Real-World Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.

BACKGROUND: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. OBJECTIVE: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. DESIGN: Comparative effectiveness research accounting for underreported vaccination in 3 study cohorts: adolescents (12 to 20 years) during the Delta phase and children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. SETTING: A national collaboration of pediatric health systems (PEDSnet). PARTICIPANTS: 77 392 adolescents (45 007 vaccinated) during the Delta phase and 111 539 children (50 398 vaccinated) and 56 080 adolescents (21 180 vaccinated) during the Omicron phase. INTERVENTION: First dose of the BNT162b2 vaccine versus no receipt of COVID-19 vaccine. MEASUREMENTS: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100, with confounders balanced via propensity score stratification. RESULTS: During the Delta period, the estimated effectiveness of the BNT162b2 vaccine was 98.4% (95% CI, 98.1% to 98.7%) against documented infection among adolescents, with no statistically significant waning after receipt of the first dose. An analysis of cardiac complications did not suggest a statistically significant difference between vaccinated and unvaccinated groups. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (CI, 72.2% to 76.2%). Higher levels of effectiveness were seen against moderate or severe COVID-19 (75.5% [CI, 69.0% to 81.0%]) and ICU admission with COVID-19 (84.9% [CI, 64.8% to 93.5%]). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (CI, 83.8% to 87.1%), with 84.8% (CI, 77.3% to 89.9%) against moderate or severe COVID-19, and 91.5% (CI, 69.5% to 97.6%) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined 4 months after the first dose and then stabilized. The analysis showed a lower risk for cardiac complications in the vaccinated group during the Omicron variant period. LIMITATION: Observational study design and potentially undocumented infection. CONCLUSION: This study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time. PRIMARY FUNDING SOURCE: National Institutes of Health.

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.

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. METHODS: This observational cohort study used the PEDSnet database to identify children diagnosed with IgAV between January 1, 2009, and February 29, 2020. Demographic and clinical characteristics were compared among children with and without kidney involvement. For children followed by nephrology, clinical course, and management patterns were described. Patients were divided into four categories based on treatment: observation, renin-angiotensin-aldosterone system (RAAS) blockade, corticosteroids, and other immunosuppression, and outcomes were compared among these groups. RESULTS: A total of 6802 children had a diagnosis of IgAV, of whom 1139 (16.7%) were followed by nephrology for at least 2 visits over a median follow-up period of 1.7 years [0.4,4.2]. Conservative management was the most predominant practice pattern, consisting of observation in 57% and RAAS blockade in 6%. Steroid monotherapy was used in 29% and other immunosuppression regimens in 8%. Children receiving immunosuppression had higher rates of proteinuria and hypertension compared to those managed with observation (p < 0.001). At the end of follow-up, 2.6 and 0.5% developed CKD and kidney failure, respectively. CONCLUSIONS: Kidney outcomes over a limited follow-up period were favorable in a large cohort of children with IgAV. Immunosuppressive medications were used in those with more severe presentations and may have contributed to improved outcomes. A higher resolution version of the Graphical abstract is available as Supplementary information.

