key: cord-0878253-pfz5z9x9
authors: Martin, Blake; DeWitt, Peter E.; Russell, Seth; Anand, Adit; Bradwell, Katie R.; Bremer, Carolyn; Gabriel, Davera; Girvin, Andrew T.; Hajagos, Janos G.; McMurry, Julie A.; Neumann, Andrew J.; Pfaff, Emily R.; Walden, Anita; Wooldridge, Jacob T.; Yoo, Yun Jae; Saltz, Joel; Gersing, Ken R.; Chute, Christopher G.; Haendel, Melissa A.; Moffitt, Richard; Bennett, Tellen D.
title: Characteristics, Outcomes, and Severity Risk Factors Associated With SARS-CoV-2 Infection Among Children in the US National COVID Cohort Collaborative
date: 2022-02-08
journal: JAMA Netw Open
DOI: 10.1001/jamanetworkopen.2021.43151
sha: ac12733fc23c7edfdc56a4826d1bac51d66d96c4
doc_id: 878253
cord_uid: pfz5z9x9

IMPORTANCE: Understanding of SARS-CoV-2 infection in US children has been limited by the lack of large, multicenter studies with granular data. OBJECTIVE: To examine the characteristics, changes over time, outcomes, and severity risk factors of children with SARS-CoV-2 within the National COVID Cohort Collaborative (N3C). DESIGN, SETTING, AND PARTICIPANTS: A prospective cohort study of encounters with end dates before September 24, 2021, was conducted at 56 N3C facilities throughout the US. Participants included children younger than 19 years at initial SARS-CoV-2 testing. MAIN OUTCOMES AND MEASURES: Case incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs multisystem inflammatory syndrome in children (MIS-C), and Delta vs pre-Delta variant differences for children with SARS-CoV-2. RESULTS: A total of 1 068 410 children were tested for SARS-CoV-2 and 167 262 test results (15.6%) were positive (82 882 [49.6%] girls; median age, 11.9 [IQR, 6.0-16.1] years). Among the 10 245 children (6.1%) who were hospitalized, 1423 (13.9%) met the criteria for severe disease: mechanical ventilation (796 [7.8%]), vasopressor-inotropic support (868 [8.5%]), extracorporeal membrane oxygenation (42 [0.4%]), or death (131 [1.3%]). Male sex (odds ratio [OR], 1.37; 95% CI, 1.21-1.56), Black/African American race (OR, 1.25; 95% CI, 1.06-1.47), obesity (OR, 1.19; 95% CI, 1.01-1.41), and several pediatric complex chronic condition (PCCC) subcategories were associated with higher severity disease. Vital signs and many laboratory test values from the day of admission were predictive of peak disease severity. Variables associated with increased odds for MIS-C vs acute COVID-19 included male sex (OR, 1.59; 95% CI, 1.33-1.90), Black/African American race (OR, 1.44; 95% CI, 1.17-1.77), younger than 12 years (OR, 1.81; 95% CI, 1.51-2.18), obesity (OR, 1.76; 95% CI, 1.40-2.22), and not having a pediatric complex chronic condition (OR, 0.72; 95% CI, 0.65-0.80). The children with MIS-C had a more inflammatory laboratory profile and severe clinical phenotype, with higher rates of invasive ventilation (117 of 707 [16.5%] vs 514 of 8241 [6.2%]; P < .001) and need for vasoactive-inotropic support (191 of 707 [27.0%] vs 426 of 8241 [5.2%]; P < .001) compared with those who had acute COVID-19. Comparing children during the Delta vs pre-Delta eras, there was no significant change in hospitalization rate (1738 [6.0%] vs 8507 [6.2%]; P = .18) and lower odds for severe disease (179 [10.3%] vs 1242 [14.6%]) (decreased by a factor of 0.67; 95% CI, 0.57-0.79; P < .001). CONCLUSIONS AND RELEVANCE: In this cohort study of US children with SARS-CoV-2, there were observed differences in demographic characteristics, preexisting comorbidities, and initial vital sign and laboratory values between severity subgroups. Taken together, these results suggest that early identification of children likely to progress to severe disease could be achieved using readily available data elements from the day of admission. Further work is needed to translate this knowledge into improved outcomes.

Participants: All children <19-years-old at the time of their first SARS-CoV-2 test within the N3C with an encounter end date prior to 9/24/2021.

• Date/time of viral testing • Geographic location at the time of viral testing by United States Census Bureau geographic subregion • Demographics: EHR-documented race, ethnicity, biological sex, and age (at encounter start) • Preselected comorbidities: asthma*, diabetes mellitus*, obesity † , and pediatric complex chronic condition (PCCC, as defined by Chris Feudtner et al 6 and determined via adaptation of our R implementation 7 using available diagnosis and procedure codes) • Medication administration for each patient with matching day of hospitalization (day 0 = day of admission) • Select patient vital sign and laboratory values documented during index encounter hospitalization *Asthma and diabetes mellitus diagnoses were identified via application of validated, pre-existing concept sets within the N3C enclave. † Presence of obesity is determined only for those children ≥2-years-old with a BMI measurement available, and obesity is defined as those with a BMI ≥95 th percentile for age and sex per CDC criteria 13 ).

-Severe versus moderate maximum CPS clinical severity category -MIS-C versus acute COVID-19 patient class -Severe versus moderate maximum CPS clinical severity category by variant era (Delta era versus pre-Delta era) o Delta era is defined as all encounters with a start date on or after 6/26/2021, the approximate date at which Delta variant strains exceeded 50% of all sampled strains over a two week period 14 Data Sources / Measurement: 56 data partners from the 9/24/2021 release of the N3C dataset. See Haendel et al. 3 for details on data ingestion, common data model mapping, quality assurance, and harmonization.

To assess for the introduction of bias by contributing site, we performed a sensitivity analysis to evaluate the impact of the data source (that is, the contributing healthcare site) on the strength of association between each variable and patient maximum clinical severity. For each of the above listed outcome variables, a sensitivity analysis was performed to identify changes in the strength of association between a given predictor and the outcome of interest (see "Statistical Methods" section below).

Study Size:

The study size was determined by the number of index patient encounters (see above) identified at each contributing data partner during the study period.

Quantitative Variables: Quantitative variables such as patient age and BMI are reported as medians with associated interquartile range (IQR). Patients are grouped based on index encounter maximum clinical severity (e.g. mild, mild ED, moderate, or severe) to aid in identification of variables associated with higher peak clinical severity. For vital sign measurements obtained during hospitalization, each vital sign is reported using the mean for that day (e.g. day 0) over all patients in a given maximum clinical severity category with its corresponding 95% confidence interval. Similarly, laboratory test values are reported using the mean for each lab on a given day of hospitalization over all patients with the corresponding 95% confidence interval and grouped by maximum clinical severity category. When vital signs and laboratory test values are plotted by day of hospitalization, the median is shown along with the associated IQR for that day.

Statistical Methods As noted above, quantitative variables are reported as medians with corresponding interquartile ranges. Categorical variables (e.g. ethnicity, race, or presence of a particular PCCC category) are reported as the number of patients and associated percentage, stratified by maximum clinical severity category. We used chi square deviance tests to evaluate for statistically significant changes in patient severity over time (specifically the odds of moderate or severe disease) and statistically significant change in the relative proportions of preselected age groups over time.

Proportions of children in each maximum clinical severity category receiving a given medication or medication class (e.g. corticosteroid) were compared using Pearson's chi-squared test statistic. To report on the percent of hospitalized patients receiving a given medication or medication class, patients were grouped by study quarter (three month blocks), by index encounter start date, to allow for some granularity while ensuring that each datapoint represented a patient group of at least 20 patients (as reporting of results for groups comprised of <20 patients is not allowed per N3C policy). Separate lines for pediatric and adult classes were plotted to highlight changes in medication use over time during the study period within these two groups.

Multivariable logistic regression (MLR) was used to identify which predictors among a set of preselected variables were associated with an increased odds of severe (versus moderate) maximum clinical severity. Preselected variables included biologic sex, age, ethnicity, race, asthma, obesity, and PCCC categories. Diabetes mellitus (both type I and type II) was initially selected as a predictor for evaluation; however, the low number of hospitalized children with diabetes precluded evaluation in an MLR model. To evaluate age as a risk factor, outcomes of children under 12-years-old were compared to those of children 12-years-old and over. To evaluate ethnicity as a risk factor for severe disease, Hispanic / Latino patient outcomes were compared with non-Hispanic / non-Latino patient outcomes. Similarly, to evaluate race as a risk factor for severe disease, Black / African American and non-Black, non-White severity outcomes were each compared to those of White children. To evaluate the impact of obesity, an MLR for severe (versus moderate) disease was created using only those children ≥2-years-old with a BMI measurement available.

This same MLR approach was used to identify predictors associated with increased odds of receiving an MIS-C diagnosis (versus acute COVID-19) and to identify changes in odds of severe (versus moderate) disease in the Delta era as compared to the pre-Delta era (both overall and for individual predictors). Pre-Delta era and Delta era odds ratios (and 95% CIs) for severe disease for each predictor were calculated along with the change in odds (and corresponding 95% CI) for the change. For each MLR, a separate sensitivity analysis was performed in which we used generalized estimating equations (GEEs) to account for the impact of patient healthcare site on the odds ratio for severe disease for each predictor. All GEEs used an exchangeable working correlation structure.

Mean values for a given vital sign or laboratory test value on a given day of hospitalization (e.g. day 0 or day 7) were compared using GEEs with an exchangeable working correlation structure. We assessed for significance in change over time for each vital sign or lab result during hospitalization using a linear model to average over the first week of values. We then used the model results to estimate the difference between hospital day 0 and day 7 for each severity subgroup. Lastly, proportions of cases requiring hospitalization in the pre-Delta and Delta eras were compared using chi-square tests. Proportion of children in the moderate (N = 8,822) and severe (N = 1,423) disease subgroups alongside the number and percent of adults in the moderate (N = 136,055) and severe (N = 41,457) disease subgroups who had at least one value for a given lab test available during their inpatient hospital encounter. eTable 7: MIS-C versus acute COVID-19 characteristics, outcomes, and risk factors.

Acute COVID-19 (N = 8,241), n(%) The number and percent of children in each era (Delta, defined as patients with a visit start date on/after 6/26/2021, and pre-Delta, defined as those with a start date before 6/26/2021) with a given demographic characteristic, preexisting comorbidity, or clinical outcome. Also shown is the change in the odds ratios (ORs) for severe (as compared to moderate) disease observed during the Delta era as compared with the pre-Delta era. Similarly, the change in adjusted odds ratio (aOR) (correcting for health care site), is also shown. *Given the low number of children with diabetes mellitus, calculation of OR's for this potential risk factor was not feasible † Odds ratio listed for sex is the odds of a male patient developing severe disease compared to a female patient; the odds ratio for age is the odds of a patient <12-years-old getting severe disease compared to a patient ≥12-years-old;

the odds ratio for ethnicity is the odds of a Hispanic patient getting severe disease vs. a non-Hispanic patient (either not Hispanic or unknown) ‡ Odds ratio for a Black / African American child developing severe disease as compared to a White child ± Odds ratio for a non-Black, non-White child developing severe disease as compared to a White child ↡ BMI calculated as per the Centers for Disease Control and Prevention (CDC) guidelines 13 with obesity defined as any child ≥ 2-years-old with a BMI ≥ 95th percentile for age and sex. The percent of children with obesity was calculated by dividing the number of children ≥2-years-old who had a BMI for age and sex ≥95th percentile in that subgroup, by the number of children in that subgroup who were ≥2 years old and had a BMI measurement available.

The N3C Phenotype & Data Acquisition Workstream

Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment

Common Data Model. Observational Health Data Sciences and Informatics

WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection: A minimal common outcome measure set for COVID-19 clinical research

Pediatric deaths attributable to complex chronic conditions: a population-based study of Washington State

R package for pediatric complex chronic condition classification

Scaling r programs with spark

PySpark Documentation

Apache spark: a unified engine for big data processing

Package 'ggplot2'. Create Elegant Data Visualisations Using the Grammar of Graphics Version

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies

Centers for Disease Control and Prevention. Defining Childhood Obesity