Real-world Effectiveness and Causal Mediation Study of BNT162b2 on Long COVID Risks in Children and Adolescents (preprint)

BackgroundThe impact of pre-infection vaccination on the risk of long COVID remains unclear in the pediatric population. Further, it is unknown if such pre-infection vaccination can mitigate the risk of long COVID beyond its established protective benefits against SARS-CoV-2 infection. ObjectiveTo assess the effectiveness of BNT162b2 on long COVID risks with various strains of the SARS-CoV-2 virus in children and adolescents, using comparative effectiveness methods. To disentangle the overall effectiveness of the vaccine on long COVID outcomes into its independent impact and indirect impact via prevention of SARS-CoV-2 infections, using causal mediation analysis. DesignReal-world vaccine effectiveness study and mediation analysis in three independent 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. SettingTwenty health systems in the RECOVER PCORnet electronic health record (EHR) Program. Participants112,590 adolescents (88,811 vaccinated) in the Delta period, 188,894 children (101,277 vaccinated), and 84,735 adolescents (37,724 vaccinated) in the Omicron period. ExposuresFirst dose of the BNT162b2 vaccine vs. no receipt of COVID-19 vaccine. MeasurementsOutcomes of interest include conclusive or probable diagnosis of long COVID following a documented SARS-CoV-2 infection, and body-system-specific condition clusters of post-acute sequelae of SARS-CoV-2 infection (PASC), such as cardiac, gastrointestinal, musculoskeletal, respiratory, and syndromic categories. The effectiveness was reported as (1-relative risk)*100 and mediating effects were reported as relative risks. ResultsDuring the Delta period, the estimated effectiveness of the BNT162b2 vaccine against long COVID among adolescents was 95.4% (95% CI: 90.9% to 97.7%). During the Omicron phase, the estimated effectiveness against long COVID among children was 60.2% (95% CI: 40.3% to 73.5%) and 75.1% (95% CI: 50.4% to 87.5%) among adolescents. The direct effect of vaccination, defined as the effect beyond their impact on SARS-CoV-2 infections, was found to be statistically non-significant in all three study cohorts, with estimates of 1.08 (95% CI: 0.75 to 1.55) in the Delta study among adolescents, 1.24 (95% CI: 0.92 to 1.66) among children and 0.91 (95% CI: 0.69 to 1.19) among adolescents in the Omicron studies. Meanwhile, the estimated indirect effects, which are effects through protecting SARS-CoV-2 infections, were estimated as 0.04 (95% CI: 0.03 to 0.05) among adolescents during Delta phase, 0.31 (95% CI: 0.23 to 0.42) among children and 0.21 (95% CI: 0.16 to 0.27) among adolescents during the Omicron period. LimitationsObservational study design and potentially undocumented infection. ConclusionsOur study suggests that BNT162b2 was effective in reducing risk of long COVID outcomes in children and adolescents during the Delta and Omicron periods. The mediation analysis indicates the vaccines effectiveness is primarily derived from its role in reducing the risk of SARS-CoV-2 infection. Primary Funding SourceNational Institutes of Health.

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

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. childrens 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.

Vaccine Effectiveness Against Long COVID in Children: A Report from the RECOVER EHR Cohort (preprint)

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 to 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 to 11, 12 to 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 probably 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. 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.

Real-world Effectiveness of BNT162b2 in Children and Adolescents in Preventing Infection and Severe Diseases with SARS-CoV-2 During the Delta and Omicron Periods: Findings from Trial Emulation Using an EHR-based Cohort (preprint)

