IMPORTANCEThe National COVID Cohort Collaborative (N3C) is a centralized, harmonized, highgranularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy.OBJECTIVES To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. DESIGN, SETTING, AND PARTICIPANTSIn a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). MAIN OUTCOMES AND MEASURESPatient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. RESULTSThe cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472(18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, (continued) Key Points Question In a US data resource large enough to adjust for multiple confounders, what risk factors are associated with COVID-19 severity and severity trajectory over time, and can machine learning models predict clinical severity? Findings In this cohort study of 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized and 6565 (20.2%) were severely ill, and first-day machine learning models accurately predicted clinical severity. Mortality was 11.6%

Little is known about the processes or the relative outcomes of involving children in decisions about their own health care. The purpose of this descriptive study was to examine the involvement of school-age children and adolescents in such decisions. A secondary analysis of two data sets composed of individual interviews regarding involvement in health care decisions was completed. One sample was composed of 20 school-age children with cystic fibrosis (CF) and both parents of each; the other was composed of 8 children and adolescents who were having surgeryfor scoliosis secondary to a chronic condition, and one parent of each. Dimensional analysis was used to summarize and contrast the data according to child, adolescent, and parent perspectives across the two data sets. The type of decision, view ofthe decision, involvement in the decision-making process, and satisfaction with the decision-making process were themes common to both data sets.

The decision-making process related to a child's participation in clinical trials often involves multiple family members. The aim of this study was to compare family patterns of decision-making within and across family units in pediatric clinical trials. Participants for this secondary analysis included 14 families from a larger study of informed consent. Four distinct patterns of decision-making were identified: Exclusionary, informative, collaborative, and delegated. These patterns varied with regard to three dimensions of parents' decision-making goals, child level of involvement, and the parental role. These patterns of decision-making affect how parents and children communicate with health professionals and influence the effectiveness of health care providers interactions with the family related to the decision-making process.

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