Single nucleus transcriptome of a "Super RV" shows increased insulin and angiogenesis signaling.

The right ventricle (RV) is one of the four pumping chambers of the heart, pumping blood to the lungs. In severe forms of congenital heart disease and pulmonary hypertension, the RV is made to pump into the systemic circulation. Such systemic RVs typically display early failure due to pressure overload. In rare cases a systemic RV persists into later decades of life - colloquially called a 'Super RV'. Here we present the single-nucleus transcriptome of a systemic RV from a 60-year-old with congenitally corrected transposition of great arteries (ccTGA). Our data shows two specific signaling pathways enriched in the ccTGA RV myocardium. First, we show increased insulin like growth factor (IGF1) signaling within the systemic RV myocardium: there is increased expression of the main receptor IGFR1 within the cardiomyocytes, and IGF1 ligands within the cardiofibroblasts and macrophages. Second, we find increased VEGF and Wnt9 ligand expression in cardiomyocytes and increased VEGF1R and Wnt9 receptors in endothelial cells, which are implicated in angiogenesis. We show that increased insulin and angiogenesis signaling are potentially beneficial RV adaptations to increased pressure overload. This study of an adult systemic RV provides an important framework for understanding RV remodeling to systemic pressures in congenital heart disease and pulmonary hypertension.

Comparison of helicopter and ground transportation in pediatric trauma patients.

BACKGROUND: Decision making regarding transportation mode after a traumatic injury may have a significant impact on outcomes, due to differences in time to definitive care. The objective of this study was to determine if transport mode had an impact on in-hospital mortality and discharge disposition in pediatric trauma patients. METHODS: Data were abstracted from the National Trauma Data Bank from 2007 to 2016 comparing helicopter and ground transportation modes effects on mortality and discharge outcomes. The primary outcome was in-hospital death, while the secondary outcome was discharge home without services (DCHWOS). Analyses included logistic regression modeling and propensity score matching. RESULTS: Significant variables from univariate analysis were included in the multivariate, propensity-matched regression model. Pediatric trauma patients transported by helicopter had lower odds of mortality (OR 0.69 [0.64,0.75]) and higher odds of DCHWOS (1.29 [1.20,1.39]). There were no differences in overall mechanism, but individual injury patterns showed higher odds of mortality. CONCLUSION: Critical decisions regarding triage of patients by different modes of transport occur every day. This study supports the current literature on the topic and shows a potential additional benefit of a meaningful discharge outcome for those transported by helicopter. IMPACT: This study may impact prehospital triage decision making process for pediatric trauma patients on mortality. Prehospital transport mode may contribute to pediatric trauma discharge outcomes. Highlights the need for future research regarding non-clinical data that is unable to be abstracted from national databases (e.g., family dynamics, insurance status, weather, access to post-discharge resources).

Physiologic profile associated with severe multisystem inflammatory syndrome in children: a retrospective study.

OBJECTIVE: The purpose of this study was to describe the clinical presentation and physiologic profile of individuals with varying degrees of severity of multisystem inflammatory syndrome in children (MIS-C). METHODS: We performed a retrospective study of children diagnosed with MIS-C admitted to a single quaternary children's hospital from May 2020 to April 2021. We created an MIS-C severity score using the following parameters: hospital admission status (e.g., floor vs intensive care unit), need for inotropic or vasoactive medications, and need for mechanical ventilation. Univariate and multivariate analyses were performed to associate risk factors corresponding to the MIS-C severity score. RESULTS: The study included 152 children who were followed for 14 days post hospital admission. A stepwise forward selection process identified seven physiologic variables associated with "severe" MIS-C according to a logistic regression. Specifically, a combination of elevated creatinine (p = 0.013), international normalized ratio (p = 0.002), brain natriuretic peptide (p = 0.001), white blood cell count (p = 0.009), ferritin (p = 0.041), respiratory rate (p = 0.047), and decreased albumin (p = 0.047) led to an excellent discrimination between mild versus severe MIS-C (AUC = 0.915). CONCLUSION: This study derived a physiologic profile associated with the stratification of MIS-C severity. IMPACT: Based on a cohort of 152 individuals diagnosed with MIS-C, this study derived a nomenclature that stratifies the severity of MIS-C. Investigated demographic, presentational vital signs, and blood analytes associated with severity of illness. Identification of a multivariate physiologic profile that strongly associates with MIS-C severity. This model allows the care team to recognize patients likely to require a higher level of intensive care.

Development of a peripheral blood transcriptomic gene signature to predict bronchopulmonary dysplasia.

Bronchopulmonary dysplasia (BPD) is the most common lung disease of extreme prematurity, yet mechanisms that associate with or identify neonates with increased susceptibility for BPD are largely unknown. Combining artificial intelligence with gene expression data is a novel approach that may assist in better understanding mechanisms underpinning chronic lung disease and in stratifying patients at greater risk for BPD. The objective of this study is to develop an early peripheral blood transcriptomic signature that can predict preterm neonates at risk for developing BPD. Secondary analysis of whole blood microarray data from 97 very low birth weight neonates on day of life 5 was performed. BPD was defined as positive pressure ventilation or oxygen requirement at 28 days of age. Participants were randomly assigned to a training (70%) and testing cohort (30%). Four gene-centric machine learning models were built, and their discriminatory abilities were compared with gestational age or birth weight. This study adheres to the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement. Neonates with BPD (n = 62 subjects) exhibited a lower median gestational age (26.0 wk vs. 30.0 wk, P < 0.01) and birth weight (800 g vs. 1,280 g, P < 0.01) compared with non-BPD neonates. From an initial pool (33,252 genes/patient), 4,523 genes exhibited a false discovery rate (FDR) <1%. The area under the receiver operating characteristic curve (AUC) for predicting BPD utilizing gestational age or birth weight was 87.8% and 87.2%, respectively. The machine learning models, using a combination of five genes, revealed AUCs ranging between 85.8% and 96.1%. Pathways integral to T cell development and differentiation were associated with BPD. A derived five-gene whole blood signature can accurately predict BPD in the first week of life.

Big Data for Tiny Patients: A Precision Medicine Approach to Bronchopulmonary Dysplasia.

Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease of extreme prematurity. Despite more than 50 years of research, current treatments are ineffective, and clinicians are largely unable to accurately predict which neonates the condition will develop in. A deeper understanding of the molecular mechanisms underlying the characteristic arrest in lung development are warranted. Integrating high-fidelity technology from precision medicine approaches may fill this gap and provide the tools necessary to identify biomarkers and targetable pathways. In this review, we describe insights garnered from current studies using omics for BPD prediction and stratification. We conclude by describing novel programs that will integrate multi-omics in efforts to better understand and treat the pathogenesis of BPD. [Pediatr Ann. 2022;51(10):e396-e404.].

Genomics and Pediatric Sepsis.

Sepsis is a clinical syndrome manifested by a dysregulation of the immune system triggered by an infection. The severity of illness is variable, which can include mild symptoms with no organ dysfunction to severe symptoms and multiorgan failure, eventually leading to death. Advances in bioinformatics have elucidated distinct sepsis endotypes and have allowed for a better understanding of the pathophysiologic mechanisms. As we learn more about these sepsis endotypes, more precise therapies will emerge for use as adjuncts to antibiotics. [Pediatr Ann. 2022;51(10):e387-e389.].

Social and Demographic Disparities in the Severity of Multisystem Inflammatory Syndrome in Children.

Social constructs are known risk factors for multisystem inflammatory syndrome in children. A review of 206 patients demonstrated that children who were non-Hispanic Black, over the age of 12 years or living in a disadvantaged neighborhood associated with severe multisystem inflammatory syndrome in children (intensive care unit admission, intubation and/or vasopressor use).

Comparison of Laboratory and Hemodynamic Time Series Data Across Original, Alpha, and Delta Variants in Patients With Multisystem Inflammatory Syndrome in Children.

