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1.
Sci Rep ; 11(1): 20238, 2021 10 12.
Article in English | MEDLINE | ID: covidwho-1467130

ABSTRACT

Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January-September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7-7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7-10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19-25%), cerebrovascular diseases (24%, 13-35%), nontraumatic intracranial hemorrhage (34%, 20-50%), encephalitis and/or myelitis (37%, 17-60%) and myopathy (72%, 67-77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease.


Subject(s)
COVID-19 , Nervous System Diseases , Pandemics , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/complications , COVID-19/epidemiology , Child , Child, Preschool , Cross-Sectional Studies , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Nervous System Diseases/epidemiology , Nervous System Diseases/etiology , Prevalence , Severity of Illness Index , Young Adult
2.
J Med Internet Res ; 23(10): e31400, 2021 10 11.
Article in English | MEDLINE | ID: covidwho-1463405

ABSTRACT

BACKGROUND: Many countries have experienced 2 predominant waves of COVID-19-related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. OBJECTIVE: In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. METHODS: Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. RESULTS: Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. CONCLUSIONS: Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.


Subject(s)
COVID-19 , Pandemics , Adult , Aged , Female , Hospitalization , Hospitals , Humans , Male , Middle Aged , Retrospective Studies , SARS-CoV-2
3.
J Am Med Inform Assoc ; 2021 Sep 30.
Article in English | MEDLINE | ID: covidwho-1443051

ABSTRACT

BACKGROUND: 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. METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using four 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 CDM 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 data quality improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multi-site data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.

4.
JAMA Netw Open ; 4(7): e2116901, 2021 07 01.
Article in English | MEDLINE | ID: covidwho-1306627

ABSTRACT

Importance: The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity 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 Participants: In 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 Measures: Patient 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. Results: The 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, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance: This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission.


Subject(s)
COVID-19 , Databases, Factual , Forecasting , Hospitalization , Models, Biological , Severity of Illness Index , Adult , Aged , Aged, 80 and over , COVID-19/ethnology , COVID-19/mortality , Comorbidity , Ethnic Groups , Extracorporeal Membrane Oxygenation , Female , Humans , Hydrogen-Ion Concentration , Male , Middle Aged , Pandemics , Respiration, Artificial , Retrospective Studies , Risk Factors , SARS-CoV-2 , United States , Young Adult
5.
JAMIA Open ; 4(2): ooab036, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1266122

ABSTRACT

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

6.
J Am Med Inform Assoc ; 28(7): 1411-1420, 2021 07 14.
Article in English | MEDLINE | ID: covidwho-1075534

ABSTRACT

OBJECTIVE: The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS: Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS: The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION: We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS: We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.


Subject(s)
COVID-19 , Electronic Health Records , Severity of Illness Index , COVID-19/classification , Hospitalization , Humans , Machine Learning , Prognosis , ROC Curve , Sensitivity and Specificity
9.
Transplant Rev (Orlando) ; 35(1): 100588, 2021 01.
Article in English | MEDLINE | ID: covidwho-922148

ABSTRACT

Severe acute respiratory virus syndrome 2 (SARS-CoV-2) has led to a worldwide pandemic. Early studies in solid organ transplant (SOT) recipients suggested a wide variety of presentations, however, there remains a paucity of robust data in this population. We conducted a systematic review and meta-analysis of SOT recipients with SARS-CoV-2 infection from January 1st t October 9th, 2020. Pooled incidence of symptoms, treatments and outcomes were assessed. Two hundred and fifteen studies were included for systematic review and 60 for meta-analysis. We identified 2,772 unique SOT recipients including 1,500 kidney, 505 liver, 141 heart and 97 lung. Most common presenting symptoms were fever and cough in 70.2% and 63.8% respectively. Majority (81%) required hospital admission. Immunosuppressive medications, especially antimetabolites, were decreased in 76.2%. Hydroxychloroquine and interleukin six antagonists were administered in59.5% and 14.9% respectively, while only few patients received remdesivir and convalescent plasma. Intensive care unit admission was 29% from amongst hospitalized patients. Only few studies reported secondary infections. Overall mortality was 18.6%. Our analysis shows a high incidence of hospital admission in SOT recipients with SARS-CoV-2 infection. As management of SARS-CoV-2 continues to evolve, long-term outcomes among SOT recipients should be assessed in future studies.


