Dataset Information

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

ABSTRACT:

Objective

Coronavirus disease 2019 (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.

Materials and methods

The Clinical and Translational Science Award 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.

Results

Organized in inclusive workstreams, 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.

Conclusions

The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational 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.

SUBMITTER: Haendel MA

PROVIDER: S-EPMC7454687 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Haendel Melissa A MA Chute Christopher G CG Bennett Tellen D TD Eichmann David A DA Guinney Justin J Kibbe Warren A WA Payne Philip R O PRO Pfaff Emily R ER Robinson Peter N PN Saltz Joel H JH Spratt Heidi H Suver Christine C Wilbanks John J Wilcox Adam B AB Williams Andrew E AE Wu Chunlei C Blacketer Clair C Bradford Robert L RL Cimino James J JJ Clark Marshall M Colmenares Evan W EW Francis Patricia A PA Gabriel Davera D Graves Alexis A Hemadri Raju R Hong Stephanie S SS Hripscak George G Jiao Dazhi D Klann Jeffrey G JG Kostka Kristin K Lee Adam M AM Lehmann Harold P HP Lingrey Lora L Miller Robert T RT Morris Michele M Murphy Shawn N SN Natarajan Karthik K Palchuk Matvey B MB Sheikh Usman U Solbrig Harold H Visweswaran Shyam S Walden Anita A Walters Kellie M KM Weber Griffin M GM Zhang Xiaohan Tanner XT Zhu Richard L RL Amor Benjamin B Girvin Andrew T AT Manna Amin A Qureshi Nabeel N Kurilla Michael G MG Michael Sam G SG Portilla Lili M LM Rutter Joni L JL Austin Christopher P CP Gersing Ken R KR

Journal of the American Medical Informatics Association : JAMIA 20210301 3

<h4>Objective</h4>Coronavirus disease 2019 (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 pati ...[more]

PMID: 32805036

Similar Datasets

Project description:ImportanceSARS-CoV-2.ObjectiveTo determine the characteristics, changes over time, outcomes, and severity risk factors of SARS-CoV-2 affected children within the National COVID Cohort Collaborative (N3C).DesignProspective cohort study of patient encounters with end dates before May 27th, 2021.Setting45 N3C institutions.ParticipantsChildren <19-years-old at initial SARS-CoV-2 testing.Main outcomes and measuresCase incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs MIS-C contrasts for children infected with SARS-CoV-2.Results728,047 children in the N3C were tested for SARS-CoV-2; of these, 91,865 (12.6%) were positive. Among the 5,213 (6%) hospitalized children, 685 (13%) met criteria for severe disease: mechanical ventilation (7%), vasopressor/inotropic support (7%), ECMO (0.6%), or death/discharge to hospice (1.1%). Male gender, African American race, older age, and several pediatric complex chronic condition (PCCC) subcategories were associated with higher clinical severity (p ≤ 0.05). Vital signs (all p≤0.002) and many laboratory tests from the first day of hospitalization were predictive of peak disease severity. Children with severe (vs moderate) disease were more likely to receive antimicrobials (71% vs 32%, p<0.001) and immunomodulatory medications (53% vs 16%, p<0.001). Compared to those with acute COVID-19, children with MIS-C were more likely to be male, Black/African American, 1-to-12-years-old, and less likely to have asthma, diabetes, or a PCCC (p < 0.04). MIS-C cases demonstrated a more inflammatory laboratory profile and more severe clinical phenotype with higher rates of invasive ventilation (12% vs 6%) and need for vasoactive-inotropic support (31% vs 6%) compared to acute COVID-19 cases, respectively (p<0.03).ConclusionsIn the largest U.S. SARS-CoV-2-positive pediatric cohort to date, we observed differences in demographics, pre-existing comorbidities, and initial vital sign and laboratory test 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.

Project description:ObjectiveThe impact of comorbidities and biomarkers on COVID-19 severity vary by sex but have not yet been verified in population-based studies. We examined the association of comorbidities, inflammatory biomarkers, and severe outcomes in men and women hospitalized for COVID-19.DesignThis is a retrospective cohort analysis based on the National COVID Cohort Collaborative (N3C). We included 574,391 adult patients admitted for COVID-19 at hospitals or emergency rooms between 01/01/2020 and 12/31/2021.MethodsWe defined comorbidities at or before the first admission for COVID-19 by Charlson Comorbidity Index (CCI) and CCI components. We used the averaged lab values taken within 15 days before or after the admission date to measure biomarkers including c-reactive protein (CRP), ferritin, procalcitonin, N-terminal pro b-type natriuretic peptide (NT proBNP), d-dimer, absolute lymphocyte counts, absolute neutrophil counts, and platelets. Our primary outcome was all-cause mortality; secondary outcomes were invasive mechanical ventilation (IMV) and hospital length of stay (LOS). We used logistic regression adjusted for age, race, ethnicity, visit type, and medications to assess the association of comorbidities, biomarkers, and mortality disaggregating by sex.ResultsModerate to severe liver disease, renal disease, metastatic solid tumor, and myocardial infarction were the top four fatal comorbidities among patients who were hospitalized for COVID-19 (adjusted odds ratio [aOR] > 2). These four comorbid conditions remained the most lethal in both sexes, with a higher magnitude of risk in women than in men (p-interaction < 0.05). Abnormal elevations of CRP, ferritin, procalcitonin, NT proBNP, neutrophil, and platelet counts, and lymphocytopenia were significantly associated with the risk of death, with procalcitonin and NT proBNP as the strongest predictors (aOR > 2). The association between the abnormal biomarkers and death was stronger in women than in men (p-interaction < 0.05).ConclusionThere are sex differences in inpatient mortality associated with comorbidities and biomarkers. The significant impact of these clinical determinants in women with COVID-19 may be underappreciated as previous studies stressed the increased death rate in male patients that is related to comorbidities or inflammation. Our study highlights the importance and the need for sex-disaggregated research to understand the risk factors of poor outcomes and health disparities in COVID-19.

