Dysregulated airway epithelial antiviral immunity in Down Syndrome impairs type III IFN response and amplifies airway inflammation during RSV infection.

Trisomy 21 (TS21), also known as Down syndrome (DS), increases pediatric mortality risk from respiratory syncytial virus (RSV) by nine-fold, yet its underlying immunological basis remains unclear. Here, we investigated RSV-induced immunological responses in TS21 airway epithelial cells (AECs), the primary site of respiratory virus entry and host defense. TS21 AECs exhibit hyperactive interferon (IFN) signaling and reduced RSV infectivity, but they also show impaired type-III IFN responses during viral infection.

Leveraging a global, federated, real-world data network to optimize investigator-initiated pediatric clinical trials: the TriNetX Pediatric Collaboratory Network.

Objective: Clinical research networks facilitate collaborative research, but data sharing remains a common barrier.

Materials And Methods: The TriNetX platform provides real-time access to electronic health record (EHR)-derived, anonymized data from 173 healthcare organizations (HCOs) and tools for queries and analysis. In 2022, 4 pediatric HCOs worked with TriNetX leadership to found the Pediatric Collaboratory Network (PCN), facilitated via a multi-institutional data-use agreement (DUA).

Post-acute and Chronic Kidney Function Outcomes of COVID-19 in Children and Adolescents: An EHR Cohort Study from the RECOVER Initiative.

We investigated the risks of post-acute and chronic adverse kidney outcomes of SARS-CoV-2 infection in the pediatric population via a retrospective cohort study using data from the RECOVER program. We included 1,864,637 children and adolescents under 21 from 19 children's hospitals and health institutions in the US with at least six months of follow-up time between March 2020 and May 2023. We divided the patients into three strata: patients with pre-existing chronic kidney disease (CKD), patients with acute kidney injury (AKI) during the acute phase (within 28 days) of SARS-CoV-2 infection, and patients without pre-existing CKD or AKI.

Pediatric Gastrointestinal Outcomes During the Post-Acute Phase of COVID-19: Findings from RECOVER Initiative from 29 Hospitals in the US.

Importance: The profile of gastrointestinal (GI) outcomes that may affect children in post-acute and chronic phases of COVID-19 remains unclear.

Objective: To investigate the risks of GI symptoms and disorders during the post-acute phase (28 days to 179 days after SARS-CoV-2 infection) and the chronic phase (180 days to 729 days after SARS-CoV-2 infection) in the pediatric population.

Design: We used a retrospective cohort design from March 2020 to Sept 2023.

External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients.

Objective: To assess the single site performance of the Dynamic Criticality Index (CI-D) models developed from a multi-institutional database to predict future care. Secondarily, to assess future care-location predictions in a single institution when CI-D models are re-developed using single-site data with identical variables and modeling methods. Four CI-D models were assessed for predicting care locations >6-12 hours, >12-18 hours, >18-24 hours, and >24-30 hours in the future.

High TSLP responses in the human infant airways are associated with pre-activated airway epithelial IFN antiviral immunity.

Thymic stromal lymphopoietin (TSLP) is a primarily epithelial-derived cytokine that drives type 2 allergic immune responses. Early life viral respiratory infections elicit high TSLP production, which leads to the development of type 2 inflammation and airway hyperreactivity. The goal of this study was to examine in vivo and in vitro the human airway epithelial responses leading to high TSLP production during viral respiratory infections in early infancy.

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach.

The functional impact of 1,570 individual amino acid substitutions in human OTC.

Deleterious mutations in the X-linked gene encoding ornithine transcarbamylase (OTC) cause the most common urea cycle disorder, OTC deficiency. This rare but highly actionable disease can present with severe neonatal onset in males or with later onset in either sex. Individuals with neonatal onset appear normal at birth but rapidly develop hyperammonemia, which can progress to cerebral edema, coma, and death, outcomes ameliorated by rapid diagnosis and treatment.

Clinical Instability Is a Sign of Severity of Illness: A Cohort Study.

Objectives: Test the hypothesis that within patient clinical instability measured by deterioration and improvement in mortality risk over 3-, 6-, 9-, and 12-hour time intervals is indicative of increasing severity of illness.

Design: Analysis of electronic health data from January 1, 2018, to February 29, 2020.

Setting: PICU and cardiac ICU at an academic children's hospital.

Childbirth after perioperative systemic therapy in patients with breast cancer: a retrospective single institutional study in Japan.

Background: The number of breast cancer patients of childbearing age has been increasing. Therefore, we investigated the characteristics and the childbearing status of the patients who received systemic therapy for breast cancer during their childbearing age to better understand the clinical impact of childbirth.

Methods: Female patients with breast cancer younger than 40 years old who underwent surgery and received perioperative systemic therapy from 2007 to 2014 were included in this study.

The impact of obesity and endocrine therapy on the prognosis of premenopausal women with hormone receptor-positive breast cancer: A single-institute retrospective study.

Background: Higher body mass index (BMI) is associated with worse prognosis in pre- and postmenopausal patients with breast cancer (BC). However, there is insufficient evidence regarding the optimal adjuvant endocrine therapy for obese premenopausal women with hormone receptor (HR)-positive BC.

Aim: To evaluate the impact of obesity and adjuvant endocrine therapy on prognosis in premenopausal patients with BC.

A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program.

Background: As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data.

Methods And Findings: In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach.

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.

Background: The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change.

Objectives: To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions.

Association of Congenital and Acquired Cardiovascular Conditions With COVID-19 Severity Among Pediatric Patients in the US.

Importance: Identifying the associations between severe COVID-19 and individual cardiovascular conditions in pediatric patients may inform treatment.

Objective: To assess the association between previous or preexisting cardiovascular conditions and severity of COVID-19 in pediatric patients.

Design, Setting, And Participants: This retrospective cohort study used data from a large, multicenter, electronic health records database in the US.

Label-free multiphoton excitation imaging as a promising diagnostic tool for breast cancer.

Histopathological diagnosis is the ultimate method of attaining the final diagnosis; however, the observation range is limited to the two-dimensional plane, and it requires thin slicing of the tissue, which limits diagnostic information. To seek solutions for these problems, we proposed a novel imaging-based histopathological examination. We used the multiphoton excitation microscopy (MPM) technique to establish a method for visualizing unfixed/unstained human breast tissues.

Validation of a computational phenotype for finding patients eligible for genetic testing for pathogenic PTEN variants across three centers.

Background: Computational phenotypes are most often combinations of patient billing codes that are highly predictive of disease using electronic health records (EHR). In the case of rare diseases that can only be diagnosed by genetic testing, computational phenotypes identify patient cohorts for genetic testing and possible diagnosis. This article details the validation of a computational phenotype for PTEN hamartoma tumor syndrome (PHTS) against the EHR of patients at three collaborating clinical research centers: Boston Children's Hospital, Children's National Hospital, and the University of Washington.

Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models.

Objectives: Assess a machine learning method of serially updated mortality risk.

Design: Retrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO).

Setting: Hospitals caring for children in ICUs.

Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative.

Objective: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations.