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Bradwell KR, Wooldridge JT, Amor B, Bennett TD, Anand A, Bremer C, Yoo YJ, Qian Z, Johnson SG, Pfaff ER, Girvin AT, Manna A, Niehaus EA, Hong SS, Zhang XT, Zhu RL, Bissell M, Qureshi N, Saltz J, Haendel MA, Chute CG, Lehmann HP, Moffitt RA. Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset. J Am Med Inform Assoc 2022; 29:1172-1182. [PMID: 35435957 PMCID: PMC9196692 DOI: 10.1093/jamia/ocac054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/25/2022] [Accepted: 04/08/2022] [Indexed: 11/24/2022] Open
Abstract
Objective The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing. Materials and Methods The National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test. Results Of the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors’ records lacked units). Discussion The harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference. Conclusion The pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.
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Affiliation(s)
| | - Jacob T Wooldridge
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | | | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, Colorado, USA
| | - Adit Anand
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Carolyn Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Zhenglong Qian
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA
| | - Emily R Pfaff
- Department of Medicine, North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Amin Manna
- Palantir Technologies, Denver, Colorado, USA
| | | | - Stephanie S Hong
- School of Medicine, Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Richard L Zhu
- Department of Medicine, Johns Hopkins, Baltimore, Maryland, USA
| | | | | | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | | | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
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Martin B, DeWitt PE, Russell S, Anand A, Bradwell KR, Bremer C, Gabriel D, Girvin AT, Hajagos JG, McMurry JA, Neumann AJ, Pfaff ER, Walden A, Wooldridge JT, Yoo YJ, Saltz J, Gersing KR, Chute CG, Haendel MA, Moffitt R, Bennett TD. Characteristics, Outcomes, and Severity Risk Factors Associated With SARS-CoV-2 Infection Among Children in the US National COVID Cohort Collaborative. JAMA Netw Open 2022; 5:e2143151. [PMID: 35133437 PMCID: PMC8826172 DOI: 10.1001/jamanetworkopen.2021.43151] [Citation(s) in RCA: 85] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/15/2021] [Indexed: 01/20/2023] Open
Abstract
Importance Understanding of SARS-CoV-2 infection in US children has been limited by the lack of large, multicenter studies with granular data. Objective To examine the characteristics, changes over time, outcomes, and severity risk factors of children with SARS-CoV-2 within the National COVID Cohort Collaborative (N3C). Design, Setting, and Participants A prospective cohort study of encounters with end dates before September 24, 2021, was conducted at 56 N3C facilities throughout the US. Participants included children younger than 19 years at initial SARS-CoV-2 testing. Main Outcomes and Measures Case incidence and severity over time, demographic and comorbidity severity risk factors, vital sign and laboratory trajectories, clinical outcomes, and acute COVID-19 vs multisystem inflammatory syndrome in children (MIS-C), and Delta vs pre-Delta variant differences for children with SARS-CoV-2. Results A total of 1 068 410 children were tested for SARS-CoV-2 and 167 262 test results (15.6%) were positive (82 882 [49.6%] girls; median age, 11.9 [IQR, 6.0-16.1] years). Among the 10 245 children (6.1%) who were hospitalized, 1423 (13.9%) met the criteria for severe disease: mechanical ventilation (796 [7.8%]), vasopressor-inotropic support (868 [8.5%]), extracorporeal membrane oxygenation (42 [0.4%]), or death (131 [1.3%]). Male sex (odds ratio [OR], 1.37; 95% CI, 1.21-1.56), Black/African American race (OR, 1.25; 95% CI, 1.06-1.47), obesity (OR, 1.19; 95% CI, 1.01-1.41), and several pediatric complex chronic condition (PCCC) subcategories were associated with higher severity disease. Vital signs and many laboratory test values from the day of admission were predictive of peak disease severity. Variables associated with increased odds for MIS-C vs acute COVID-19 included male sex (OR, 1.59; 95% CI, 1.33-1.90), Black/African American race (OR, 1.44; 95% CI, 1.17-1.77), younger than 12 years (OR, 1.81; 95% CI, 1.51-2.18), obesity (OR, 1.76; 95% CI, 1.40-2.22), and not having a pediatric complex chronic condition (OR, 0.72; 95% CI, 0.65-0.80). The children with MIS-C had a more inflammatory laboratory profile and severe clinical phenotype, with higher rates of invasive ventilation (117 of 707 [16.5%] vs 514 of 8241 [6.2%]; P < .001) and need for vasoactive-inotropic support (191 of 707 [27.0%] vs 426 of 8241 [5.2%]; P < .001) compared with those who had acute COVID-19. Comparing children during the Delta vs pre-Delta eras, there was no significant change in hospitalization rate (1738 [6.0%] vs 8507 [6.2%]; P = .18) and lower odds for severe disease (179 [10.3%] vs 1242 [14.6%]) (decreased by a factor of 0.67; 95% CI, 0.57-0.79; P < .001). Conclusions and Relevance In this cohort study of US children with SARS-CoV-2, there were observed differences in demographic characteristics, preexisting comorbidities, and initial vital sign and laboratory 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.
