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1.
JAMIA open ; 2022.
Article in English | EuropePMC | ID: covidwho-1940060

ABSTRACT

Lay Summary Electronic Health Record (EHR) data collected during routine clinical care offer real world evidence to support decision making and observational research. In the wake of the COVID-19 pandemic, one of the most powerful tools used in clinical trials is the World Health Organization Clinical Progression Scale which provides a minimal set of common outcome measures for guiding research. We developed a generalizable disease severity framework to facilitate research studies utilizing EHR data. EHR data on 2,880,456 SARS-CoV-2-infected patients from 63 health centers across the United States were examined using the National COVID Cohort Collaborative (N3C). We identified and validated concept sets using standard medical terminologies necessary to assign a level of disease severity to each patient. Patterns of change in disease severity among patients during the 28-day period following a COVID-19 diagnosis were characterized and usefulness of the proposed scale was demonstrated. Our severity scale can be used in other COVID-19 observational studies and potentially future diseases requiring point-in-time monitoring of real-world data. Objectives Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data. Methods An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal Components Analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis. Results The data set used in this analysis consists of 2,880,456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period. Discussion An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on progression of COVID-19 disease severity over time. Conclusion The OS provides a framework which can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.

2.
J Rural Health ; 2022 Jun 27.
Article in English | MEDLINE | ID: covidwho-1909479

ABSTRACT

PURPOSE: Rural communities are among the most underserved and resource-scarce populations in the United States. However, there are limited data on COVID-19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS-CoV-2-infected persons stratified by residential rurality. METHODS: This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS-CoV-2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all-cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90-day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan-Meier survival estimates and mixed-effects logistic regression. FINDINGS: Of 1,033,229 diagnosed COVID-19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban-adjacent and nonurban-adjacent rural dwellers with COVID-19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16-1.21 and aOR 1.29, CI 1.24-1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29-1.43 and 1.37, CI 1.26-1.50), respectively. All secondary outcomes were more likely among rural patients. CONCLUSIONS: Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID-19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

3.
Am J Transplant ; 2022 Jun 08.
Article in English | MEDLINE | ID: covidwho-1883178

ABSTRACT

Clinical outcomes in solid organ transplant (SOT) recipients with breakthrough COVID (BTCo) after two doses of mRNA vaccination compared to the non-immunocompromised/immunosuppressed (ISC) general population, are not well described. In a cohort of adult patients testing positive for COVID-19 between December 10, 2020 and April 4, 2022, we compared the cumulative incidence of BTCo in a non-ISC population to SOT recipients (overall and by organ type) using the National COVID Cohort Collaborative (N3C) including data from 36 sites across the United States. We assessed the risk of complications post-BTCo in vaccinated SOT recipients versus SOT with unconfirmed vaccination status (UVS) using multivariable Cox proportional hazards and logistic regression. BTCo occurred in 4776 vaccinated SOT recipients over a median of 149 days (IQR 99-233), with the highest cumulative incidence in heart recipients. The relative risk of BTCo was greatest in SOT recipients (relative to non-ISC) during the pre-Delta period (HR 2.35, 95% CI 1.80-3.08). The greatest relative benefit with vaccination for both non-ISC and SOT cohorts was in BTCo mortality (HR 0.37, 95% CI 0.36-0.39 for non-ISC; HR 0.67, 95% 0.57-0.78 for SOT relative to UVS). While the relative benefit of vaccine was less in SOT than non-ISC, SOT patients still exhibited significant benefit with vaccination.

4.
JPEN J Parenter Enteral Nutr ; 2022 Jun 07.
Article in English | MEDLINE | ID: covidwho-1877655

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is now the third leading cause of death in the United States. Malnutrition in hospitalized patients increases risk of complications. However, the effect of malnutrition on outcomes in patients infected is unclear. This study aims to identify the impact of malnutrition on mortality and adverse hospital events in patients hospitalized with COVID-19. METHODS: This study used data from the National COVID Cohort Collaborative (N3C), a COVID-19 repository containing harmonized, longitudinal electronic health record data from US health systems. Malnutrition was categorized into three groups based on condition diagnosis: (1) none documented, (2) history of malnutrition, and (3) hospital-acquired malnutrition. Multivariable logistic regression was performed to determine whether malnutrition was associated with mortality and adverse events, including mechanical ventilation, acute respiratory distress syndrome, extracorporeal membrane oxygenation, and hospital-acquired pressure injury, in hospitalized patients with COVID-19. RESULTS: Of 343,188 patients hospitalized with COVID-19, 11,206 had a history of malnutrition and 15,711 had hospital-acquired malnutrition. After adjustment for potential confounders, odds of mortality were significantly higher in patients with a history of malnutrition (odds ratio [OR], 1.71; 95% confidence interval [CI], 1.63-1.79; P < 0.001) and hospital-acquired malnutrition (OR, 2.5; 95% CI, 2.4-2.6; P < 0.001). Adjusted odds of adverse hospital events were also significantly elevated in both malnutrition groups. CONCLUSIONS: Results indicate the risk of mortality and adverse inpatient events in adults with COVID-19 is significantly higher in patients with malnutrition. Prevention, diagnosis, and treatment of malnutrition could be a key component in improving outcomes in these patients.

5.
Alcohol Clin Exp Res ; 46(6): 1023-1035, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1794779

ABSTRACT

BACKGROUND: Coronavirus Disease 2019 (COVID-19) has affected every country globally, with hundreds of millions of people infected with the SARS-CoV-2 virus and over 6 million deaths to date. It is unknown how alcohol use disorder (AUD) affects the severity and mortality of COVID-19. AUD is known to increase the severity and mortality of bacterial pneumonia and the risk of developing acute respiratory distress syndrome. Our objective is to determine whether individuals with AUD have increased severity and mortality from COVID-19. METHODS: We utilized a retrospective cohort study of inpatients and outpatients from 44 centers participating in the National COVID Cohort Collaborative. All were adult COVID-19 patients with and without documented AUDs. RESULTS: We identified 25,583 COVID-19 patients with an AUD and 1,309,445 without. In unadjusted comparisons, those with AUD had higher odds of hospitalization (odds ratio [OR] 2.00, 95% confidence interval [CI] 1.94 to 2.06, p < 0.001). After adjustment for age, sex, race/ethnicity, smoking, body mass index, and comorbidities, individuals with an AUD still had higher odds of requiring hospitalization (adjusted OR [aOR] 1.51, CI 1.46 to 1.56, p < 0.001). In unadjusted comparisons, individuals with AUD had higher odds of all-cause mortality (OR 2.18, CI 2.05 to 2.31, p < 0.001). After adjustment as above, individuals with an AUD still had higher odds of all-cause mortality (aOR 1.55, CI 1.46 to 1.65, p < 0.001). CONCLUSION: This work suggests that AUD can increase the severity and mortality of COVID-19 infection. This reinforces the need for clinicians to obtain an accurate alcohol history from patients hospitalized with COVID-19. For this study, our results are limited by an inability to quantify the daily drinking habits of the participants. Studies are needed to determine the mechanisms by which AUD increases the severity and mortality of COVID-19.


Subject(s)
Alcoholism , COVID-19 , Adult , Alcoholism/epidemiology , Hospitalization , Humans , Retrospective Studies , SARS-CoV-2
6.
JAMA Intern Med ; 182(2): 153-162, 2022 02 01.
Article in English | MEDLINE | ID: covidwho-1598451

ABSTRACT

Importance: Persons with immune dysfunction have a higher risk for severe COVID-19 outcomes. However, these patients were largely excluded from SARS-CoV-2 vaccine clinical trials, creating a large evidence gap. Objective: To identify the incidence rate and incidence rate ratio (IRR) for COVID-19 breakthrough infection after SARS-CoV-2 vaccination among persons with or without immune dysfunction. Design, Setting, and Participants: This retrospective cohort study analyzed data from the National COVID Cohort Collaborative (N3C), a partnership that developed a secure, centralized electronic medical record-based repository of COVID-19 clinical data from academic medical centers across the US. Persons who received at least 1 dose of a SARS-CoV-2 vaccine between December 10, 2020, and September 16, 2021, were included in the sample. Main Outcomes and Measures: Vaccination, COVID-19 diagnosis, immune dysfunction diagnoses (ie, HIV infection, multiple sclerosis, rheumatoid arthritis, solid organ transplant, and bone marrow transplantation), other comorbid conditions, and demographic data were accessed through the N3C Data Enclave. Breakthrough infection was defined as a COVID-19 infection that was contracted on or after the 14th day of vaccination, and the risk after full or partial vaccination was assessed for patients with or without immune dysfunction using Poisson regression with robust SEs. Poisson regression models were controlled for a study period (before or after [pre- or post-Delta variant] June 20, 2021), full vaccination status, COVID-19 infection before vaccination, demographic characteristics, geographic location, and comorbidity burden. Results: A total of 664 722 patients in the N3C sample were included. These patients had a median (IQR) age of 51 (34-66) years and were predominantly women (n = 378 307 [56.9%]). Overall, the incidence rate for COVID-19 breakthrough infection was 5.0 per 1000 person-months among fully vaccinated persons but was higher after the Delta variant became the dominant SARS-CoV-2 strain (incidence rate before vs after June 20, 2021, 2.2 [95% CI, 2.2-2.2] vs 7.3 [95% CI, 7.3-7.4] per 1000 person-months). Compared with partial vaccination, full vaccination was associated with a 28% reduced risk for breakthrough infection (adjusted IRR [AIRR], 0.72; 95% CI, 0.68-0.76). People with a breakthrough infection after full vaccination were more likely to be older and women. People with HIV infection (AIRR, 1.33; 95% CI, 1.18-1.49), rheumatoid arthritis (AIRR, 1.20; 95% CI, 1.09-1.32), and solid organ transplant (AIRR, 2.16; 95% CI, 1.96-2.38) had a higher rate of breakthrough infection. Conclusions and Relevance: This cohort study found that full vaccination was associated with reduced risk of COVID-19 breakthrough infection, regardless of the immune status of patients. Despite full vaccination, persons with immune dysfunction had substantially higher risk for COVID-19 breakthrough infection than those without such a condition. For persons with immune dysfunction, continued use of nonpharmaceutical interventions (eg, mask wearing) and alternative vaccine strategies (eg, additional doses or immunogenicity testing) are recommended even after full vaccination.


Subject(s)
COVID-19 Testing/statistics & numerical data , COVID-19/diagnosis , COVID-19/epidemiology , Health Status , Vaccination/statistics & numerical data , Adult , Aged , COVID-19 Vaccines , Cohort Studies , Female , Humans , Incidence , Male , Middle Aged , Retrospective Studies , Risk Factors , SARS-CoV-2/isolation & purification , Sex Distribution
9.
Open forum infectious diseases ; 8(Suppl 1):324-325, 2021.
Article in English | EuropePMC | ID: covidwho-1564350

ABSTRACT

Background A major challenge to identifying effective treatments for COVID-19 has been the conflicting results offered by small, often underpowered clinical trials. The World Health Organization (WHO) Ordinal Scale (OS) has been used to measure clinical improvement among clinical trial participants and has the benefit of measuring effect across the spectrum of clinical illness. We modified the WHO OS to enable assessment of COVID-19 patient outcomes using electronic health record (EHR) data. Methods Employing the National COVID Cohort Collaborative (N3C) database of EHR data from 50 sites in the United States, we assessed patient outcomes, April 1,2020 to March 31, 2021, among those with a SARS-CoV-2 diagnosis, using the following modification of the WHO OS: 1=Outpatient, 3=Hospitalized, 5=Required Oxygen (any), 7=Mechanical Ventilation, 9=Organ Support (pressors;ECMO), 11=Death. OS is defined over 4 weeks beginning at first diagnosis and recalculated each week using the patient’s maximum OS value in the corresponding 7-day period. Modified OS distributions were compared across time using a Pearson Chi-Squared test. Results The study sample included 1,446,831 patients, 54.7% women, 14.7% Black, 14.6% Hispanic/Latinx. Pearson Chi-Sq P< 0.0001 was obtained comparing the distribution of 2nd Quarter 2020 OS with the distribution of later time points for Week 4. Table 1. OS at week 1 and 4 by quarter The study sample included 1,446,831 patients, 54.7% women, 14.7% Black, 14.6% Hispanic/Latinx. Pearson Chi-Sq P< 0.0001 was obtained comparing the distribution of 2nd Quarter 2020 OS with the distribution of later time points for Week 4. Conclusion All Week 4 OS distributions significantly improved from the initial period (April-June 2020) compared with subsequent months, suggesting improved management. Further work is needed to determine which elements of care are driving the improved outcomes. Time series analyses must be included when assessing impact of therapeutic modalities across the COVID pandemic time frame. Disclosures Sally L. Hodder, M.D., Gilead (Advisor or Review Panel member)Merck (Grant/Research Support, Advisor or Review Panel member)Viiv Healthcare (Grant/Research Support, Advisor or Review Panel member)

