AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.

Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.

Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.

Noncoding variants and sulcal patterns in congenital heart disease: Machine learning to predict functional impact.

Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding variants (ncDNVs) with sulcal patterns in people with CHD remains poorly understood. Leveraging deep learning models, we examined the predicted impact of ncDNVs on gene regulatory signals.

Breaking barriers: fostering equitable access to pediatric genomics through innovative care models and technologies.

The integration of genomic medicine into pediatric clinical practice is a critical need that remains largely unmet, especially in socioeconomically challenged and rural areas where healthcare disparities are most pronounced. This review seeks to summarize the barriers responsible for delayed diagnosis and treatment, and examines diverse care models, technological innovations, and strategies for dissemination and implementation aimed at addressing the evolving genomic needs of pediatric populations. Through a comprehensive review of the literature, we explore proposed methodologies to bridge this gap in pediatric healthcare, with a specific emphasis on understanding and speeding implementation approaches and technologies to mitigate disparities in underserved populations, including rural and marginalized communities.

AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.

Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.

Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.

Case Ascertainment in Pediatric Heart Failure Using International Classification of Disease Clinical Modification (ICD-CM) Codes.

Most epidemiological studies in pediatric heart failure (HF) use administrative database sources, defining patient cohorts by presence of a single HF ICD code. However, the ability of ICD codes to identify true HF patients is unknown in pediatrics. Here we describe the accuracy of HF ICD-10-CM code search algorithms, in identifying pediatric patients with HF from electronic data sources.

Case Ascertainment in Pediatric Heart Failure Using International Classification of Disease Clinical Modification (ICD-CM) Codes.

Background: Most epidemiological studies in pediatric heart failure (HF) use administrative database sources, defining patient cohorts by presence of a single HF ICD code. However, the ability of ICD codes to identify true HF patients is unknown in pediatrics. Here we describe the accuracy of HF ICD-10-CM code search algorithms, in identifying pediatric patients with HF from electronic data sources.

Environmental Cues Facilitate Maturation and Patterning of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes.

Background/aims: Advances in induced pluripotent stem cell (iPSC) technology allow for reprogramming of adult somatic cells into stem cells from which patient- and disease-specific cardiomyocytes (CMs) can be derived. Yet, the potential of iPSC technology to revolutionize cardiovascular research is limited, in part, by the embryonic nature of these cells. Here, we test the hypothesis that decellularized porcine left ventricular extracellular cardiac matrix (ECM) provides environmental cues that promote transcriptional maturation and patterning of iPSC-CMs in culture.

Pediatric and Familial Genetic Arrhythmia Syndromes: Evaluation of Bidirectional Ventricular Tachycardia-Differential Diagnosis.

Bidirectional ventricular tachycardia is a unique arrhythmia that can herald lethal arrhythmia syndromes. Using cases based on real patient stories, this article examines 3 different presentations to help clinicians learn the differential diagnosis associated with this condition. Each associated genetic disorder will be briefly discussed, and valuable tips for distinguishing them from each other will be provided.

Limited Relationship Between Echocardiographic Measures and Electrocardiographic Markers of Left Ventricular Size in Healthy Children.

Pediatric ECG standards have been defined without echocardiographic confirmation of normal anatomy. The Pediatric Heart Network Normal Echocardiogram Z-score Project provides a racially diverse group of healthy children with normal echocardiograms. We hypothesized that ECG and echocardiographic measures of left ventricular (LV) dimensions are sufficiently correlated in healthy children to imply a clinically meaningful relationship.

The impact of damaging epilepsy and cardiac genetic variant burden in sudden death in the young.

Background: Sudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing.

Familial Associations of Prevalence and Cause-Specific Mortality for Thoracic Aortic Disease and Bicuspid Aortic Valve in a Large-Population Database.

Background: Thoracic aortic disease and bicuspid aortic valve (BAV) likely have a heritable component, but large population-based studies are lacking. This study characterizes familial associations of thoracic aortic disease and BAV, as well as cardiovascular and aortic-specific mortality, among relatives of these individuals in a large-population database.

Methods: In this observational case-control study of the Utah Population Database, we identified probands with a diagnosis of BAV, thoracic aortic aneurysm, or thoracic aortic dissection.

Care Recommendations for the Investigation and Management of Children With Skeletal Muscle Channelopathies.

The field of pediatric skeletal muscle channelopathies has seen major new advances in terms of a wider understanding of clinical presentations and new phenotypes. Skeletal muscle channelopathies cause significant disability and even death in some of the newly described phenotypes. Despite this, there are virtually no data on the epidemiology and longitudinal natural history of these conditions or randomized controlled trial evidence of efficacy or tolerability of any treatment in children, and thus best practice care recommendations do not exist.

Contribution of Previously Unrecognized RNA Splice-Altering Variants to Congenital Heart Disease.

Background: Known genetic causes of congenital heart disease (CHD) explain <40% of CHD cases, and interpreting the clinical significance of variants with uncertain functional impact remains challenging. We aim to improve diagnostic classification of variants in patients with CHD by assessing the impact of noncanonical splice region variants on RNA splicing.

Methods: We tested de novo variants from trio studies of 2649 CHD probands and their parents, as well as rare (allele frequency, <2×10) variants from 4472 CHD probands in the Pediatric Cardiac Genetics Consortium through a combined computational and in vitro approach.

The impact of damaging epilepsy and cardiac genetic variant burden in sudden death in the young.

Background: Sudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing.

Mitochondrial calcium uniporter stabilization preserves energetic homeostasis during Complex I impairment.

Calcium entering mitochondria potently stimulates ATP synthesis. Increases in calcium preserve energy synthesis in cardiomyopathies caused by mitochondrial dysfunction, and occur due to enhanced activity of the mitochondrial calcium uniporter channel. The signaling mechanism that mediates this compensatory increase remains unknown.

Neither cardiac mitochondrial DNA variation nor copy number contribute to congenital heart disease risk.

The well-established manifestation of mitochondrial mutations in functional cardiac disease (e.g., mitochondrial cardiomyopathy) prompted the hypothesis that mitochondrial DNA (mtDNA) sequence and/or copy number (mtDNAcn) variation contribute to cardiac defects in congenital heart disease (CHD).

An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records.

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications.