Quantifying lifetime risk for 1,401 infectious diseases across the diabetes spectrum using a Bayesian approach.

Although many diabetes complications have been extensively studied, less is known about the burden of infectious diseases. We developed a Bayesian approach to compare infection risk across 9,476 patients with type 1 diabetes (T1D), 74,270 with type 2 diabetes (T2D), and 32,095 with prediabetes. Patients with T1D, T2D, and prediabetes had multifold increased risk for all organ system- and pathogen-based composite infection outcomes.

Investigating the frequency of unreported cases and laboratory correspondence practices for variants of uncertain significance in pediatric epilepsy.

Genetic testing via multigene panels has become common practice for children with epilepsy. This results in frequent identification of variants of uncertain significance (VUS). Providers must then assess clinical suspicion of the VUS, considering clinical correlation, family variant testing, and laboratory contact for internal case data.

Enrichment of tandem repeat element variants near CHD genes identified by short- and long-read genome sequencing.

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.

Genome sequencing is critical for forecasting outcomes following congenital cardiac surgery.

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.

MPSE identifies newborns for whole genome sequencing within 48 h of NICU admission.

Identifying critically ill newborns who will benefit from whole genome sequencing (WGS) is difficult and time-consuming due to complex eligibility criteria and evolving clinical features. The Mendelian Phenotype Search Engine (MPSE) automates the prioritization of neonatal intensive care unit (NICU) patients for WGS. Using clinical data from 2885 NICU patients, we evaluated the utility of different machine learning (ML) classifiers, clinical natural language processing (CNLP) tools, and types of Electronic Health Record (EHR) data to identify sick newborns with genetic diseases.

Explainable artificial intelligence for stroke risk stratification in atrial fibrillation.

Current risk stratification tools can limit the optimal implementation of new and emerging therapies for patients with heart rhythm disorders. For example, stroke prevention treatments have outpaced means for stroke risk stratification for patients with atrial fibrillation (AF). Artificial intelligence (AI) techniques have shown promise for improving various tasks in cardiovascular medicine.

Genetic and training adaptations in the Haenyeo divers of Jeju, Korea.

Natural selection and relative isolation have shaped the genetics and physiology of unique human populations from Greenland to Tibet. Another such population is the Haenyeo, the all-female Korean divers renowned for their remarkable diving abilities in frigid waters. Apnea diving induces considerable physiological strain, particularly in females diving throughout pregnancy.

Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes.

Congenital heart disease (CHD) is a leading cause of infant mortality. We analyzed de novo mutations (DNMs) and very rare transmitted/unphased damaging variants in 248 prespecified genes in 11,555 CHD probands. The results identified 60 genes with a significant burden of heterozygous damaging variants.

Graph-based prototype inverse-projection for identifying cortical sulcal pattern abnormalities in congenital heart disease.

Examining the altered arrangement and patterning of sulcal folds offers insights into the mechanisms of neurodevelopmental differences in psychiatric and neurological disorders. Previous sulcal pattern analysis used spectral graph matching of sulcal pit-based graph structures to assess deviations from normative sulcal patterns. However, challenges exist, including the absence of a standard criterion for defining a typical reference set, time-consuming cost of graph matching, user-defined feature weight sets, and assumptions about uniform node distribution.

The Utah NeoSeq Project: a collaborative multidisciplinary program to facilitate genomic diagnostics in the neonatal intensive care unit.

Rapid genomic diagnostics in the Neonatal Intensive Care Unit represents a paradigm shift in medicine with increasing evidence of the utility of early diagnosis, impacting management. The goal of the Utah NeoSeq Project was to implement and evaluate a multidisciplinary and longitudinal rapid sequencing program while transitioning to CLIA-certified sequencing. Enrollment of 65 infants resulted in 26 (40%) with a diagnostic variant(s) and 7 (11%) harboring a strong candidate.

Recessive genetic contribution to congenital heart disease in 5,424 probands.

Variants with large effect contribute to congenital heart disease (CHD). To date, recessive genotypes (RGs) have commonly been implicated through anecdotal ascertainment of consanguineous families and candidate gene-based analysis; the recessive contribution to the broad range of CHD phenotypes has been limited. We analyzed whole exome sequences of 5,424 CHD probands.

Modeling Mutations in Induced Pluripotent Stem Cells Provides Insights Into Cardiovascular Disease Pathogenesis.

Background: SMAD2 is a coregulator that binds a variety of transcription factors in human development. Heterozygous loss-of-function and missense variants are identified in patients with congenital heart disease (CHD) or arterial aneurysms. Mechanisms that cause distinct cardiovascular phenotypes remain unknown.

Deciphering the digenic architecture of congenital heart disease using trio exome sequencing data.

Congenital heart disease (CHD) is the most common congenital anomaly and a leading cause of infant morbidity and mortality. Despite extensive exploration of the monogenic causes of CHD over the last decades, ∼55% of cases still lack a molecular diagnosis. Investigating digenic interactions, the simplest form of oligogenic interactions, using high-throughput sequencing data can elucidate additional genetic factors contributing to the disease.

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.