10.
Open forum infectious diseases ; 8(Suppl 1):S23-S24, 2021.
Article in English | EuropePMC | ID: covidwho-1563823

ABSTRACT

Background Rural communities are among the most vulnerable and resource-scarce populations in the United States. Rural data is rarely centralized, precluding comparability across regions, and no significant studies have studied this population at scale. The purpose of this study is to present findings from the National COVID Cohort Collaborative (N3C) to provide insight into future research and highlight the urgent need to address health disparities in rural populations. N3C Patient Distribution This figure shows the geospatial distribution of the N3C COVID-19 positive population. N3C contains data from 55 data contributors from across the United States, 40 of whom include sufficient location information to map by ZIP Code centroid spatially. Of those sites, we selected 27 whose data met our minimum robustness qualifications for inclusion in our study. This bubble map is to scale with larger bubbles representing more patients. A. shows all N3C patients. B. shows only urban N3C distribution. C. shows the urban-adjacent rural patient distribution. D. shows the nonurban-adjacent rural patient distribution, representing the most isolated patients in N3C. Methods This retrospective cohort of 573,018 patients from 27 hospital systems presenting with COVID-19 between January 2020 and March 2021, of whom 117,897 were admitted (see Data Analysis Plan diagram for inclusion/exclusion criteria), analyzes outcomes and 30-day survival for the hospitalized population by the degree of rurality. Multivariate Cox regression analysis and mixed-effects models were used to estimate the association between rurality, hospitalization, and all-cause mortality, controlling for major risk factors associated with rural-urban health discrepancies and differences in health system outcomes. The difference in distribution by rurality is described as well as supplemented by population-level statistics to confirm representativeness. Data Analysis Plan This data analysis plan includes an overview of study inclusion and exclusion criteria, the matrix for data robustness to determine potential sites to include, and our covariate selection, model building, and residual testing strategy. Results This study demonstrates a significant difference between hospital admissions and outcomes in urban versus urban-adjacent rural (UAR) and nonurban-adjacent rural (NAR) lines. Hospital admissions for UAR (OR 1.41, p< 0.001, 95% CI: 1.37 – 1.45) and NAR (OR 1.42, p< 0.001, 95% CI: 1.35 – 1.50) were significantly higher than their urban counterparts. Similar distributions were present for all-cause mortality for UAR (OR 1.39, p< 0.001, 95% CI: 1.30 – 1.49) and NAR (OR 1.38, p< 0.001, 95% CI: 1.22 – 1.55) compared to urban populations. These associations persisted despite adjustments for significant differences in BMI, Charlson Comorbidity index Score, gender, age, and the quarter of diagnosis for COVID-19. Baseline Characteristics Hospitalized COVID-19 Positive Population by Rurality Category, January 2020 – March 2021 Survival Curves in Hospitalized Patients Over 30 Days from Day of Admission This figure shows a survival plot of COVID-19 positive hospitalized patients in N3C by rural category (A), Charlson Comorbidity Index (B), Quarter of Diagnosis (C), and Age Group (D) from hospital admission through day 30. Events were censored at day 30 based on the incidence of death or transfer to hospice care. These four factors had the highest predictive power of the covariates evaluated in this study. Unadjusted and Adjusted Odds Ratios for Hospitalization and All-Cause Mortality by Rural Category, January 2020 – March 2021 This figure shows the adjusted and unadjusted odds ratios for being hospitalized or dying after hospitalization for the COVID-19 positive population in N3C. Risk is similar between adjusted and unadjusted models, suggesting a real impact of rurality on all-cause mortality. A shows the unadjusted odds ratios for admission to the hospital after a positive COVID-19 diagnosis for all N3C patients. B shows the unadjusted odds ratios for all-cause mortality at any point after hospitalization for COVID-19 positive patients. C shows the adjusted odds ratios for being admitted to the hospital after a positive COVID-19 diagnosis for all N3C patients. D shows the adjusted odds ratios for all-cause mortality for all-cause mortality at any point after hospitalization for COVID-19 positive patients. Adjusted models include adjustments for gender, race, ethnicity, BMI, age, Charlson Comorbidity Index (CCI) composite score, rurality, and quarter of diagnosis. The data provider is included as a random effect in all models. Conclusion In N3C, we found that hospitalizations and all-cause mortality were greater among rural populations when compared to urban populations after adjustment for several factors, including age and co-morbidities. This study also identified key demographic and clinical disparities among rural patients that require further investigation. Disclosures Sally L. Hodder, M.D., Gilead (Advisor or Review Panel member)Merck (Grant/Research Support, Advisor or Review Panel member)Viiv Healthcare (Grant/Research Support, Advisor or Review Panel member)