OBJECTIVES: To compare the clinical, laboratory, and hemodynamic parameters during hospitalization for patients with multisystem inflammatory syndrome in children (MIS-C), across the Original/Alpha and the Delta variants of severe acute respiratory syndrome coronavirus 2 infection. DESIGN: Retrospective cohort study. SETTING: Single-center quaternary children's hospital. PATIENTS: Children with MIS-C admitted from May 2020 to February 2021(Original and Alpha variant cohort) and August 2021 to November 2021 (Delta variant cohort). MEASUREMENTS AND MAIN RESULTS: Continuous vital sign measurements, laboratory results, medications data, and hospital outcomes from all subjects were evaluated. Of the 134 patients (102 with Original/Alpha and 32 with Delta), median age was 9 years, 75 (56%) were male, and 61 (46%) were Hispanics. The cohort with Original/Alpha variant had more males (61% vs 41%; p = 0.036) and more respiratory/musculoskeletal symptoms on presentation compared with the Delta variant ( p < 0.05). More patients in the Original/Alpha variant cohort received mechanical ventilation (16 vs 0; p = 0.009). Median hospital length of stay (LOS) was 7 days, and ICU LOS was 3 days for the entire cohort. ICU LOS was shorter in cohort with the Delta variant compared with the Original/Alpha variant (4 vs 2 d; p = 0.001). Only one patient had cardiac arrest, two needed extracorporeal membrane oxygenation, and two needed left ventricular assist device (Impella, Danvers, MA), all in the Original/Alpha variant cohort; no mortality occurred in the entire cohort. MIS-C cohort associated with the Delta variant had lower INR, prothrombin time, WBCs, sodium, phosphorus, and potassium median values ( p < 0.05) during hospitalization compared with the Original/Alpha variants. Hemodynamic assessment showed significant tachycardia in the Original/Alpha variants cohort compared with the Delta variant cohort ( p < 0.05). INTERVENTIONS: None. CONCLUSIONS: Patients with MIS-C associated with the Delta variants had lower severity during hospitalization compared with the Original/Alpha variant. Analysis of distinct trends in clinical and laboratory parameters with future variants of concerns will allow for potential modification of treatment protocol.

Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review.

INTRODUCTION: Approximately 7,000 newborns die every day, accounting for almost half of child deaths under 5 years of age. Deciphering which neonates are at increased risk for mortality can have an important global impact. As such, integrating high computational technology (e.g., artificial intelligence [AI]) may help identify the early and potentially modifiable predictors of neonatal mortality. Therefore, the objective of this study was to collate, critically appraise, and analyze neonatal prediction studies that included AI. METHODS: A literature search was performed in PubMed, Cochrane, OVID, and Google Scholar. We included studies that used AI (e.g., machine learning (ML) and deep learning) to formulate prediction models for neonatal death. We excluded small studies (n < 500 individuals) and studies using only antenatal factors to predict mortality. Two independent investigators screened all articles for inclusion. The data collection consisted of study design, number of models, features used per model, feature importance, internal and/or external validation, and calibration analysis. Our primary outcome was the average area under the receiving characteristic curve (AUC) or sensitivity and specificity for all models included in each study. RESULTS: Of 434 articles, 11 studies were included. The total number of participants was 1.26 M with gestational ages ranging from 22 weeks to term. Number of features ranged from 3 to 66 with timing of prediction as early as 5 min of life to a maximum of 7 days of age. The average number of models per study was 4, with neural network, random forest, and logistic regression comprising the most used models (58.3%). Five studies (45.5%) reported calibration plots and 2 (18.2%) conducted external validation. Eight studies reported results by AUC and 5 studies reported the sensitivity and specificity. The AUC varied from 58.3% to 97.0%. The mean sensitivities ranged from 63% to 80% and specificities from 78% to 99%. The best overall model was linear discriminant analysis, but it also had a high number of features (n = 17). DISCUSSION/CONCLUSION: ML models can accurately predict death in neonates. This analysis demonstrates the most commonly used predictors and metrics for AI prediction models for neonatal mortality. Future studies should focus on external validation, calibration, as well as deployment of applications that can be readily accessible to health-care providers.