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. Outcomes queried included diagnostic imaging and testing related to known organ involvement of PH, surgical and medical interventions specific to PH-related renal disease, and select PH-related hospital service utilization. Outcomes were evaluated relative to cohort entrance date (CED), defined as date of first PH-related diagnostic code. Thirty-three patients were identified: 23 with PH type 1; 4 with PH type 2; 6 with PH type 3. Median age at CED was 5.0 years (IQR 1.4, 9.3 years) with the majority being non-Hispanic white (73%) males (70%). Median follow-up between CED and most recent encounter was 5.1 years (IQR 1.2, 6.8). Nephrology and Urology were the most common specialties involved in care, with low utilization of other sub-specialties (12%-36%). Most patients (82%) had diagnostic imaging used to evaluate kidney stones; 11 (33%) had studies of extra-renal involvement. Stone surgery was performed in 15 (46%) patients. Four patients (12%) required dialysis, begun in all prior to CED; four patients required renal or renal/liver transplant.    Conclusion: In this large cohort of U.S. PH children, patients required heavy health care utilization with room for improvement in involving multi-disciplinary specialists. What is Known: • Primary hyperoxaluria (PH) is rare with significant implications on patient health. Typical involvement includes the kidneys; however, extra-renal manifestations occur. • Most large population studies describe clinical manifestations and involve registries. What is New: • We report the clinical journey, particularly related to diagnostic studies, interventions, multispecialty involvement, and hospital utilization, of a large cohort of PH pediatric patients in the PEDSnet clinical research network. • There are missed opportunities, particularly in that of specialty care, that could help in the diagnosis, treatment, and even prevention of known clinical manifestations.

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 6.5 million patients from 2009 to 2021. Incidence rates (per 10,000 person-years) of fracture, slipped capital femoral epiphysis (SCFE), and avascular necrosis/osteonecrosis (AVN) in 4598 children and young adults with glomerular disease were compared with those among 553,624 general pediatric patients using Poisson regression analysis. The glomerular disease cohort was identified using a published computable phenotype. Inclusion criteria for the general pediatric cohort were two or more primary care visits 1 year or more apart between 1 and 21 years of age, one visit or more every 18 months if followed >3 years, and no chronic progressive conditions defined by the Pediatric Medical Complexity Algorithm. Fracture, SCFE, and AVN were identified using SNOMED-CT diagnosis codes; fracture required an associated x-ray or splinting/casting procedure within 48 hours. RESULTS: We found a higher risk of fracture for the glomerular disease cohort compared with the general pediatric cohort in girls only (incidence rate ratio [IRR], 1.6; 95% CI, 1.3 to 1.9). Hip/femur and vertebral fracture risk were increased in the glomerular disease cohort: adjusted IRR was 2.2 (95% CI, 1.3 to 3.7) and 5 (95% CI, 3.2 to 7.6), respectively. For SCFE, the adjusted IRR was 3.4 (95% CI, 1.9 to 5.9). For AVN, the adjusted IRR was 56.2 (95% CI, 40.7 to 77.5). CONCLUSIONS: Children and young adults with glomerular disease have significantly higher burden of skeletal complications than the general pediatric population.

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. Tier 1 had specific diagnostic codes for PH; tier 2 had codes for hyperoxaluria, oxalate nephropathy, or oxalosis; and tier 3 had a combination of ≥2 codes for disorder of carbohydrate metabolism and ≥1 code for kidney stones. We reviewed the electronic health records of patients with possible PH to confirm PH diagnosis and evaluate the accuracy and timing of diagnostic codes. The PPV of the codes was compared across tiers, time, PH type, and site. RESULTS: We identified 341 patients in the screen; 33 had confirmed PH (9.7%). Tier 1 had the highest proportion of PH; however, the PPV was only 20%. The degree to which an institution accurately represented point of care diagnoses in the data extraction process was predictive of higher PPV. The PPV of diagnostic codes was highest for PH3 (100%) and lowest for PH1 (22.8%). CONCLUSIONS: Diagnostic codes for PH have poor PPV. Findings suggest that one should be careful in research using large databases in which source validation is not possible.

Behavioral Health Diagnoses in Youth with Gender Dysphoria Compared with Controls: A PEDSnet Study.

OBJECTIVE: To assess the odds of a psychiatric or neurodevelopmental diagnosis among youth with a diagnosis of gender dysphoria compared with matched controls in a large electronic health record dataset from 6 pediatric health systems, PEDSnet. We hypothesized that youth with gender dysphoria would have higher odds of having psychiatric and neurodevelopmental diagnoses than controls. STUDY DESIGN: All youth with a diagnosis of gender dysphoria (n = 4173 age at last visit 16.2 ± 3.4) and at least 1 outpatient encounter were extracted from the PEDSnet database and propensity-score matched on 8 variables to controls without gender dysphoria (n = 16 648, age at last visit 16.2 ± 4.8) using multivariable logistic regression. The odds of having psychiatric and neurodevelopmental diagnoses were examined using generalized estimating equations. RESULTS: Youth with gender dysphoria had higher odds of psychiatric (OR 4.0 [95% CI 3.8, 4.3] P < .0001) and neurodevelopmental diagnoses (1.9 [1.7, 2.0], P < .0001). Youth with gender dysphoria were more likely to have a diagnosis across all psychiatric disorder subcategories, with particularly high odds of mood disorder (7.3 [6.8, 7.9], P < .0001) and anxiety (5.5 [5.1, 5.9], P < .0001). Youth with gender dysphoria had a greater odds of autism spectrum disorder (2.6, [2.2, 3.0], P < .0001). CONCLUSIONS: Youth with gender dysphoria at large pediatric health systems have greater odds of psychiatric and several neurodevelopmental diagnoses compared with youth without gender dysphoria. Further studies are needed to evaluate changes in mental health over time with access to gender affirming care.

Behavioral Health Diagnoses in Youth with Differences of Sex Development or Congenital Adrenal Hyperplasia Compared with Controls: A PEDSnet Study.

OBJECTIVE: To evaluate the odds of a behavioral health diagnosis among youth with differences of sex development (DSD) or congenital adrenal hyperplasia (CAH) compared with matched controls in the PEDSnet database. STUDY DESIGN: All youth with a diagnosis of DSD (n = 1216) or CAH (n = 1647) and at least 1 outpatient encounter were extracted from the PEDSnet database and propensity-score matched on 8 variables (1:4) with controls (n = 4864 and 6588, respectively) using multivariable logistic regression. The likelihood of having behavioral health diagnoses was examined using generalized estimating equations. RESULTS: Youth with DSD had higher odds of a behavioral health diagnosis (OR, 1.7; 95% CI, 1.4-2.1; P < .0001) and neurodevelopmental diagnosis (OR, 1.7; 95% CI, 1.4, 2.0; P < .0001) compared with matched controls. Youth with CAH did not have an increased odds of a behavioral health diagnosis (OR, 1.0; 95% CI, 0.9, 1.1; P = .9) compared with matched controls but did have higher odds of developmental delay (OR, 1.8; 95% CI, 1.4, 2.4; P < .0001). CONCLUSIONS: Youth with DSD diagnosis have higher odds of a behavioral health or neurodevelopmental diagnosis compared with matched controls. Youth with CAH have higher odds of developmental delay, highlighting the need for screening in both groups.

Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research.

BACKGROUND: The rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients. METHODS: The authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children's hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798). RESULTS: The final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months. CONCLUSIONS: The authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.

Unintended consequences of evolution of the Common Terminology Criteria for Adverse Events.

BACKGROUND: Adverse events (AEs) on Children's Oncology Group (COG) trials are reported manually by clinical research assistants (CRAs). The Common Terminology Criteria for Adverse Events (CTCAE) was developed to provide standardized definitions for identifying and grading AEs. The CTCAE has expanded significantly over its five versions, but the impact of CTCAE definitional changes has not been examined. PROCEDURE: This study compared AE number and ascertainment among the first four CTCAE versions using a case vignette. Each CTCAE version was used to create a list of AEs and grades by two separate CRAs. RESULTS: The CTCAE expanded from 9 categories and 49 AEs in v1.0 to 26 categories and 790 AEs in v4.0. CRAs independently selected different approaches to AE ascertainment-comprehensive and parsimonious. The number of AEs identified in the parsimonious approach was stable with 10-14 in each CTC version. The comprehensive approach identified 9, 20, 29, and 37 AEs in CTC versions 1.0, 2.0, 3.0, and 4.0, respectively. Only approximately 65% of AEs were conclusively graded in versions 2.0 to 4.0 using the comprehensive approach. CONCLUSIONS: CTCAE has increased in complexity. Although this increased complexity allows for more granular AE reporting, these data demonstrate potential unintended negative consequences of increasing CTC AE complexity, including the risk of varying approaches to AE capture. A comprehensive evaluation of CTC AE definitions and CRA reporting practices across COG institutions and AEs are needed to improve the accuracy and efficiency of AE reporting.

Development and evaluation of a computable phenotype to identify pediatric patients with leukemia and lymphoma treated with chemotherapy using electronic health record data.

BACKGROUND: Widespread implementation of electronic health records (EHR) has created new opportunities for pediatric oncology observational research. Little attention has been given to using EHR data to identify patients with pediatric hematologic malignancies. METHODS: This study used EHR-derived data in a pediatric clinical data research network, PEDSnet, to develop and evaluate a computable phenotype algorithm to identify pediatric patients with leukemia and lymphoma who received treatment with chemotherapy. To guide early development, multiple computable phenotype-defined cohorts were compared to one institution's tumor registry. The most promising algorithm was chosen for formal evaluation and consisted of at least two leukemia/lymphoma diagnoses (Systematized Nomenclature of Medicine codes) within a 90-day period, two chemotherapy exposures, and three hematology-oncology provider encounters. During evaluation, the computable phenotype was executed against EHR data from 2011 to 2016 at three large institutions. Classification accuracy was assessed by masked medical record review with phenotype-identified patients compared to a control group with at least three hematology-oncology encounters. RESULTS: The computable phenotype had sensitivity of 100% (confidence interval [CI] 99%, 100%), specificity of 99% (CI 99%, 100%), positive predictive value (PPV) and negative predictive value (NPV) of 100%, and C-statistic of 1 at the development institution. The computable phenotype performance was similar at the two test institutions with sensitivity of 100% (CI 99%, 100%), specificity of 99% (CI 99%, 100%), PPV of 96%, NPV of 100%, and C-statistic of 0.99. CONCLUSION: The EHR-based computable phenotype is an accurate cohort identification tool for pediatric patients with leukemia and lymphoma who have been treated with chemotherapy and is ready for use in clinical studies.

EHR-based Case Identification of Pediatric Long COVID: A Report from the RECOVER EHR Cohort.

Objective: Long COVID, marked by persistent, recurring, or new symptoms post-COVID-19 infection, impacts children's well-being yet lacks a unified clinical definition. This study evaluates the performance of an empirically derived Long COVID case identification algorithm, or computable phenotype, with manual chart review in a pediatric sample. This approach aims to facilitate large-scale research efforts to understand this condition better. Methods: The algorithm, composed of diagnostic codes empirically associated with Long COVID, was applied to a cohort of pediatric patients with SARS-CoV-2 infection in the RECOVER PCORnet EHR database. The algorithm classified 31,781 patients with conclusive, probable, or possible Long COVID and 307,686 patients without evidence of Long COVID. A chart review was performed on a subset of patients (n=651) to determine the overlap between the two methods. Instances of discordance were reviewed to understand the reasons for differences. Results: The sample comprised 651 pediatric patients (339 females, M age = 10.10 years) across 16 hospital systems. Results showed moderate overlap between phenotype and chart review Long COVID identification (accuracy = 0.62, PPV = 0.49, NPV = 0.75); however, there were also numerous cases of disagreement. No notable differences were found when the analyses were stratified by age at infection or era of infection. Further examination of the discordant cases revealed that the most common cause of disagreement was the clinician reviewers' tendency to attribute Long COVID-like symptoms to prior medical conditions. The performance of the phenotype improved when prior medical conditions were considered (accuracy = 0.71, PPV = 0.65, NPV = 0.74). Conclusions: Although there was moderate overlap between the two methods, the discrepancies between the two sources are likely attributed to the lack of consensus on a Long COVID clinical definition. It is essential to consider the strengths and limitations of each method when developing Long COVID classification algorithms.