BACKGROUND The current understanding of the long-term effectiveness of the BNT162b2 vaccine for a range of outcomes across diverse U.S. pediatric populations is limited. In this study, we assessed the effectiveness of BNT162b2 against various strains of the SARS-CoV-2 virus using data from a national collaboration of pediatric health systems (PEDSnet). METHODS We emulated three target trials to assess the real-world effectiveness of BNT162b2: adolescents aged 12 to 20 years during the Delta variant period (Target trial 1), children aged 5 to 11 years (Target trial 2) and adolescents aged 12 to 20 years during the Omicron variant period (Target trial 3). The outcomes included documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and two cardiac-related outcomes, myocarditis and pericarditis. In the U.S., immunization records are often captured and stored across multiple disconnected sources, resulting in incomplete vaccination records in patients' electronic health records (EHR). We implemented a novel trial emulation pipeline accounting for possible misclassification bias in vaccine documentation in EHRs. The effectiveness of the BNT162b2 vaccine was estimated from the Poisson regression model with confounders balanced via propensity score stratification. RESULTS During the Delta period, the BNT162b2 vaccine demonstrated an overall effectiveness 98.4% (95% CI, 98.1 to 98.7) against documented infection among adolescents, with no significant waning after receipt of the first dose. During the Omicron period, the overall effectiveness was estimated to be 74.3% (95% CI, 72.2 to 76.2) in preventing documented infection among children, which was higher 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). In the adolescent population, the overall effectiveness against documented Omicron infection was 85.5% (95% CI, 83.8 to 87.1), with effectiveness of 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 with higher levels of uncertainty. Across all three cohorts, the risk of cardiac outcomes was approximately 65% to 85% lower in the vaccinated group than that of the unvaccinated group accounting for possible misclassification bias. CONCLUSIONS This study suggests BNT162b2 was effective among children and adolescents in Delta and Omicron periods for a range of COVID-19-related outcomes and is associated with a lower risk for cardiac complications. Waning effectiveness over time suggests that revaccination may be needed in the future.

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

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. 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. Conclusions 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 (preprint)

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 signals associated with PASC. Materials and Methods We used a propensity-matched cohort design comparing children identified using the new PASC ICD10CM diagnosis code (U09.9) (N=1250) to children with (N=6250) and without (N=6250) 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 pre-specified 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.

Impact of SARS-CoV-2 infection on disease trajectory in youth with T1D: An EHR-based cohort study from the RECOVER program (preprint)

Background: Post-acute sequelae of SARS-Co-V-2 infection (PASC) is associated with worsening diabetes trajectory. It is unknown whether PASC in children with type 1 diabetes (T1D) manifests as worsening diabetes trajectory. Objective: To explore the association between SARS-CoV-2 infection (COVID-19) and T1D-related healthcare utilization (for diabetic ketoacidosis [DKA] or severe hypoglycemia [SH]) or Hemoglobin (Hb) A1c trajectory. Methods: We included children <21 years with T1D and [≥]1 HbA1c prior to cohort entry, which was defined as COVID-19 (positive diagnostic test or diagnosis code for COVID-19, multisystem inflammatory syndrome in children, or PASC) or a randomly selected negative test for those who were negative throughout the study period (Broad Cohort). A subset with [≥]1 HbA1c value from 28-275 days after cohort entry (Narrow Cohort) was included in the trajectory analysis. Propensity score-based matched cohort design followed by weighted Cox regression was used to evaluate the association of COVID-19 with healthcare utilization >28 days after cohort entry. Generalized estimating equation models were used to measure change in HbA1c in the Narrow cohort. Results: From 03/01/2020-06/22/2022, 2,404 and 1,221 youth met entry criteria for the Broad and Narrow cohorts, respectively. The hazard ratio for utilization was (HR 1.45 [95%CI,0.97,2.16]). In the Narrow Cohort, the rate of change (slope) of HbA1c increased 91-180 days after cohort entry for those with COVID-19 (0.138 vs. -0.002, p=0.172). Beyond 180 days, greater declines in HbA1c were observed in the positive cohort (-0.104 vs. 0.008 per month, p=0.024). Conclusion: While a trend towards worse outcomes following COVID-19 in T1D patients was observed, these findings were not statistically significant. Continued clinical monitoring of youth with T1D following COVID-19 is warranted.

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

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.

Multisystem Inflammatory Syndrome in Children Managed in the Outpatient Setting: An EHR-based cohort study from the RECOVER program (preprint)

Using electronic health record data combined with primary chart review, we identified 7 children across 8 pediatric medical centers with a diagnosis of Multisystem Inflammatory Syndrome in Children (MIS-C) who were managed as outpatients. These findings should prompt a discussion about modifying the case definition to allow for such a possibility.