Demographic predictors of hospitalization and mortality in US children with COVID-19.

Understanding which children are at increased risk for poor outcome with COVID-19 is critical. In this study, we link pediatric population-based data from the US Center for Disease Control and Prevention to COVID-19 hospitalization and in-hospital death. In 27,045 US children with confirmed COVID-19, we demonstrate that African American [OR 2.28 (95% CI: 1.93, 2.70)] or mixed race [OR 2.95 (95% CI: 2.28, 3.82)] and an underlying medical condition [OR 3.55 (95% CI: 3.14, 4.01)] are strong predictors for hospitalization. Death occurred in 39 (0.19%) of 20,096 hospitalized children; children with a prior medical condition had an increased odd for death [OR 8.8 (95% CI: 3.7, 21.1)].Conclusion: Hospitalization and in-hospital death are rare in children diagnosed with COVID-19. However, children at higher risk for these outcomes include those with an underlying medical condition, as well as those of African American descent. What is Known: • Demographic factors are independent prognosticators of poor outcome in children with COVID-19. What is New: • Children with an underlying medical condition and those from an African American or mixed race/ethnicity are at high risk for COVID-19 hospitalization. • History of a comorbidity supersedes age, gender, and race/ethnicity as a risk factor for in-hospital pediatric COVID-19 death.

Effects of mesenchymal stromal cell-conditioned media on measures of lung structure and function: a systematic review and meta-analysis of preclinical studies.

BACKGROUND: Lung disease is a leading cause of morbidity and mortality. A breach in the lung alveolar-epithelial barrier and impairment in lung function are hallmarks of acute and chronic pulmonary illness. This review is part two of our previous work. In part 1, we demonstrated that CdM is as effective as MSCs in modulating inflammation. Herein, we investigated the effects of mesenchymal stromal cell (MSC)-conditioned media (CdM) on (i) lung architecture/function in animal models mimicking human lung disease, and (ii) performed a head-to-head comparison of CdM to MSCs. METHODS: Adhering to the animal Systematic Review Centre for Laboratory animal Experimentation protocol, we conducted a search of English articles in five medical databases. Two independent investigators collected information regarding lung: alveolarization, vasculogenesis, permeability, histologic injury, compliance, and measures of right ventricular hypertrophy and right pulmonary pressure. Meta-analysis was performed to generate random effect size using standardized mean difference with 95% confidence interval. RESULTS: A total of 29 studies met inclusion. Lung diseases included bronchopulmonary dysplasia, asthma, pulmonary hypertension, acute respiratory distress syndrome, chronic obstructive pulmonary disease, and pulmonary fibrosis. CdM improved all measures of lung structure and function. Moreover, no statistical difference was observed in any of the lung measures between MSCs and CdM. CONCLUSIONS: In this meta-analysis of animal models recapitulating human lung disease, CdM improved lung structure and function and had an effect size comparable to MSCs.

Multisystem inflammatory syndrome in children: A systematic review.

BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C), also known as pediatric inflammatory multisystem syndrome, is a new dangerous childhood disease that is temporally associated with coronavirus disease 2019 (COVID-19). We aimed to describe the typical presentation and outcomes of children diagnosed with this hyperinflammatory condition. METHODS: We conducted a systematic review to communicate the clinical signs and symptoms, laboratory findings, imaging results, and outcomes of individuals with MIS-C. We searched four medical databases to encompass studies characterizing MIS-C from January 1st, 2020 to July 25th, 2020. Two independent authors screened articles, extracted data, and assessed risk of bias. This review was registered with PROSPERO CRD42020191515. FINDINGS: Our search yielded 39 observational studies (n = 662 patients). While 71·0% of children (n = 470) were admitted to the intensive care unit, only 11 deaths (1·7%) were reported. Average length of hospital stay was 7·9 ± 0·6 days. Fever (100%, n = 662), abdominal pain or diarrhea (73·7%, n = 488), and vomiting (68·3%, n = 452) were the most common clinical presentation. Serum inflammatory, coagulative, and cardiac markers were considerably abnormal. Mechanical ventilation and extracorporeal membrane oxygenation were necessary in 22·2% (n = 147) and 4·4% (n = 29) of patients, respectively. An abnormal echocardiograph was observed in 314 of 581 individuals (54·0%) with depressed ejection fraction (45·1%, n = 262 of 581) comprising the most common aberrancy. INTERPRETATION: Multisystem inflammatory syndrome is a new pediatric disease associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is dangerous and potentially lethal. With prompt recognition and medical attention, most children will survive but the long-term outcomes from this condition are presently unknown. FUNDING: Parker B. Francis and pilot grant from 2R25-HL126140. Funding agencies had no involvement in the study.