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Affiliation(s)
- Blake Martin
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
| | - Peter E. DeWitt
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
| | - Seth Russell
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
| | - Adit Anand
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | | | - Carolyn Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Davera Gabriel
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Janos G. Hajagos
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Julie A. McMurry
- Translational and Integrative Sciences Center, University of Colorado, Aurora
- Center for Health AI, University of Colorado, Aurora
| | - Andrew J. Neumann
- Translational and Integrative Sciences Center, University of Colorado, Aurora
- Center for Health AI, University of Colorado, Aurora
| | - Emily R. Pfaff
- North Carolina Translational and Clinical Sciences Institute), University of North Carolina at Chapel Hill, Chapel Hill
| | - Anita Walden
- Center for Health AI, University of Colorado, Aurora
| | - Jacob T. Wooldridge
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Ken R. Gersing
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland
| | - Christopher G. Chute
- Johns Hopkins University School of Medicine, Baltimore, Maryland
- Schools of Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland
| | | | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Tellen D. Bennett
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
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Martin B, DeWitt PE, Russell S, Anand A, Bradwell KR, Bremer C, Gabriel D, Girvin AT, Hajagos JG, McMurry JA, Neumann AJ, Pfaff ER, Walden A, Wooldridge JT, Yoo YJ, Saltz J, Gersing KR, Chute CG, Haendel MA, Moffitt R, Bennett TD. Children with SARS-CoV-2 in the National COVID Cohort Collaborative (N3C). medRxiv 2021:2021.07.19.21260767. [PMID: 34341796 PMCID: PMC8328064 DOI: 10.1101/2021.07.19.21260767] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
IMPORTANCE SARS-CoV-2. OBJECTIVE To determine the characteristics, changes over time, outcomes, and severity risk factors of SARS-CoV-2 affected children within the National COVID Cohort Collaborative (N3C). DESIGN Prospective cohort study of patient encounters with end dates before May 27th, 2021. SETTING 45 N3C institutions. PARTICIPANTS Children <19-years-old at initial SARS-CoV-2 testing. MAIN OUTCOMES AND MEASURES Case 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. RESULTS 728,047 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). CONCLUSIONS In 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.
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Affiliation(s)
- Blake Martin
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, CO, USA
| | - Peter E. DeWitt
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, CO, USA
| | - Seth Russell
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, CO, USA
| | - Adit Anand
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | | | - Carolyn Bremer
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Davera Gabriel
- Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Janos G. Hajagos
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Julie A. McMurry
- Translational and Integrative Sciences Center, University of Colorado, Aurora, CO, USA,Center for Health AI, University of Colorado, Aurora, CO, USA
| | - Andrew J. Neumann
- Translational and Integrative Sciences Center, University of Colorado, Aurora, CO, USA,Center for Health AI, University of Colorado, Aurora, CO, USA
| | - Emily R. Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Anita Walden
- Center for Health AI, University of Colorado, Aurora, CO, USA
| | - Jacob T. Wooldridge
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Joel Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Ken R. Gersing
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Christopher G. Chute
- Johns Hopkins University School of Medicine, Baltimore, MD, USA,Schools of Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Tellen D. Bennett
- Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, CO, USA,Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, CO, USA
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Bennett TD, Moffitt RA, Hajagos JG, Amor B, Anand A, Bissell MM, Bradwell KR, Bremer C, Byrd JB, Denham A, DeWitt PE, Gabriel D, Garibaldi BT, Girvin AT, Guinney J, Hill EL, Hong SS, Jimenez H, Kavuluru R, Kostka K, Lehmann HP, Levitt E, Mallipattu SK, Manna A, McMurry JA, Morris M, Muschelli J, Neumann AJ, Palchuk MB, Pfaff ER, Qian Z, Qureshi N, Russell S, Spratt H, Walden A, Williams AE, Wooldridge JT, Yoo YJ, Zhang XT, Zhu RL, Austin CP, Saltz JH, Gersing KR, Haendel MA, Chute CG. Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative. JAMA Netw Open 2021; 4:e2116901. [PMID: 34255046 PMCID: PMC8278272 DOI: 10.1001/jamanetworkopen.2021.16901] [Citation(s) in RCA: 146] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/03/2021] [Indexed: 12/15/2022] Open
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.