Subject(s)
COVID-19/immunology , Immunocompromised Host , Transplant Recipients , Humans , Immunosuppression , Pandemics , SARS-CoV-2
10.
Acad Pathol ; 7: 2374289520958200, 2020.
Article in English | MEDLINE | ID: covidwho-853136

ABSTRACT

When South Florida became a hot spot for COVID-19 disease in March 2020, we faced an urgent need to develop test capability to detect SARS-CoV-2 infection. We assembled a transdisciplinary team of knowledgeable and dedicated physicians, scientists, technologists, and administrators who rapidly built a multiplatform, polymerase chain reaction- and serology-based detection program, established drive-through facilities, and drafted and implemented guidelines that enabled efficient testing of our patients and employees. This process was extremely complex, due to the limited availability of needed reagents, but outreach to our research scientists and multiple diagnostic laboratory companies, and government officials enabled us to implement both Food and Drug Administration authorized and laboratory-developed testing-based testing protocols. We analyzed our workforce needs and created teams of appropriately skilled and certified workers to safely process patient samples and conduct SARS-CoV-2 testing and contact tracing. We initiated smart test ordering, interfaced all testing platforms with our electronic medical record, and went from zero testing capacity to testing hundreds of health care workers and patients daily, within 3 weeks. We believe our experience can inform the efforts of others when faced with a crisis situation.

11.
Open Forum Infect Dis ; 7(9): ofaa372, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-787250

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 is associated with severe disease in patients with hematologic malignancy. We report a series of patients with underlying hematologic malignancy and coronavirus disease of 2019 with discrepancy between radiographic findings and molecular testing. Initial chest x-ray findings should raise suspicion in immunosuppressed patients with typical clinical presentation even with negative initial testing.

12.
J Am Med Inform Assoc ; 28(3): 427-443, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-719257

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.


Subject(s)
COVID-19 , Data Science/organization & administration , Information Dissemination , Intersectoral Collaboration , Computer Security , Data Analysis , Ethics Committees, Research , Government Regulation , Humans , National Institutes of Health (U.S.) , United States
13.
Transpl Infect Dis ; 22(6): e13416, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-652876

ABSTRACT

BACKGROUND: Coronavirus 2019 (COVID-19) pandemic has resulted in more than 350 000 deaths worldwide. The number of kidney transplants has declined during the pandemic. We describe our deceased donor kidney transplantation (DDKT) experience during the pandemic. METHODS: A retrospective study was conducted to evaluate the safety of DDKT during the COVID-19 pandemic. Multiple preventive measures were implemented. Adult patients that underwent DDKT from 3/1/20 to 4/30/20 were included. COVID-19 clinical manifestations from donors and recipients, and post-transplant outcomes (COVID-19 infections, readmissions, allograft rejection, and mortality) were obtained. The kidney transplant (KT) recipients were followed until 5/31/20. RESULTS: Seventy-six patients received kidneys from 57 donors. Fever, dyspnea, and cough were reported in 1, 2, and 1 donor, respectively. Thirty-eight (66.6%) donors were tested for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) prior to donation (mainly by nasopharyngeal or bronchoalveolar lavage polymerase chain reaction [PCR]) and 36 (47.3%) KT recipients were tested at the time of DDKT by nasopharyngeal PCR; all of these were negative. Our recipients were followed for a median of 63 (range: 33-91) days. A total of 42 (55.3%) recipients were tested post-transplant for SARS-CoV2 by nasopharyngeal PCR including 12 patients that became symptomatic; all tests were negative except for one that was inconclusive, but it was repeated and came back negative. Forty (52.6%) KT recipients were readmitted, and 7 (9.2%) had biopsy-proven rejection during the follow-up. None of the KT recipients transplanted during this period died. CONCLUSIONS: Our cohort demonstrated that DDKT can be safely performed during the COVID-19 pandemic when preventive measures are implemented.


Subject(s)
COVID-19/prevention & control , Kidney Transplantation , SARS-CoV-2/isolation & purification , Adult , Aged , COVID-19/epidemiology , COVID-19/mortality , Cough/etiology , Dyspnea/etiology , Female , Fever/etiology , Florida , Hospitals , Humans , Immunosuppression , Male , Middle Aged , Pandemics , Polymerase Chain Reaction , Retrospective Studies , Safety , Transplantation, Homologous/mortality
14.
J Biol Chem ; 295(33): 11742-11753, 2020 08 14.
Article in English | MEDLINE | ID: covidwho-615997

ABSTRACT

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has challenged the speed at which laboratories can discover the viral composition and study health outcomes. The small ∼30-kb ssRNA genome of coronaviruses makes them adept at cross-species spread while enabling a robust understanding of all of the proteins the viral genome encodes. We have employed protein modeling, molecular dynamics simulations, evolutionary mapping, and 3D printing to gain a full proteome- and dynamicome-level understanding of SARS-CoV-2. We established the Viral Integrated Structural Evolution Dynamic Database (VIStEDD at RRID:SCR_018793) to facilitate future discoveries and educational use. Here, we highlight the use of VIStEDD for nsp6, nucleocapsid (N), and spike (S) surface glycoprotein. For both nsp6 and N, we found highly conserved surface amino acids that likely drive protein-protein interactions. In characterizing viral S protein, we developed a quantitative dynamics cross-correlation matrix to gain insights into its interactions with the angiotensin I-converting enzyme 2 (ACE2)-solute carrier family 6 member 19 (SLC6A19) dimer. Using this quantitative matrix, we elucidated 47 potential functional missense variants from genomic databases within ACE2/SLC6A19/transmembrane serine protease 2 (TMPRSS2), warranting genomic enrichment analyses in SARS-CoV-2 patients. These variants had ultralow frequency but existed in males hemizygous for ACE2. Two ACE2 noncoding variants (rs4646118 and rs143185769) present in ∼9% of individuals of African descent may regulate ACE2 expression and may be associated with increased susceptibility of African Americans to SARS-CoV-2. We propose that this SARS-CoV-2 database may aid research into the ongoing pandemic.


Subject(s)
Betacoronavirus/chemistry , Betacoronavirus/genetics , Coronavirus Infections/metabolism , Databases, Protein , Molecular Dynamics Simulation , Pneumonia, Viral/metabolism , Proteome , African Continental Ancestry Group/genetics , Amino Acid Transport Systems, Neutral/chemistry , Amino Acid Transport Systems, Neutral/genetics , Amino Acid Transport Systems, Neutral/metabolism , Angiotensin-Converting Enzyme 2 , COVID-19 , Coronavirus Infections/virology , Coronavirus Nucleocapsid Proteins , Genetic Predisposition to Disease , Genetic Variation , Host-Pathogen Interactions , Humans , Male , Nucleocapsid Proteins/chemistry , Nucleocapsid Proteins/metabolism , Pandemics , Peptidyl-Dipeptidase A/chemistry , Peptidyl-Dipeptidase A/genetics , Peptidyl-Dipeptidase A/metabolism , Phosphoproteins , Pneumonia, Viral/virology , Protein Interaction Maps , Protein Processing, Post-Translational , SARS-CoV-2 , Sequence Homology, Amino Acid , Serine Endopeptidases/chemistry , Serine Endopeptidases/genetics , Serine Endopeptidases/metabolism , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/metabolism
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