genes is also informative, reducing the risk for some adverse postoperative outcomes. Thus, genome sequencing enriches the ability to forecast outcomes following congenital cardiac surgery.