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. We propose a distributed multi-site latent class analysis (dMLCA) approach to jointly learn MIS-C subphenotypes using data across multiple institutions. Methods: We used data from the electronic health records (EHR) systems across nine U.S. children's hospitals. Among the 3,549,894 patients, we extracted 864 patients < 21 years of age who had received a diagnosis of MIS-C during an inpatient stay or up to one day before admission. Using MIS-C conditions, laboratory results, and procedure information as input features for the patients, we applied our dMLCA algorithm and identified three MIS-C subphenotypes. As validation, we characterized and compared more granular features across subphenotypes. To evaluate the specificity of the identified subphenotypes, we further compared them with the general subphenotypes identified in the COVID-19 infected patients. Findings: Subphenotype 1 (46.1%) represents patients with a mild manifestation of MIS-C not requiring intensive care, with minimal cardiac involvement. Subphenotype 2 (25.3%) is associated with a high risk of shock, cardiac and renal involvement, and an intermediate risk of respiratory symptoms. Subphenotype 3 (28.6%) represents patients requiring intensive care, with a high risk of shock and cardiac involvement, accompanied by a high risk of >4 organ system being impacted. Importantly, for hospital-specific clinical decision-making, our algorithm also revealed a substantial heterogeneity in relative proportions of these three subtypes across hospitals. Properly accounting for such heterogeneity can lead to accurate characterization of the subphenotypes at the patient-level. Interpretation: Our identified three MIS-C subphenotypes have profound implications for personalized treatment strategies, potentially influencing clinical outcomes. Further, the proposed algorithm facilitates federated subphenotyping while accounting for the heterogeneity across hospitals.

Systematic data quality assessment of electronic health record data to evaluate study-specific fitness: Report from the PRESERVE research study.

Study-specific data quality testing is an essential part of minimizing analytic errors, particularly for studies making secondary use of clinical data. We applied a systematic and reproducible approach for study-specific data quality testing to the analysis plan for PRESERVE, a 15-site, EHR-based observational study of chronic kidney disease in children. This approach integrated widely adopted data quality concepts with healthcare-specific evaluation methods. We implemented two rounds of data quality assessment. The first produced high-level evaluation using aggregate results from a distributed query, focused on cohort identification and main analytic requirements. The second focused on extended testing of row-level data centralized for analysis. We systematized reporting and cataloguing of data quality issues, providing institutional teams with prioritized issues for resolution. We tracked improvements and documented anomalous data for consideration during analyses. The checks we developed identified 115 and 157 data quality issues in the two rounds, involving completeness, data model conformance, cross-variable concordance, consistency, and plausibility, extending traditional data quality approaches to address more complex stratification and temporal patterns. Resolution efforts focused on higher priority issues, given finite study resources. In many cases, institutional teams were able to correct data extraction errors or obtain additional data, avoiding exclusion of 2 institutions entirely and resolving 123 other gaps. Other results identified complexities in measures of kidney function, bearing on the study's outcome definition. Where limitations such as these are intrinsic to clinical data, the study team must account for them in conducting analyses. This study rigorously evaluated fitness of data for intended use. The framework is reusable and built on a strong theoretical underpinning. Significant data quality issues that would have otherwise delayed analyses or made data unusable were addressed. This study highlights the need for teams combining subject-matter and informatics expertise to address data quality when working with real world data.

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. We examined both probable (symptom-based) and diagnosed long COVID after vaccination. RESULTS: The vaccination rate was 67% in the cohort of 1 037 936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, whereas diagnosed long COVID was 0.8%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5-44.7) against probable long COVID and 41.7% (15.0-60.0) against diagnosed long COVID. VE was higher for adolescents (50.3% [36.6-61.0]) than children aged 5 to 11 (23.8% [4.9-39.0]). VE was higher at 6 months (61.4% [51.0-69.6]) but decreased to 10.6% (-26.8% to 37.0%) at 18-months. CONCLUSIONS: This large retrospective study shows moderate protective effect of severe acute respiratory coronavirus 2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including electronic health record sources and prospective data.