COVID-19 in 7780 pediatric patients: A systematic review.

BACKGROUND: Studies summarizing the clinical picture of COVID-19 in children are lacking. This review characterizes clinical symptoms, laboratory, and imaging findings, as well as therapies provided to confirmed pediatric cases of COVID-19. METHODS: Adhering to PRISMA guidelines, we searched four medical databases (PubMed, LitCovid, Scopus, WHO COVID-19 database) between December 1, 2019 to May 14, 2020 using the keywords "novel coronavirus", "COVID-19" or "SARS-CoV-2". We included published or in press peer-reviewed cross-sectional, case series, and case reports providing clinical signs, imaging findings, and/or laboratory results of pediatric patients who were positive for COVID-19. Risk of bias was appraised through the quality assessment tool published by the National Institutes of Health. PROSPERO registration # CRD42020182261. FINDINGS: We identified 131 studies across 26 countries comprising 7780 pediatric patients. Although fever (59·1%) and cough (55·9%) were the most frequent symptoms 19·3% of children were asymptomatic. Patchy lesions (21·0%) and ground-glass opacities (32·9%) depicted lung radiograph and computed tomography findings, respectively. Immunocompromised children or those with respiratory/cardiac disease comprised the largest subset of COVID-19 children with underlying medical conditions (152 of 233 individuals). Coinfections were observed in 5.6% of children and abnormal laboratory markers included serum D-dimer, procalcitonin, creatine kinase, and interleukin-6. Seven deaths were reported (0·09%) and 11 children (0·14%) met inclusion for multisystem inflammatory syndrome in children. INTERPRETATION: This review provides evidence that children diagnosed with COVID-19 have an overall excellent prognosis. Future longitudinal studies are needed to confirm our findings and better understand which patients are at increased risk for developing severe inflammation and multiorgan failure. FUNDING: Parker B. Francis and pilot grant from 2R25-HL126140. Funding agencies had no involvement in the study.

Safety and efficacy of cell therapies in pediatric heart disease: a systematic review and meta-analysis.

BACKGROUND: Adult clinical trials have reported safety and the therapeutic potential of stem cells for cardiac disease. These observations have now translated to the pediatric arena. We conducted a meta-analysis to assess safety and efficacy of cell-based therapies in animal and human studies of pediatric heart disease. METHODS AND RESULTS: A literature search was conducted to examine the effects of cell-based therapies on: (i) safety and (ii) cardiac function. In total, 18 pre-clinical and 13 human studies were included. Pre-clinical: right ventricular dysfunction was the most common animal model (80%). Cardiac-derived (28%) and umbilical cord blood (24%) cells were delivered intravenously (36%) or intramyocardially (35%). Mortality was similar between cell-based and control groups (OR 0.94; 95% CI 0.05, 17.41). Cell-based treatments preserved ejection fraction by 6.9% (p < 0.01), while intramyocardial at a dose of 1-10 M cells/kg optimized ejection fraction. Clinical: single ventricle physiology was the most common cardiac disease (n = 9). Cardiac tissue was a frequent cell source, dosed from 3.0 × 105 to 2.4 × 107 cells/kg. A decrease in adverse events occurred in the cell-based cohort (OR 0.17, p < 0.01). Administration of cell-based therapies improved ejection fraction (MD 4.84; 95% CI 1.62, 8.07; p < 0.01). CONCLUSIONS: In this meta-analysis, cell-based therapies were safe and improved specific measures of cardiac function. Implications from this review may provide methodologic recommendations (source, dose, route, timing) for future clinical trials. Of note, many of the results described in this study pattern those seen in adult stem cell reviews and meta-analyses.