Project description:BackgroundMulti-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness.MethodsUsing the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria typical in non-interventional inpatient drug effectiveness studies. We present the challenges encountered when constructing this dataset, beginning with an examination of data quality across data partners. We then discuss the methods and best practices used to operationalize several important study elements: exposure to treatment, baseline health comorbidities, and key outcomes of interest.ResultsWe share our experiences and lessons learned when working with heterogeneous EHR data from over 65 healthcare institutions and 4 common data models. We discuss six key areas of data variability and quality. (1) The specific EHR data elements captured from a site can vary depending on source data model and practice. (2) Data missingness remains a significant issue. (3) Drug exposures can be recorded at different levels and may not contain route of administration or dosage information. (4) Reconstruction of continuous drug exposure intervals may not always be possible. (5) EHR discontinuity is a major concern for capturing history of prior treatment and comorbidities. Lastly, (6) access to EHR data alone limits the potential outcomes which can be used in studies.ConclusionsThe creation of large scale centralized multi-site EHR databases such as N3C enables a wide range of research aimed at better understanding treatments and health impacts of many conditions including COVID-19. As with all observational research, it is important that research teams engage with appropriate domain experts to understand the data in order to define research questions that are both clinically important and feasible to address using these real world data.

Project description:IntroductionElevated glycosylated hemoglobin (HbA1c) in individuals with type 2 diabetes is associated with increased risk of hospitalization and death after acute COVID-19, however the effect of HbA1c on Long COVID is unclear.ObjectiveEvaluate the association of glycemic control with the development of Long COVID in patients with type 2 diabetes (T2D).Research design and methodsWe conducted a retrospective cohort study using electronic health record data from the National COVID Cohort Collaborative. Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30-180 days after COVID-19. Symptoms were identified as keywords from clinical notes using NLP in respiratory, brain fog, fatigue, loss of smell/taste, cough, cardiovascular and musculoskeletal symptom categories. Logistic regression was used to evaluate the risk of Long COVID by HbA1c range, adjusting for demographics, body mass index, comorbidities, and diabetes medication. A COVID-negative group was used as a control.ResultsAmong 7430 COVID-positive patients, 1491 (20.1%) developed symptomatic Long COVID, and 380 (5.1%) died. The primary outcome of death or Long COVID was increased in patients with HbA1c 8% to <10% (OR 1.20, 95% CI 1.02 to 1.41) and ≥10% (OR 1.40, 95% CI 1.14 to 1.72) compared with those with HbA1c 6.5% to <8%. This association was not seen in the COVID-negative group. Higher HbA1c levels were associated with increased risk of Long COVID symptoms, especially respiratory and brain fog. There was no association between HbA1c levels and risk of death within 30-180 days following COVID-19. NLP identified more patients with Long COVID symptoms compared with diagnosis codes.ConclusionPoor glycemic control (HbA1c≥8%) in people with T2D was associated with higher risk of Long COVID symptoms 30-180 days following COVID-19. Notably, this risk increased as HbA1c levels rose. However, this association was not observed in patients with T2D without a history of COVID-19. An NLP-based definition of Long COVID identified more patients than diagnosis codes and should be considered in future studies.

