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Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two‐step meta‐analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one‐shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP‐B. We evaluate the performance of our method through both simulation studies and real‐world case analyses of postacute sequelae of SARS‐CoV‐2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta‐analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication‐efficient and privacy‐preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two‐step meta‐analysis methods. Overall, ODAP‐B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator. [ABSTRACT FROM AUTHOR]

Objective: Children with chronic ventilator dependence are a complex and heterogeneous population with unique needs. We sought input from parents, clinicians, and hospital staff to identify opportunities for process improvement during hospitalization and discharge preparation., Methods: We conducted a qualitative study at a large referral center for children with chronic ventilator dependence. Participants included parents of children with chronic ventilator dependence, physicians, nurses, respiratory therapists, and support staff. Two group-level assessment (GLA) sessions occurred, followed by one-on-one semistructured interviews. Thematic analysis identified areas for process improvements in the care of children with chronic ventilator dependence., Results: A total of 27 individuals participated in 2 GLA sessions followed by 9 interviews. Potential areas for process improvements included (1) acknowledging and addressing limited resources for children with long-term mechanical ventilation dependence; (2) advancing beyond a "one-size-fits-most" care model; (3) placing the patient and family at the center of care; and (4) improving support for families and staff. Families persistently noted a traumatic component of their experiences through hospitalizations and learning to care for a child with chronic ventilator dependence., Conclusions: There are multiple opportunities for process improvement during the hospitalization of children with chronic ventilator dependence. Protocols and practices that support efficient and safe care, such as education before discharge, may require modifications to better meet family needs, address system shortcomings, and mitigate trauma., (Copyright © 2025 by the American Academy of Pediatrics.)

Importance: Obesity is associated with increased severity of COVID-19. Whether obesity is associated with an increased risk of post-acute sequelae of SARS-CoV-2 infection (PASC) among pediatric populations, independent of its association with acute infection severity, is unclear., Objective: To quantify the association of body mass index (BMI) status before SARS-CoV-2 infection with pediatric PASC risk, controlling for acute infection severity., Design, Setting, and Participants: This retrospective cohort study occurred at 26 US children's hospitals from March 2020 to May 2023 with a minimum follow-up of 179 days. Eligible participants included children and young adults aged 5 to 20 years with SARS-CoV-2 infection. Data analysis was conducted from October 2023 to January 2024., Exposures: BMI status assessed within 18 months before infection; the measure closest to the index date was selected. The BMI categories included healthy weight (≥5th to <85th percentile for those aged 5-19 years or ≥18.5 to <25 for those aged >19 years), overweight (≥85th to <95th percentile for those aged 5-19 years or ≥25 to <30 for for those aged >19 years), obesity (≥95th percentile to <120% of the 95th percentile for for those aged 5-19 years or ≥30 to <40 for those aged >19 years), and severe obesity (≥120% of the 95th percentile for those aged 5-19 years or ≥40 for those aged >19 years)., Main Outcomes and Measures: To identify PASC, a diagnostic code specific for post-COVID-19 conditions was used and a second approach used clusters of symptoms and conditions that constitute the PASC phenotype. Relative risk (RR) for the association of BMI with PASC was quantified by Poisson regression models, adjusting for sociodemographic, acute COVID severity, and other clinical factors., Results: A total of 172 136 participants (mean [SD] age at BMI assessment 12.6 [4.4] years; mean [SD] age at cohort entry, 13.1 [4.4] years; 90 187 female [52.4%]) were included. Compared with participants with healthy weight, those with obesity had a 25.4% increased risk of PASC (RR, 1.25; 95% CI, 1.06-1.48) and those with severe obesity had a 42.1% increased risk of PASC (RR, 1.42; 95% CI, 1.25-1.61) when identified using the diagnostic code. Compared with those with healthy weight, there was an increased risk for any occurrences of PASC symptoms and conditions among those with obesity (RR, 1.11; 95% CI, 1.06-1.15) and severe obesity (RR, 1.17; 95% CI, 1.14-1.21), and the association held when assessing total incident occurrences among those with overweight (RR, 1.05; 95% CI, 1.00-1.11), obesity (RR, 1.13; 95% CI, 1.09-1.19), and severe obesity (RR, 1.18; 95% CI, 1.14-1.22)., Conclusions and Relevance: In this cohort study, elevated BMI was associated with a significantly increased PASC risk in a dose-dependent manner, highlighting the need for targeted care to prevent chronic conditions in at-risk children and young adults.

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., Competing Interests: Author Conflict of Interest Disclosures: Dr. Mejias reports funding from Janssen, Merck for research support, and Janssen, Merck and Sanofi-Pasteur for Advisory Board participation; Dr. Rao reports prior grant support from GSK and Biofire and is a consultant for Sequiris. Dr. Jhaveri is a consultant for AstraZeneca, Seqirus and Dynavax, and receives an editorial stipend from Elsevier. All other authors have no conflicts of interest to disclose.

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., Competing Interests: Declaration of interests Dr. Mejias reports funding from Janssen, Merck for research support, and Janssen, Merck and Sanofi-Pasteur for Advisory Board participation; Dr. Rao reports prior grant support from GSK and Biofire. Dr. Chen receives consulting support from GSK. Dr. Jhaveri is a consultant for AstraZeneca, Seqirus and Dynavax, and receives an editorial stipend from Elsevier. All other authors have no conflicts of interest to disclose.

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. Conditional logistic regression was used to estimate VE against long COVID with matching on age group (5-11, 12-17) and time period and adjustment for sex, ethnicity, health system, comorbidity burden, and pre-exposure health care utilization. We examined both probable (symptom-based) and diagnosed long COVID in the year following vaccination., Results: The vaccination rate was 56% in the cohort of 1,037,936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, while diagnosed long COVID was 0.7%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5 - 44.5) against probable long COVID and 41.7% (15.0 - 60.0) against diagnosed long COVID. VE was higher for adolescents 50.3% [36.3 - 61.0]) than children aged 5-11 (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 - 37.0%) at 18-months., Discussion: This large retrospective study shows a moderate protective effect of SARS-CoV-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 EHR sources and prospective data., Article Summary: Vaccination against COVID-19 has a protective effect against long COVID in children and adolescents. The effect wanes over time but remains significant at 12 months., What’s Known on This Subject: Vaccines reduce the risk and severity of COVID-19 in children. There is evidence for reduced long COVID risk in adults who are vaccinated, but little information about similar effects for children and adolescents, who have distinct forms of long COVID., What This Study Adds: Using electronic health records from US health systems, we examined large cohorts of vaccinated and unvaccinated patients <18 years old and show that vaccination against COVID-19 is associated with reduced risk of long COVID for at least 12 months., Contributors’ Statement: Drs. Hanieh Razzaghi and Charles Bailey conceptualized and designed the study, supervised analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript.Drs. Christopher Forrest and Yong Chen designed the study and critically reviewed and revised the manuscript.Ms. Kathryn Hirabayashi, Ms. Andrea Allen, and Dr. Qiong Wu conducted analyses, and critically reviewed and revised the manuscript.Drs. Suchitra Rao, H Timothy Bunnell, Elizabeth A. Chrischilles, Lindsay G. Cowell, Mollie R. Cummins, David A. Hanauer, Benjamin D. Horne, Carol R. Horowitz, Ravi Jhaveri, Susan Kim, Aaron Mishkin, Jennifer A. Muszynski, Susanna Nagie, Nathan M. Pajor, Anuradha Paranjape, Hayden T. Schwenk, Marion R. Sills, Yacob G. Tedla, David A. Williams, and Ms. Miranda Higginbotham critically reviewed and revised the manuscript.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work., Authorship Statement: Authorship has been determined according to ICMJE recommendations.

Objective: This study was conducted to identify rates of pediatric nirmatrelvir/ritonavir (Paxlovid) prescriptions overall and by patient characteristics., Methods: Patients up to 23 years old with a clinical encounter and a nirmatrelvir/ritonavir (Paxlovid, n/r) prescription in a PEDSnet-affiliated institution between December 1, 2021 and September 14, 2022 were identified using electronic health record (EHR) data., Results: Of the 1,496,621 patients with clinical encounters during the study period, 920 received a nirmatrelvir/ritonavir prescription (mean age 17.2 years; SD 2.76 years). 40% (367/920) of prescriptions were provided to individuals aged 18-23, and 91% (838/920) of prescriptions occurred after April 1, 2022. The majority of patients (70%; 648/920) had received at least one COVID-19 vaccine dose at least 28 days before nirmatrelvir/ritonavir prescription. Only 40% (371/920) of individuals had documented COVID-19 within the 0 to 6 days prior to receiving a nirmatrelvir/ritonavir prescription. 53% (485/920) had no documented COVID-19 infection in the EHR. Among nirmatrelvir/ritonavir prescription recipients, 64% (586/920) had chronic or complex chronic disease and 9% (80/920) had malignant disease. 38/920 (4.5%) were hospitalized within 30 days of receiving nirmatrelvir/ritonavir., Conclusion: Clinicians prescribe nirmatrelvir/ritonavir infrequently to children. While individuals receiving nirmatrelvir/ritonavir generally have significant chronic disease burden, a majority are receiving nirmatrelvir/ritonavir prescriptions without an EHR-recorded COVID-19 positive test or diagnosis. Development and implementation of concerted pediatric nirmatrelvir/ritonavir prescribing workflows can help better capture COVID-19 presentation, response, and adverse events at the population level.

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. We included demographics, symptoms, conditions, laboratory values, medications and outcomes (ICU admission, death), and grouped variables into eight categories according to organ system involvement. We used a heterogeneity-adaptive latent class analysis model to identify three clinically-relevant subphenotypes. We further characterized the sociodemographic and clinical characteristics of each subphenotype, and evaluated their temporal patterns., Findings: We identified 1186 children hospitalized with MIS-C. The highest proportion of children (44·4%) were aged between 5-11 years, with a male predominance (61.0%), and non- Hispanic white ethnicity (40·2%). Most (67·8%) children did not have a chronic condition. Class 1 represented children with a severe clinical phenotype, with 72·5% admitted to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 represented a moderate presentation, with 4-6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 represented a mild presentation, with fewer organ systems involved, lower CRP, troponin values and less cardiac involvement. Class 1 initially represented 51·1% of children early in the pandemic, which decreased to 33·9% from the pre-delta period to the omicron period., Interpretation: MIS-C has a spectrum of clinical severity, with degree of laboratory abnormalities rather than the number of organ systems involved providing more useful indicators of severity. The proportion of severe/critical MIS-C decreased over time., Research in Context: Evidence before this study: We searched PubMed and preprint articles from December 2019, to July 2022, for studies published in English that investigated the clinical subphenotypes of MIS-C using the terms "multi-system inflammatory syndrome in children" or "pediatric inflammatory multisystem syndrome" and "phenotypes". Most previous research described the symptoms, clinical characteristics and risk factors associated with MIS-C and how these differ from acute COVID-19, Kawasaki Disease and Toxic Shock Syndrome. One single-center study of 63 patients conducted in 2020 divided patients into Kawasaki and non-Kawasaki disease subphenotypes. Another CDC study evaluated 3 subclasses of MIS-C in 570 children, with one class representing the highest number of organ systems, a second class with predominant respiratory system involvement, and a third class with features overlapping with Kawasaki Disease. However, this study evaluated cases from March to July 2020, during the early phase of the pandemic when misclassification of cases as Kawasaki disease or acute COVID-19 may have occurred. Therefore, it is not known from the existing literature whether the presentation of MIS-C has changed with newer variants such as delta and omicron. Added value of this study: PEDSnet provides one of the largest MIS-C cohorts described so far, providing sufficient power for detailed analyses on MIS-C subphenotypes. Our analyses span the entire length of the pandemic, including the more recent omicron wave, and provide an update on the presentations of MIS-C and its temporal dynamics. We found that children have a spectrum of illness that can be characterized as mild (lower inflammatory markers, fewer organ systems involved), moderate (4-6 organ involvement with clinical overlap with acute COVID-19) and severe (higher inflammatory markers, critically ill, more likely to have cardiac involvement, with hypotension/shock and need for vasopressors). Implications of all the available evidence: These results provide an update to the subphenotypes of MIS-C including the more recent delta and omicron periods and aid in the understanding of the various presentations of MIS-C. These and other findings provide a useful framework for clinicians in the recognition of MIS-C, identify factors associated with children at risk for increased severity, including the importance of laboratory parameters, for risk stratification, and to facilitate early evaluation, diagnosis and treatment.

Importance: The post-acute sequelae of SARS-CoV-2 (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: Retrospective cohort study using electronic health records from 9 US children's hospitals for individuals <21 years-old who underwent reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 between March 1, 2020 - October 31, 2021 and had at least 1 encounter in the 3 years before testing., Exposure: SARS-CoV-2 PCR positivity., Main Outcomes and Measures: We identified syndromic (symptoms), systemic (conditions), and medication PASC features in the 28-179 days following the initial test date. Adjusted hazard ratios (aHRs) were obtained for 151 clinically predicted PASC features by contrasting PCR-positive with PCR-negative groups using proportional hazards models, adjusting for site, age, sex, testing location, race/ethnicity, and time-period of cohort entrance. We estimated the incidence proportion for any syndromic, systemic or medication PASC feature in the two groups to obtain a burden of PASC estimate., Results: Among 659,286 children in the study sample, 59,893 (9.1%) tested positive by PCR for SARS-CoV-2. Most were tested in outpatient testing facility (50.3%) or office (24.6%) settings. The most common syndromic, systemic, and medication features were loss of taste or smell (aHR 1.96 [95% CI 1.16-3.32), myocarditis (aHR 3.10 [95% CI 1.94-4.96]), and cough and cold preparations (aHR 1.52 [95% CI 1.18-1.96]). The incidence of at least one systemic/syndromic/medication feature of PASC was 41.9% among PCR-positive children versus 38.2% among PCR-negative children, with an incidence proportion difference of 3.7% (95% CI 3.2-4.2%). A higher strength of association for PASC was identified in those cared for in the ICU during the acute illness phase, children less than 5 years-old, and individuals with complex chronic conditions., Conclusions and Relevance: In this large-scale, exploratory study, the burden of pediatric PASC that presented to health systems was low. Myocarditis was the most commonly diagnosed PASC-associated condition. Acute illness severity, young age, and comorbid complex chronic disease increased the risk of PASC., Key Points: Question: What are the incidence and clinical features of post-acute sequelae of SARS-CoV-2 infection (PASC) in children? Findings: In this retrospective cohort study of 659,286 children tested for SARS-CoV-2 by polymerase chain reaction (PCR), the symptom, condition and medication with the strongest associations with SARS-CoV-2 infection were loss of taste/smell, myocarditis, and cough and cold preparations. The incidence proportion of non-MIS-C related PASC in the PCR-positive group exceeded the PCR-negative group by 3.7% (95% CI 3.2-4.2), with increased rates associated with acute illness severity, young age, and medical complexity. Meaning: PASC in children appears to be uncommon, with features that differ from adults.