Outcomes of paediatric cardiac patients after 30 minutes of cardiopulmonary resuscitation prior to extracorporeal support.

OBJECTIVES: To characterise the mortality and neurological outcomes of paediatric cardiac patients requiring cardiopulmonary resuscitation for more than 30 minutes prior to extracorporeal membrane oxygenation cannulation and to identify risk factors associated with adverse outcomes in this population. MATERIALS AND METHODS: Observational retrospective cohort study in paediatric cardiac patients undergoing cardiopulmonary resuscitation for greater than 30 minutes prior to cannulation in a tertiary children's hospital, from July 2000 to July 2013. RESULTS: Seventy-three paediatric cardiac patients requiring cardiopulmonary resuscitation for more than 30 minutes prior to cannulation were included in the study. Survival to hospital discharge was 43.8%, with 75% of survivors having either normal neurologic function or only mild disability. Multivariable logistic regression analysis demonstrated that increased use of calcium during resuscitation (odds ratio 14.5, p 0.01), cardiopulmonary resuscitation duration >50 minutes (odds ratio 4.12, p 0.03), >6 interruptions of chest compressions during cannulation (odds ratio 6.40, p 0.03), the need for continuous renal replacement therapy (odds ratio 11.1, p 0.001), and abnormal pupillary response during extracorporeal membrane oxygenation (odds ratio 33.9, p 0.006) were independent predictors for hospital mortality. CONCLUSION: Survival after cardiopulmonary resuscitation for more than 30 minutes prior to extracorporeal membrane oxygenation cannulation in our paediatric cardiac cohort was 43.8%. Factors associated with mortality included calcium use during resuscitation, longer cardiopulmonary resuscitation, increased chest compression pauses during cannulation, the use of continuous renal replacement therapy, and abnormal pupils during extracorporeal membrane oxygenation support. A prospective assessment of these factors in paediatric cardiac patients may be beneficial in improving outcomes.

Mesenchymal stromal cell conditioned media for lung disease: a systematic review and meta-analysis of preclinical studies.

BACKGROUND: Inflammation plays an important role in the pathogenesis of many lung diseases. Preclinical studies suggest that mesenchymal stromal cell (MSC) conditioned media (CdM) can attenuate inflammation. Our aim was threefold: (1) summarize the existing animal literature evaluating CdM as a therapeutic agent for pediatric/adult lung disease, (2) quantify the effects of CdM on inflammation, and (3) compare inflammatory effects of CdM to MSCs. METHODS: Adhering to the Systematic Review Protocol for Animal Intervention Studies, a systematic search of English articles was performed in five databases. Meta-analysis and meta-regression were performed to generate random effect size using standardized mean difference (SMD). RESULTS: A total of 10 studies met inclusion. Lung diseases included bronchopulmonary dysplasia, asthma, pulmonary hypertension, and acute respiratory distress syndrome. CdM decreased inflammatory cells (1.02 SMD, 95% CI 0.86, 1.18) and cytokines (0.71 SMD, 95% CI 0.59, 0.84). The strongest effect for inflammatory cells was in bronchopulmonary dysplasia (3.74 SMD, 95% CI 3.13, 4.36) while pulmonary hypertension had the greatest reduction in inflammatory cytokine expression (1.44 SMD, 95% CI 1.18, 1.71). Overall, CdM and MSCs had similar anti-inflammatory effects. CONCLUSIONS: In this meta-analysis of animal models recapitulating lung disease, CdM improved inflammation and had an effect size comparable to MSCs. While these findings are encouraging, the risk of bias and heterogeneity limited the strength of our findings.