Project description:Background and objectiveThe coronavirus disease 2019 (COVID-19) pandemic has been an unprecedented healthcare crisis, one that threatened to overwhelm health systems and prompted an urgent need for early treatment options for patients with mild-to-moderate COVID-19 at high risk for progression to severe disease. Randomised clinical trials established the safety and efficacy of monoclonal antibodies (mAbs) early in the pandemic; in vitro data subsequently led to use of the mAbs being discontinued, without clear evidence on how these data were linked to outcomes. In this study, we describe and compare real-world outcomes for patients with mild-to-moderate COVID-19 at high risk for progression to severe COVID-19 treated with sotrovimab versus untreated patients.MethodsElectronic health records from the National COVID Cohort Collaborative (N3C) were used to identify US patients (aged ≥ 12 years) diagnosed with COVID-19 (positive test or ICD-10: U07.1) in an ambulatory setting (27 September 2021-30 April 2022) who met Emergency Use Authorization (EUA) high-risk criteria. Patients receiving the mAb sotrovimab within 10 days of diagnosis were assigned to the sotrovimab cohort, with the day of infusion as the index date. Untreated patients (no evidence of early mAb treatment, prophylactic mAb or oral antiviral treatment) were assigned to the untreated cohort, with an imputed index date based on the time distribution between diagnosis and sotrovimab infusion in the sotrovimab cohort. The primary endpoint was hospitalisation or death (both all-cause) within 29 days of index, reported as descriptive rate and adjusted [via inverse probability of treatment weighting (IPTW)] odds ratio (OR) and 95% confidence interval (CI).ResultsOf nearly 2.9 million patients diagnosed with COVID-19 during the analysis period, 4992 met the criteria for the sotrovimab cohort, and 541,325 were included in the untreated cohort. Before weighting, significant differences were noted between the cohorts; for example, patients in the sotrovimab cohort were older (60 years versus 54 years), were more likely to be white (85% versus 75%) and met more EUA criteria (mean 3.1 versus 2.2) versus the untreated cohort. The proportions of patients with 29-day hospitalisation or death were 3.5% (176/4992) and 4.5% (24,163/541,325) in the sotrovimab and untreated cohorts, respectively (unadjusted OR: 0.78; 95% CI: 0.67, 0.91; p = 0.001). In adjusted analysis, sotrovimab was associated with a 25% reduction in the odds of hospitalisation or death compared with the untreated cohort (IPTW-adjusted OR: 0.75; 95% CI: 0.61, 0.92; p = 0.005).ConclusionsSotrovimab demonstrated clinical effectiveness in preventing severe outcomes (hospitalisation, mortality) in the period 27 September 2021-30 April 2022, which included Delta and Omicron BA.1 variants and an early surge of Omicron BA.2 variant.

Project description:ImportanceIdentifying individuals with a higher risk of developing severe COVID-19 outcomes will inform targeted or more intensive clinical monitoring and management.ObjectiveTo examine, using data from the National COVID Cohort Collaborative (N3C), whether patients with pre-existing autoimmune disease (AID) diagnosis and/or immunosuppressant (IS) exposure are at a higher risk of developing severe COVID-19 outcomes.Design setting and participantsA retrospective cohort of 2,453,799 individuals diagnosed with COVID-19 between January 1 st , 2020, and June 30 th , 2022, was created from the N3C data enclave, which comprises data of 15,231,849 patients from 75 USA data partners. Patients were stratified as those with/without a pre-existing diagnosis of AID and/or those with/without exposure to IS prior to COVID-19.Main outcomes and measuresTwo outcomes of COVID-19 severity, derived from the World Health Organization severity score, were defined, namely life-threatening disease and hospitalization. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using logistic regression models with and without adjustment for demographics (age, BMI, gender, race, ethnicity, smoking status), and comorbidities (cardiovascular disease, dementia, pulmonary disease, liver disease, type 2 diabetes mellitus, kidney disease, cancer, and HIV infection).ResultsIn total, 2,453,799 (16.11% of the N3C cohort) adults (age> 18 years) were diagnosed with COVID-19, of which 191,520 (7.81%) had a prior AID diagnosis, and 278,095 (11.33%) had a prior IS exposure. Logistic regression models adjusted for demographic factors and comorbidities demonstrated that individuals with a prior AID (OR = 1.13, 95% CI 1.09 - 1.17; p =2.43E-13), prior exposure to IS (OR= 1.27, 95% CI 1.24 - 1.30; p =3.66E-74), or both (OR= 1.35, 95% CI 1.29 - 1.40; p =7.50E-49) were more likely to have a life-threatening COVID-19 disease. These results were confirmed after adjusting for exposure to antivirals and vaccination in a cohort subset with COVID-19 diagnosis dates after December 2021 (AID OR = 1.18, 95% CI 1.02 - 1.36; p =2.46E-02; IS OR= 1.60, 95% CI 1.41 - 1.80; p =5.11E-14; AID+IS OR= 1.93, 95% CI 1.62 - 2.30; p =1.68E-13). These results were consistent when evaluating hospitalization as the outcome and also when stratifying by race and sex. Finally, a sensitivity analysis evaluating specific IS revealed that TNF inhibitors were protective against life-threatening disease (OR = 0.80, 95% CI 0.66-0.96; p =1.66E-2) and hospitalization (OR = 0.80, 95% CI 0.73 - 0.89; p =1.06E-05).Conclusions and relevancePatients with pre-existing AID, exposure to IS, or both are more likely to have a life-threatening disease or hospitalization. These patients may thus require tailored monitoring and preventative measures to minimize negative consequences of COVID-19.

Dataset Information

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

Objective

Materials and methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets