Objective COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers. Methods The Clinical and Translational Science Award (CTSA) Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics. Organized in inclusive workstreams, in two months we created: legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access. Discussion The N3C has demonstrated that a multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19. LAY SUMMARY COVID-19 poses societal challenges that require expeditious data and knowledge sharing. Though medical records are abundant, they are largely inaccessible to outside researchers. Statistical, machine learning, and causal research are most successful with large datasets beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many clinical centers to reveal patterns in COVID-19 patients. To create N3C, the community had to overcome technical, regulatory, policy, and governance barriers to sharing patient-level clinical data. In less than 2 months, we developed solutions to acquire and harmonize data across organizations and created a secure data environment to enable transparent and reproducible collaborative research. We expect the N3C to help save lives by enabling collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care needs and thereby reduce the immediate and long-term impacts of COVID-19.

Objective Extracorporeal membrane oxygenation, an accepted rescue therapy for refractory cardiopulmonary failure, requires a complex multidisciplinary approach and advanced technology. Little is known about the relationship between a center’s case volume and patient mortality. The purpose of this study was to analyze the relationship between hospital extracorporeal membrane oxygenation annual volume and in-hospital mortality and assess if a minimum hospital volume could be recommended. Design Retrospective cohort study Setting A retrospective cohort admitted to children’s hospitals in the Pediatric Health Information System database from 2004-2011 supported with extracorporeal membrane oxygenation was identified. Indications were assigned based on patient age (neonatal vs. pediatric), diagnosis, and procedure codes. Average hospital annual volume was defined as 0-19, 20-49, or ≥50 cases per year. Maximum likelihood estimates were used to assess minimum annual case volume. Patients A total of 7322 pediatric patients aged 0-18 years of age were supported with extracorporeal membrane oxygenation and had an indication assigned. Interventions None Measurements and Main Results Average hospital extracorporeal membrane oxygenation volume ranged from 1-58 cases per year. Overall mortality was 43% but differed significantly by indication. After adjustment for case-mix, complexity of cardiac surgery, and year of treatment, patients treated at medium (OR 0.86, 95% CI 0.75-0.98) and high (OR 0.75, 95% CI 0.63-0.89) volume centers had significantly lower odds of death compared to those treated at low volume centers. The minimum annual case load most significantly associated with lower mortality was 22 (95% CI 22-28). Conclusion Pediatric centers with low extracorporeal membrane oxygenation average annual case volume had significantly higher mortality and a minimum volume of 22 cases per year was associated with improved mortality. We suggest this threshold be evaluated by additional study.

Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. As the worldwide scientific community forges ahead with efforts to characterize a wide range of outcomes associated with SARS-CoV-2 infection, the proliferation of available data has made it clear that formal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat.

BackgroundThe majority of U.S. reports of COVID-19 clinical characteristics, disease course, and treatments are from single health systems or focused on one domain. Here we report the creation of the National COVID Cohort Collaborative (N3C), a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative U.S. cohort of COVID-19 cases and controls to date. This multi-center dataset supports robust evidence-based development of predictive and diagnostic tools and informs critical care and policy.Methods and FindingsIn a retrospective cohort study of 1,926,526 patients from 34 medical centers nationwide, we stratified patients using a World Health Organization COVID-19 severity scale and demographics; we then evaluated differences between groups over time using multivariable logistic regression. We established vital signs and laboratory values among COVID-19 patients with different severities, providing the foundation for predictive analytics. The cohort included 174,568 adults with severe acute respiratory syndrome associated with SARS-CoV-2 (PCR >99% or antigen <1%) as well as 1,133,848 adult patients that served as lab-negative controls. Among 32,472 hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March/April 2020 to 8.6% in September/October 2020 (p = 0.002 monthly trend). In a multivariable logistic regression model, age, male sex, liver disease, dementia, African-American and Asian race, and obesity were independently associated with higher clinical severity. To demonstrate the utility of the N3C cohort for analytics, we used machine learning (ML) to predict clinical severity and risk factors over time. Using 64 inputs available on the first hospital day, we predicted a severe clinical course (death, discharge to hospice, invasive ventilation, or extracorporeal membrane oxygenation) using random forest and XGBoost models (AUROC 0.86 and 0.87 respectively) that were stable over time. The most powerful predictors in these models are patient age and widely available vital sign and laboratory values. The established expected trajectories for many vital signs and laboratory values among patients with different clinical severities validates observations from smaller studies, and provides comprehensive insight into COVID-19 characterization in U.S. patients.ConclusionsThis is the first description of an ongoing longitudinal observational study of patients seen in diverse clinical settings and geographical regions and is the largest COVID-19 cohort in the United States. Such data are the foundation for ML models that can be the basis for generalizable clinical decision support tools. The N3C Data Enclave is unique in providing transparent, reproducible, easily shared, versioned, and fully auditable data and analytic provenance for national-scale patient-level EHR data. The N3C is built for intensive ML analyses by academic, industry, and citizen scientists internationally. Many observational correlations can inform trial designs and care guidelines for this new disease.