11.
EBioMedicine ; 74: 103722, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1536517

ABSTRACT

BACKGROUND: Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS: The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FUNDING: We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION: Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING: U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.


Subject(s)
COVID-19/complications , COVID-19/pathology , COVID-19/diagnosis , Humans , SARS-CoV-2
12.
Transplant Direct ; 7(11): e775, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1467456

ABSTRACT

Coronavirus disease 2019 (COVID-19) has resulted in significant morbidity and mortality in solid organ transplant (SOT) recipients. The National COVID Cohort Collaborative was developed to facilitate analysis of patient-level data for those tested for COVID-19 across the United States. METHODS: In this study, we identified a cohort of SOT recipients testing positive or negative for COVID-19 (COVID+ and COVID-, respectively) between January 1, 2020, and November 20, 2020. Univariable and multivariable logistic regression were used to determine predictors of a positive result among those tested. Outcomes following COVID-19 diagnosis were also explored. RESULTS: Of 18 121 SOT patients tested, 1925 were positive (10.6%). COVID+ SOT patients were more likely to have a kidney transplant and be non-White race. Comorbidities were common in all SOT patients but significantly more common in those who were COVID+. Of COVID+ SOT, 42.9% required hospital admission. COVID+ status was the strongest predictor of acute kidney injury (AKI), rejection, and graft failure in the 90 d after testing. A total of 40.9% of COVID+ SOT experienced a major adverse renal or cardiac event, 16.3% experienced a major adverse cardiac event, 35.3% experienced AKI, and 1.5% experienced graft loss. CONCLUSIONS: In the largest US cohort of COVID+ SOT recipients to date, we identified patient factors associated with the diagnosis of COVID-19 and outcomes following infection, including a high incidence of major adverse renal or cardiac event and AKI.

13.
Am J Transplant ; 22(1): 245-259, 2022 01.
Article in English | MEDLINE | ID: covidwho-1462722

ABSTRACT

While older males are at the highest risk for poor coronavirus disease 2019 (COVID-19) outcomes, it is not known if this applies to the immunosuppressed recipient of a solid organ transplant (SOT), nor how the type of allograft transplanted may impact outcomes. In a cohort study of adult (>18 years) patients testing positive for COVID-19 (January 1, 2020-June 21, 2021) from 56 sites across the United States identified using the National COVID Cohort Collaborative (N3C) Enclave, we used multivariable Cox proportional hazards models to assess time to MARCE after COVID-19 diagnosis in those with and without SOT. We examined the exposure of age-stratified recipient sex overall and separately in kidney, liver, lung, and heart transplant recipients. 3996 (36.4%) SOT and 91 646 (4.8%) non-SOT patients developed MARCE. Risk of post-COVID outcomes differed by transplant allograft type with heart and kidney recipients at highest risk. Males with SOT were at increased risk of MARCE, but to a lesser degree than the non-SOT cohort (HR 0.89, 95% CI 0.81-0.98 for SOT and HR 0.61, 95% CI 0.60-0.62 for non-SOT [females vs. males]). This represents the largest COVID-19 SOT cohort to date and the first-time sex-age-stratified and allograft-specific COVID-19 outcomes have been explored in those with SOT.


Subject(s)
COVID-19 , Organ Transplantation , Adult , COVID-19 Testing , Cohort Studies , Female , Humans , Kidney , Male , Organ Transplantation/adverse effects , SARS-CoV-2 , Transplant Recipients , United States
14.
J Am Med Inform Assoc ; 29(4): 609-618, 2022 03 15.
Article in English | MEDLINE | ID: covidwho-1443051

ABSTRACT

OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.


Subject(s)
COVID-19 , Cohort Studies , Data Accuracy , Health Insurance Portability and Accountability Act , Humans , United States
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