Healthcare utilization following SARS-CoV-2 infection in children and adolescents with chronic conditions: An EHR-based Cohort Study from the RECOVER Program (preprint)

Background Chronic medical conditions are a risk factor for moderate or severe COVID-19 in children, but little is known about post-acute sequelae of SARS-CoV-2 infection (PASC) in children with chronic medical conditions (CMCs). To understand whether SARS-CoV-2 infection led to potential exacerbation of underlying chronic disease in children, we explored whether children with CMCs had increased healthcare utilization in the post-acute (28 days after infection) period compared to children with CMCs without SARS-CoV-2 infection. Methods We conducted a retrospective, matched-cohort study using electronic health record data collected from 8 pediatric health care systems participating in the PEDSnet network. We included children <21 years of age with a wide array of chronic conditions, defined by the presence of diagnostic codes, who were diagnosed with COVID-19 between March 1, 2020 and February 28, 2022. Cohort entry was defined by presence of a positive SARS-CoV-2 PCR test (polymerase chain reaction or antigen) or diagnostic codes for COVID-19, PASC or MIS-C. A comparison cohort of patients testing negative or without these conditions was matched using a stratified propensity score model and exact matching on age group, race/ethnicity, institution, test location, and month of cohort entry. A negative binomial model was used to examine our primary outcome: composite and setting-specific (inpatient, outpatient, ED) utilization rate ratios between the positive and comparison cohorts. Secondary outcomes included time to first utilization in the post-acute period, and utilization stratified by severity at cohort entry. Results We identified 748,692 patients with at least one chronic condition, 78,744 of whom met inclusion criteria for the COVID-19 cohort. 96% of patients from the positive cohort were matched. Cohorts were well-balanced for chronic condition clusters, total number of conditions, time since first diagnosis, baseline utilization, cohort entry period, age, sex, race/ethnicity and test location. We found that among children with chronic medical conditions, those with COVID-19 had higher healthcare utilization than those with no recorded COVID-19 diagnosis or positive test, with utilization rate ratio of 1.21 (95% CI: 1.18-1.24). The utilization was highest for inpatient care with utilization rate ratio of 2.03 (95% CI: 1.85-2.23) but the utilization was increased across all settings. Hazard ratios estimated in time-to-first-utilization analysis mirrored these results. Patients with severe or moderate acute COVID-19 illness had greater increases in utilization in all settings than those with mild or asymptomatic disease. Conclusions We found that care utilization in all settings was increased following COVID-19 in children with chronic medical conditions in the post-acute period, particularly in the inpatient setting. Increased utilization was correlated with more severe COVID-19. Additional research is needed to better understand the reasons for higher care utilization by studying condition-specific outcomes in children with chronic disease.

Leveraging serology testing to identify children at risk for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the RECOVER program (preprint)

ABSTRACT The impact of post-acute sequelae of SARS-CoV-2 infection (PASC) in children is underrecognized. We developed an EHR-based algorithm across eight pediatric institutions to identify children with COVID-19 based on serology testing from 3/2020 through 4/2022 who had not been identified by PCR. Overall, serology tests were used 100-fold less than PCR. Seroprevalence of IgG anti-nucleocapsid antibodies remained stable, while rates of positive IgG anti-spike antibodies increased in teenagers after COVID-19 vaccine approval. Through data harmonization and after excluding 1,410 serology test results that may have been influenced by vaccines, we identified 2,714 children that were COVID-19 positive exclusively by serology. These patients were frequently tested as inpatients (24% vs. 2%), had chronic conditions more frequently (37% vs 24%), and a MIS-C diagnosis (23% vs. <1%) compared with PCR-positive children. Identification of children that could have been paucisymptomatic, not tested, or missed is critical to define the burden of PASC in children.

Clinical features and burden of post-acute sequelae of SARS-CoV-2 infection in children and adolescents: an exploratory EHR-based cohort study from the RECOVER program (preprint)

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.