In this pediatric population, with serum ferritin levels of >3000 ng/mL, there was increased risk for both receipt of critical care and subsequent death.

No abstract

IMPORTANCE Intracranial pressure (ICP) monitoring is a mainstay of therapy for children with traumatic brain injury (TBI), but its overall association with patient outcome is unclear.OBJECTIVE To test the hypothesis that ICP monitoring is associated with improved functional survival of children with severe TBI. DESIGN, SETTING, AND PARTICIPANTSA propensity-weighted effectiveness analysis was conducted using 2 linked national databases with data from 30 US children's hospitals from January 1, 2007, to December 31, 2012, on 3084 children with severe TBI. Clinical events including neurosurgical procedures were identified using validated computable phenotypes. Data analysis was conducted from September 1, 2016, to March 1, 2017.EXPOSURE Placement of an ICP monitor. MAIN OUTCOMES AND MEASURESA composite of hospital mortality, discharge to hospice, or survival with placement of new tracheostomy and gastrostomy tubes. RESULTSOf the 3084 children in the study (1128 girls and 1956 boys; mean [SD] age, 7.03 [5.44] years), 1002 (32.4%) underwent ICP monitoring, with substantial hospital variation (6% to 50% by hospital). Overall, 484 children (15.7%) experienced the primary composite outcome. A propensity approach using matching weights generated good covariate balance between those who did and those who did not undergo ICP monitoring. Using a propensity-weighted logistic regression model clustered by hospital, no statistically significant difference was found in functional survival between monitored and unmonitored patients (odds ratio of poor outcome among those who underwent ICP monitoring, 1.31; 95% CI, 0.99-1.74). In a prespecified secondary analysis, no difference in mortality was found (odds ratio, 1.16; 95% CI, 0.89-1.50). Prespecified subgroup analyses of children younger and older than 2 years of age and among those with unintentional and inflicted (intentional) injuries also showed no difference in outcome with ICP monitoring. CONCLUSIONS AND RELEVANCEWith the use of linked national data and validated computable phenotypes, no evidence was found of a benefit from ICP monitoring on functional survival of children with severe TBI. Intracranial pressure monitoring is a widely but inconsistently used technology with incompletely demonstrated effectiveness. A large prospective cohort study or randomized trial is needed.

Objectives To describe patient demographics, interventions, and outcomes in hospitalized children with macrophage activation syndrome (MAS) complicating systemic lupus erythematosus (SLE) or juvenile idiopathic arthritis (JIA). Methods Retrospective cohort study of the Pediatric Health Information System (PHIS) database, Oct 1, 2006 to September 30, 2010. Participants had ICD-9-CM diagnosis codes for MAS and either SLE or JIA. The primary outcome was hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, critical care interventions, and medication use. Results 121 children at 28 children’s hospitals met inclusion criteria, including 19 with SLE and 102 with JIA. Index admission mortality was 7% (8/121). ICU admission (33%), mechanical ventilation (26%), and inotrope/vasopressor therapy (26%) were common. Compared to children with JIA, those with SLE had similar mortality (6% versus 11%, exact p = 0.6), more ICU care (63% versus 27%, p = 0.002), more mechanical ventilation (53% versus 21%, p = 0.003), and more cardiovascular dysfunction (inotrope/vasopressor 47% versus 23%, p = 0.02). Children with SLE and JIA received cyclosporine at similar rates, but more children with SLE received cyclophosphamide and mycophenolate mofetil and more children with JIA received interleukin-1 antagonists. Conclusions Organ system dysfunction is common in children with rheumatic diseases complicated by MAS, and children with underlying SLE require more organ system support than children with JIA. Current treatment of pediatric MAS varies based on the underlying rheumatic disease.