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.