Can Multisystem Inflammatory Syndrome in Children Be Managed in the Outpatient Setting? An EHR-Based Cohort Study From the RECOVER Program.

Using electronic health record data combined with primary chart review, we identified seven children across nine participant pediatric medical centers with a diagnosis of Multisystem Inflammatory Syndrome in Children (MIS-C) managed exclusively as outpatients. These findings should raise awareness of mild presentations of MIS-C and the option of outpatient management.

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. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.

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 (U09.9) (N = 1309) to children with (N = 6545) and without (N = 6545) SARS-CoV-2 infection. We used a tree-based scan statistic to identify potential condition clusters co-occurring more frequently in cases than controls. Results: We found significant enrichment among children with PASC in cardiac, respiratory, neurologic, psychological, endocrine, gastrointestinal, and musculoskeletal systems, the most significant related to circulatory and respiratory such as dyspnea, difficulty breathing, and fatigue and malaise. Discussion: Our study addresses methodological limitations of prior studies that rely on prespecified clusters of potential PASC-associated diagnoses driven by clinician experience. Future studies are needed to identify patterns of diagnoses and their associations to derive clinical phenotypes. Conclusion: We identified multiple conditions and body systems associated with pediatric PASC. Because we rely on a data-driven approach, several new or under-reported conditions and symptoms were detected that warrant further investigation.

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. We included 1139 children hospitalized with MIS-C and used their demographics, symptoms, conditions, laboratory values, and medications for analyses. We applied heterogeneity-adaptive latent class analyses and identified three latent classes. We further characterized the sociodemographic and clinical characteristics of the latent classes and evaluated their temporal patterns. Class 1 (47.9%) represented children with the most severe presentation, with more admission to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 (23.3%) represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 (28.8%) represented a mild presentation. Our results indicated that MIS-C has a spectrum of clinical severity ranging from mild to severe and the proportion of severe or critical MIS-C decreased over time.

Real-world Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents.

Background: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. Objective: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. Design: Comparative effectiveness research accounting for underreported vaccination in three study cohorts: adolescents (12 to 20 years) during the Delta phase, children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. Setting: A national collaboration of pediatric health systems (PEDSnet). Participants: 77,392 adolescents (45,007 vaccinated) in the Delta phase, 111,539 children (50,398 vaccinated) and 56,080 adolescents (21,180 vaccinated) in the Omicron period. Exposures: First dose of the BNT162b2 vaccine vs. no receipt of COVID-19 vaccine. Measurements: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100% with confounders balanced via propensity score stratification. Results: During the Delta period, the estimated effectiveness of BNT162b2 vaccine was 98.4% (95% CI, 98.1 to 98.7) against documented infection among adolescents, with no significant waning after receipt of the first dose. An analysis of cardiac complications did not find an increased risk after vaccination. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (95% CI, 72.2 to 76.2). Higher levels of effectiveness were observed against moderate or severe COVID-19 (75.5%, 95% CI, 69.0 to 81.0) and ICU admission with COVID-19 (84.9%, 95% CI, 64.8 to 93.5). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (95% CI, 83.8 to 87.1), with 84.8% (95% CI, 77.3 to 89.9) against moderate or severe COVID-19, and 91.5% (95% CI, 69.5 to 97.6)) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined after 4 months following the first dose and then stabilized. The analysis revealed a lower risk of cardiac complications in the vaccinated group during the Omicron variant period. Limitations: Observational study design and potentially undocumented infection. Conclusions: Our study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time. Primary Funding Source: National Institutes of Health.