Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program.

Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients' clinical histories to then identify groups of patients with similar presentations.

Development and validation of a federated learning framework for detection of subphenotypes of multisystem inflammatory syndrome in children.

Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection. The highly diverse clinical features of MIS-C necessities characterizing its features by subphenotypes for improved recognition and treatment. However, jointly identifying subphenotypes in multi-site settings can be challenging.

Vaccine Effectiveness Against Long COVID in Children.

Objectives: Vaccination reduces the risk of acute coronavirus disease 2019 (COVID-19) in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5 to 17 years.

Methods: This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record program for visits after vaccine availability.

Spectrum of severity of multisystem inflammatory syndrome in children: an EHR-based cohort study from the RECOVER program.

Multi-system inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection in children, and there is a critical need to unfold its highly heterogeneous disease patterns. Our objective was to characterize the illness spectrum of MIS-C for improved recognition and management. We conducted a retrospective cohort study using data from March 1, 2020-September 30, 2022, in 8 pediatric medical centers from PEDSnet.

The Preserving Kidney Function in Children With CKD (PRESERVE) Study: Rationale, Design, and Methods.

Rationale & Objective: PRESERVE seeks to provide new knowledge to inform shared decision-making regarding blood pressure (BP) management for pediatric chronic kidney disease (CKD). PRESERVE will compare the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; expand the National Patient-Centered Clinical Research Network (PCORnet) common data model by adding pediatric- and kidney-specific variables and linking electronic health record data to other kidney disease databases; and assess the lived experiences of patients related to BP management.

Study Design: Multicenter retrospective cohort study (clinical outcomes) and cross-sectional study (patient-reported outcomes [PROs]).

Vaccine Effectiveness Against Long COVID in Children: A Report from the RECOVER EHR Cohort.

Objective: Vaccination reduces the risk of acute COVID-19 in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5-17 years.

Methods: This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record (EHR) Program for visits between vaccine availability, and October 29, 2022.

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 real world experience of pediatric primary hyperoxaluria patients in the PEDSnet clinical research network.

The rarity of primary hyperoxaluria (PH) challenges our understanding of the disease. The purpose of our study was to describe the course of clinical care in a United States cohort of PH pediatric patients, highlighting health service utilization. We performed a retrospective cohort study of PH patients < 18 years old in the PEDSnet clinical research network from 2009 to 2021.

Clinical course and management of children with IgA vasculitis with nephritis.

Background: IgA vasculitis is the most common vasculitis in children and is often complicated by acute nephritis (IgAVN). Risk of chronic kidney disease (CKD) among children with IgAVN remains unknown. This study aimed to describe the clinical management and kidney outcomes in a large cohort of children with IgAVN.

Understanding pediatric long COVID using a tree-based scan statistic approach: an EHR-based cohort study from the RECOVER Program.

Objectives: Post-acute sequalae of SARS-CoV-2 infection (PASC) is not well defined in pediatrics given its heterogeneity of presentation and severity in this population. The aim of this study is to use novel methods that rely on data mining approaches rather than clinical experience to detect conditions and symptoms associated with pediatric PASC.

Materials And Methods: We used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.

Leveraging Serologic Testing to Identify Children at Risk For Post-Acute Sequelae of SARS-CoV-2 Infection: An Electronic Health Record-Based Cohort Study from the RECOVER Program.

Using an electronic health record-based algorithm, we identified children with Coronavirus disease 2019 (COVID-19) based exclusively on serologic testing between March 2020 and April 2022. Compared with the 131 537 polymerase chain reaction-positive children, the 2714 serology-positive children were more likely to be inpatients (24% vs 2%), to have a chronic condition (37% vs 24%), and to have a diagnosis of multisystem inflammatory syndrome in children (23% vs <1%). Identification of children who could have been asymptomatic or paucisymptomatic and not tested is critical to define the burden of post-acute sequelae of severe acute respiratory syndrome coronavirus 2 infection in children.

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.

Clinical Subphenotypes of Multisystem Inflammatory Syndrome in Children: An EHR-based cohort study from the RECOVER program.

Background: Multi-system inflammatory syndrome in children (MIS-C) represents one of the most severe post-acute sequelae of SARS-CoV-2 infection in children, and there is a critical need to characterize its disease patterns for improved recognition and management. Our objective was to characterize subphenotypes of MIS-C based on presentation, demographics and laboratory parameters.

Methods: We conducted a retrospective cohort study of children with MIS-C from March 1, 2020 - April 30, 2022 and cared for in 8 pediatric medical centers that participate in PEDSnet.

Skeletal Outcomes in Children and Young Adults with Glomerular Disease.

Background: Children with glomerular disease have unique risk factors for compromised bone health. Studies addressing skeletal complications in this population are lacking.

Methods: This retrospective cohort study utilized data from PEDSnet, a national network of pediatric health systems with standardized electronic health record data for more than 6.

Testing an Automated Approach to Identify Variation in Outcomes among Children with Type 1 Diabetes across Multiple Sites.

Introduction: Efficient methods to obtain and benchmark national data are needed to improve comparative quality assessment for children with type 1 diabetes (T1D). PCORnet is a network of clinical data research networks whose infrastructure includes standardization to a Common Data Model (CDM) incorporating electronic health record (EHR)-derived data across multiple clinical institutions. The study aimed to determine the feasibility of the automated use of EHR data to assess comparative quality for T1D.

Clinical Features and Burden of Postacute Sequelae of SARS-CoV-2 Infection in Children and Adolescents.

Importance: The postacute sequelae of SARS-CoV-2 infection (PASC) has emerged as a long-term complication in adults, but current understanding of the clinical presentation of PASC in children is limited.

Objective: To identify diagnosed symptoms, diagnosed health conditions, and medications associated with PASC in children.

Design, Setting And Participants: This retrospective cohort study used electronic health records from 9 US children's hospitals for individuals younger than 21 years who underwent antigen or reverse transcriptase-polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 between March 1, 2020, and October 31, 2021, and had at least 1 encounter in the 3 years before testing.

Diagnostic Code-Based Screening for Identifying Children with Primary Hyperoxaluria.

Purpose: We evaluated the utility of diagnostic codes to screen for patients with primary hyperoxaluria (PH) and evaluate their positive predictive value (PPV) in identifying children with this rare condition in PEDSnet, a clinical research network of pediatric health systems that shares electronic health records data.

Materials And Methods: We conducted a cross-sectional study of children who received care at 7 PEDSnet institutions from January 2009 through January 2021. We developed and applied screening criteria using diagnostic codes that generated 3 categories of the hypothesized probability of PH.

Associations Between Weight and Lower Respiratory Tract Disease Outcomes in Hospitalized Children.

Objective: To identify associations between weight status and clinical outcomes in children with lower respiratory tract infection (LRTI) or asthma requiring hospitalization.

Methods: We performed a retrospective cohort study of 2 to 17 year old children hospitalized for LRTI and/or asthma from 2009 to 2019 using electronic health record data from the PEDSnet clinical research network. Children <2 years, those with medical complexity, and those without a calculable BMI were excluded.