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Affiliation(s)
- Tellen D. Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
| | - Richard A. Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | | | | | - Adit Anand
- Stony Brook University, Stony Brook, New York
| | | | | | | | - James Brian Byrd
- Department of Internal Medicine, The University of Michigan at Ann Arbor, Ann Arbor
| | - Alina Denham
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York
| | - Peter E. DeWitt
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
| | - Davera Gabriel
- Institute for Clinical and Translational Research, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Brian T. Garibaldi
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | | | - Elaine L. Hill
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, New York
| | - Stephanie S. Hong
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | | | - Ramakanth Kavuluru
- Division of Biomedical Informatics, Department of Internal Medicine, University of Kentucky, Lexington
| | - Kristin Kostka
- Real World Solutions, IQVIA, Cambridge, Massachusetts
- Observational Health Data Sciences and Informatics, New York, New York
| | - Harold P. Lehmann
- Division of Health Science Informatics, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Eli Levitt
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham
| | | | | | - Julie A. McMurry
- Translational and Integrative Sciences Center, Oregon State University, Corvallis
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Andrew J. Neumann
- Translational and Integrative Sciences Center, Oregon State University, Corvallis
| | | | - Emily R. Pfaff
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill
| | - Zhenglong Qian
- Department of biomedical informatics, Stony Brook University, Stony Brook, New York
| | | | - Seth Russell
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora
| | - Heidi Spratt
- Department of Preventive Medicine and Public Health, University of Texas Medical Branch, Galveston
| | - Anita Walden
- Sage Bionetworks, Seattle, Washington
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland
| | - Andrew E. Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts
| | | | - Yun Jae Yoo
- Stony Brook University, Stony Brook, New York
| | - Xiaohan Tanner Zhang
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Richard L. Zhu
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Christopher P. Austin
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York
| | - Ken R. Gersing
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, Maryland
| | - Melissa A. Haendel
- TriNetX, Cambridge, Massachusetts
- Center for Health AI, University of Colorado, Aurora
| | - Christopher G. Chute
- Department of Health Policy and Management, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Nursing, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Bennett TD, Moffitt RA, Hajagos JG, Amor B, Anand A, Bissell MM, Bradwell KR, Bremer C, Byrd JB, Denham A, DeWitt PE, Gabriel D, Garibaldi BT, Girvin AT, Guinney J, Hill EL, Hong SS, Jimenez H, Kavuluru R, Kostka K, Lehmann HP, Levitt E, Mallipattu SK, Manna A, McMurry JA, Morris M, Muschelli J, Neumann AJ, Palchuk MB, Pfaff ER, Qian Z, Qureshi N, Russell S, Spratt H, Walden A, Williams AE, Wooldridge JT, Yoo YJ, Zhang XT, Zhu RL, Austin CP, Saltz JH, Gersing KR, Haendel MA, Chute CG. The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction. medRxiv 2021. [PMID: 33469592 PMCID: PMC7814838 DOI: 10.1101/2021.01.12.21249511] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background: The 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 Findings: In 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. Conclusions: This 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.
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Roll W, Beyer F, Allkemper T, Tombach B, Bremer C. Klinischer Nutzen der diffusionsgewichteten Leberbildgebung mit extrapoliertem hohen b-Wert. ROFO-FORTSCHR RONTG 2017. [DOI: 10.1055/s-0037-1600222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- W Roll
- Uniklinikum Münster, Klinik für Nuklearmedizin, Münster
| | - F Beyer
- Klinik für Radiologie, St. Franziskus Hospital, Münster
| | - T Allkemper
- Institut für klinische Radiologie, Uniklinikum Münster, Münster
| | - B Tombach
- Röntgen und Strahlenklinik, Klinikum Osnabrück, Osnabrück
| | - C Bremer
- Klinik für Radiologie, St. Franziskus Hospital, Münster
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Große HN, Barczyk-Kahlert K, Becker A, Vogl T, Heindel W, Bremer C, Geyer C, Eisenblätter M. Abschätzung des metastatischen Potenzials beim murinen Mammakarzinom mittels optischer in vivo Bildgebung zur Darstellung Tumor-assoziierter Makrophagen. ROFO-FORTSCHR RONTG 2014. [DOI: 10.1055/s-0034-1373255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Bremer C. Wie wird man heute Chefarzt? – Mein Weg ins St. Franziskus Hospital Münster. ROFO-FORTSCHR RONTG 2014. [DOI: 10.1055/s-0034-1373233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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9
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Becker A, Varga G, Vogl T, Roth J, Große Hokamp N, Heindel W, Bremer C, Eisenblätter M. Targetspezifische Optische Molekulare Bildgebung zur Prognoseabschätzung des Tumorwachstums durch Darstellung zellulärer Bestandteile des Tumormikromilieus. ROFO-FORTSCHR RONTG 2014. [DOI: 10.1055/s-0034-1373254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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10
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Persigehl T, Stypmann J, Hermann S, Remmele S, Ring J, Schäfers M, Heindel W, Mesters R, Berdel W, Bremer C, Schwöppe C. Multi-modales Monitoring der anti-vaskulären tTF-NGR-Tumortherapie mittels USPIO- MRT, KM-Ultraschall (CEUS), Single-Photonen-Emissions-CT (SPECT) und Fluoreszenz-Bildgebung (FRI) im Xenograftmodell. ROFO-FORTSCHR RONTG 2013. [DOI: 10.1055/s-0033-1346201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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11
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Höink AJ, Persigehl T, Mesters RM, Berdel WE, Heindel WL, Bremer C, Schwöppe C. Echtzeit-Bildgebung des Ansprechens muriner Tumore auf die anti-vaskuläre Therapie mittels tTF-NGR mithilfe Gadofosveset-verstärkter MRT. ROFO-FORTSCHR RONTG 2013. [DOI: 10.1055/s-0033-1346432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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12
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Schmidt R, Strobel K, Reifschneider O, Delli Castelli D, Aime S, Bremer C, Faber C. Heteronukleare Protonenbildgebung als innovative MR-Technik für Cell Tracking Studien - Evaluierung im murinen Tumormodell. ROFO-FORTSCHR RONTG 2013. [DOI: 10.1055/s-0033-1346202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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13
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Persigehl T, Wieskötter B, Tiggemann H, Timmen M, Remmele S, Ring J, Heindel W, Bremer C, Stange R, Viet V. USPIO-unterstützte MR-Relaxometrie zum in-vivo Monitoring der Angiogenese im Rahmen der Frakturheilung. ROFO-FORTSCHR RONTG 2012. [DOI: 10.1055/s-0032-1329769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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14
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Becker A, Varga G, Vogl TJ, Roth J, Heindel W, Bremer C, Eisenblätter M. In-vivo-Bildgebung von Makrophagenaktivität im Tumor - Methodenentwicklung und Evaluation des prädiktiven Wertes. ROFO-FORTSCHR RONTG 2012. [DOI: 10.1055/s-0032-1311033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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15
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Eisenblätter M, Vogl TJ, Heindel W, Bremer C. Target-spezifische optische Bildgebung von Makrophagenaktivität in lokalen Infektionsfoci in-vivo - eine Methode zur Charakterisierung, Verlaufsbeurteilung und Prognoseabschätzung. ROFO-FORTSCHR RONTG 2012. [DOI: 10.1055/s-0032-1311031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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16
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Persigehl T, Wieskötter B, Tiggemann H, Timmen M, Remmele S, Ring J, Heindel W, Bremer C, Stange R, Vieth V. In-vivo Monitoring der Angiogenese im Rahmen der Frakturheilung mittels USPIO-unterstützter MRT. ROFO-FORTSCHR RONTG 2012. [DOI: 10.1055/s-0032-1311166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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17
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Höltke C, Büther K, Compeer MG, de Mey JG, Schober O, Schäfers M, Heindel W, Riemann B, Bremer C. Optische Bildgebung der Endothelin-A-Rezeptor-Expression in murinen Schilddrüsen-Karzinom-Xenograften. ROFO-FORTSCHR RONTG 2012. [DOI: 10.1055/s-0032-1311459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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18
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Hölzner E, Lippross V, Hermann S, Nagelmann N, Heselhaus J, Bohlen S, Kugel H, Deppe M, Sommer J, Bremer C, Nguyen H, Riess O, Hörsten SV, Schäfers M, Jacobs A, Reilmann R. PET/MRI-based phenotyping of a transgenic rat model for Huntington's disease - a 16 months follow-up study. KLIN NEUROPHYSIOL 2011. [DOI: 10.1055/s-0031-1272762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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19
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Höltke C, Faust A, Breyholz HJ, Kopka K, Schober O, Riemann B, Bremer C, Schäfers M, Wagner S. Non-invasive approaches to visualize the endothelin axis in vivo using state-of-the-art molecular imaging modalities. Mini Rev Med Chem 2010; 9:1580-95. [PMID: 20088779 DOI: 10.2174/138955709791012210] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2009] [Accepted: 12/18/2009] [Indexed: 11/22/2022]
Abstract
The endothelin axis plays a major role in cardiovascular diseases and a number of human cancers. This review summarizes the work that has been published in the past ten years using labeled endothelin receptor ligands for the visualization of endothelin receptor expression in vivo.
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Affiliation(s)
- C Höltke
- Department of Nuclear Medicine, University Hospital Münster, Münster, Germany.
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Bremer C. Molekulare Bildgebung ist keine Hexerei: Etablierte und kommende Anwendungen. ROFO-FORTSCHR RONTG 2010. [DOI: 10.1055/s-0030-1252126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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21
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Lippross V, Hermann S, Nagelmann N, Heselhaus J, Bohlen S, Kugel H, Deppe M, Sommer J, Bremer C, Nguyen H, Riess O, Hörsten SV, Schäfers M, Reilmann R. In vivo assessment of neuronal dysfunction in rats transgenic for Huntington's disease using small animal FDG-PET and MRI – a 16 months follow-up study. Akt Neurol 2009. [DOI: 10.1055/s-0029-1238559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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22
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Eisenblätter M, Vogl T, van Lent P, Heindel W, Roth J, Bremer C. Anti-MRP14-Cy5.5– ein spezifischer Fluoreszenztracer zur Untersuchung entzündlicher Prozesse in vivo. ROFO-FORTSCHR RONTG 2009. [DOI: 10.1055/s-0029-1221332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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23
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Ring J, Persigehl T, Dahnke H, Heindel W, Remmele S, Bremer C. Anti-angiogenes Therapiemonitoring mithilfe einer optimierten DeltaR2* MR-Relaxometrie Methode. ROFO-FORTSCHR RONTG 2009. [DOI: 10.1055/s-0029-1221330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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24
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Buerke B, Bremer C, Kooijman H, Tombach B, Heindel W, Allkemper T. Bestimmung der optimalen „Keyhole-Percentage“ für die hochaufgelöste, kontrastmittelgestützte 3D-Keyhole-MRA: Ergebnisse einer experimentellen Studie. ROFO-FORTSCHR RONTG 2009. [DOI: 10.1055/s-0029-1221604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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25
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Faust A, Waschkau B, Waldeck J, Wagner S, Kopka K, Schäfers M, Heindel W, Bremer C. Entwicklung fluoreszenzmarkierter kleinmolekularer MMP-Inhibitoren zur in vivo optischen Bildgebung. ROFO-FORTSCHR RONTG 2009. [DOI: 10.1055/s-0029-1221335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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26
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Persigehl T, Ring J, Dahnke H, Heindel W, Remmele S, Bremer C. In-vivo Abschätzung des relativen Tumorgefäßdurchmessers mittels dR2*/dR2 MR-Relaxometrie. ROFO-FORTSCHR RONTG 2009. [DOI: 10.1055/s-0029-1221279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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27
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Persigehl T, Ring J, Heindel W, Dahnke H, Remmele S, Bremer C. R2*/R2 MR-Relaxometrie zur in-vivo Evaluierung des relativen Tumorgefäßdurchmessers. ROFO-FORTSCHR RONTG 2009. [DOI: 10.1055/s-0029-1208331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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28
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Alacalde E, Amijee F, Blache G, Bremer C, Fernandez S, Garcia-Alonso M, Holt K, Legris G, Novillo C, Schlotter P, Storer N, Tinland B. Insect Resistance Monitoring for Bt Maize Cultivation in the EU: Proposal from the Industry IRM Working Group. J Verbrauch Lebensm 2008. [DOI: 10.1007/s00003-007-0236-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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29
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Höltke C, Waldeck J, Faust A, Schäfers M, Bremer C. Biodistribution, Affinität und Stabilität eines neuen ETAR-affinen Fluorochroms. ROFO-FORTSCHR RONTG 2008. [DOI: 10.1055/s-2008-1073995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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30
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Waldeck J, Häger F, Hoeltke C, Lanckohr C, Heindel W, Theilmeier G, Schäfers M, Bremer C. Targeting von alpha-v-beta-3-Expression in atherosklerotischen Plaques mithilfe eines RGD-Cy 5.5 Konjugates. ROFO-FORTSCHR RONTG 2008. [DOI: 10.1055/s-2008-1073996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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31
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Persigehl T, Kellert J, Wall A, Matuszewski L, Winkelmann S, Heindel W, Bremer C, Dahnke H. Bestimmung des Tumor-Blutvolumens mittels einer optimierten MR-Relaxometrie Multi-Gradienten Echo Sequenz. ROFO-FORTSCHR RONTG 2008. [DOI: 10.1055/s-2008-1073685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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32
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Buerke B, Bremer C, Evers S, Allkemper T, Vogt M, Heindel W, Tombach B. CE-MRA der supraaortalen Arterien mit Gadovist®, Omniscan®, Multihance® and Vasovist®: Ergebnisse einer prospektiven intraindividuellen Vergleichsstudie. ROFO-FORTSCHR RONTG 2008. [DOI: 10.1055/s-2008-1073679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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33
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Faust A, Waschkau B, Waldeck J, Breyholz HJ, Kopka K, Heindel W, Schäfers M, Bremer C. Synthese und Evaluation von fluoreszenzmarkierten Barbituraten für die optische Bildgebung von Matrix-Metalloproteinasen. ROFO-FORTSCHR RONTG 2008. [DOI: 10.1055/s-2008-1073994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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34
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Buerke B, Allkemper T, Kugel H, Bremer C, Heindel W, Tombach B. Wie verlässlich sind Signalintensitätsmessungen in der mit Software-Algorithmen (CLEAR) nachverarbeiteten CE-MRA? ROFO-FORTSCHR RONTG 2008. [DOI: 10.1055/s-2008-1073532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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35
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Eisenblätter M, Vogl T, Heindel W, Roth J, Bremer C. MRP-Targetting in toxischem Kontaktekzem – ein Mausmodell zur Bildgebung der Entzündung in vivo. ROFO-FORTSCHR RONTG 2008. [DOI: 10.1055/s-2008-1073740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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36
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Thoennissen NH, Schliemann C, Brunnberg U, Schmidt E, Staebler A, Stegger L, Bremer C, Schleicher C, Mesters RM, Müller-Tidow C, Berdel WE. Chemotherapy in metastatic malignant triton tumor: report on two cases. Oncol Rep 2007; 18:763-7. [PMID: 17786333 DOI: 10.3892/or.18.4.763] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Malignant triton tumor (MTT) is a rare, highly malignant nerve sheath tumor with rhabdomyoblastic differentiation. Initial debulking surgery followed by adjuvant therapy is the current treatment of choice, but has very limited efficacy when optimal cytoreduction is not achieved by surgical procedure. Neoadjuvant therapy for MTT, to potentially facilitate subsequent surgery, eradicate micrometastatic lesions and, therefore, improve the therapeutical outcome, has never before been presented in literature. Here, we report on the multimodal management of two cases of advanced and metastatic MTT. Treatment modalities involved neoadjuvant and adjuvant chemotherapy, surgical resection, and radiation. In both cases, integrated Positron Emission Tomography/Computed Tomography (PET/CT) emerged as an important diagnostic tool for the reliable assessment of MTT response and metabolic remission.
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Affiliation(s)
- N H Thoennissen
- Department of Medicine, Hematology and Oncology, University of Münster, D-48149 Münster, Germany.
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37
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Abstract
Superparamagnetic iron oxide (SPIO) contrast agents, clinically established for high resolution magnetic resonance imaging of reticuloendothelial system containing anatomical structures, can additionally be exploited for the non-invasive characterization and quantification of pathology down to the molecular level. In this context, SPIOs can be applied for non-invasive cell tracking, quantification of tissue perfusion and target specific imaging, as well as for the detection of gene expression. This article provides an overview of new applications for clinically approved iron oxides as well of new, modified SPIO contrast agents for parametric and molecular imaging.
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Affiliation(s)
- L Matuszewski
- Institut für Klinische Radiologie, Universitätsklinikum Münster.
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38
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Waldeck J, von Wallbrunn A, Hoeltke C, Zühlsdorf M, Heindel W, Schäfers M, Bremer C. In vivo Fluoreszenzbildgebung der CD13/APN-Expression im Tumor-Xenograft. ROFO-FORTSCHR RONTG 2007. [DOI: 10.1055/s-2007-977346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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39
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Buerke B, Allkemper T, Kugel H, Heindel W, Bremer C, Tombach B. Intraindividueller Vergleich von 2 Injektionsraten für die CE-MRA der supraaortalen Arterien mit 1,0M Gadovist®. ROFO-FORTSCHR RONTG 2007. [DOI: 10.1055/s-2007-976918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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40
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Höltke C, von Wallbrunn A, Faust A, Kopka K, Schäfers M, Bremer C. In Vivo Bildgebung der Endothelin-Rezeptor Expression humaner Brustkrebszelllinien in Xenograft-Mausmodellen. ROFO-FORTSCHR RONTG 2007. [DOI: 10.1055/s-2007-977339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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41
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Eisenblätter M, Wall A, Ehrchen J, Heindel W, Roth J, Bremer C. Fluoreszenz-basierte Visualisierung von Leukozyten-Migration in inflammatorische Granulome. ROFO-FORTSCHR RONTG 2007. [DOI: 10.1055/s-2007-976991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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42
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Wülfing P, Smollich M, Meyer V, Götte M, Bremer C, Kiesel L. Therapeutisches Targeting des Endothelin-A-Rezeptors beim Mammakarzinom in vivo. Geburtshilfe Frauenheilkd 2006. [DOI: 10.1055/s-2006-952269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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43
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Bremer C. MRT-Kontrastmittel: Neue Entwicklungen. ROFO-FORTSCHR RONTG 2006. [DOI: 10.1055/s-2006-940453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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44
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Persigehl T, Matuszewski L, Wall AW, Kessler T, Bieker R, Berdel W, Mesters R, Heindel W, Bremer C. Prognostische Wertigkeit der USPIO unterstützten MRT zur Abschätzung anti-angiogener Tumortherapie. ROFO-FORTSCHR RONTG 2006. [DOI: 10.1055/s-2006-940757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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45
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von Wallbrunn AW, Höltke C, Zühlsdorf M, Heindel W, Schäfers M, Bremer C. Fluoreszenz-Bildgebung der αvβ3-Expression mit planaren und tomographischen Methoden. ROFO-FORTSCHR RONTG 2006. [DOI: 10.1055/s-2006-941129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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46
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Wall AW, Licha K, Schirner M, von Wallbrunn AW, Persigehl T, Matuszewski L, Heindel W, Bremer C. Bestimmung des Angioneogenese – Grades in Brustkrebs-Xenograft – Modellen mit einem neuen optischen Kontrastmittel „SIDAG“ (NIR96010). ROFO-FORTSCHR RONTG 2006. [DOI: 10.1055/s-2006-940817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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47
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Matuszewski L, Kuhlpeter R, Dahnke H, Persigehl T, Wall A, Heindel W, Schaeffter T, Bremer C. T2/T2*-Mapping zur Quantifizierung eisenmarkierter Zellen im Hochfeld-MRT. ROFO-FORTSCHR RONTG 2005. [DOI: 10.1055/s-2005-867771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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48
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Persigehl T, Matuszewski L, Wall A, Wülfing C, Tombach B, Mesters R, Heindel W, Bremer C. Abschätzung der Therapieeffektivität antiangiogener Tumortherapien mittels Diffusion-gewichteter MRT. ROFO-FORTSCHR RONTG 2005. [DOI: 10.1055/s-2005-867688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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49
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Abstract
With an increasing understanding of the molecular basis of disease, various new imaging targets have recently been defined that potentially allow for an early, sensitive, and specific diagnosis of disease or monitoring of treatment response. Different approaches to depict these molecular structures in vivo are currently being explored by the molecular imaging community. We briefly review methodologies for molecular imaging by magnetic resonance imaging and optical methods. Special emphasis is put on different contrast agent designs (e.g., targeted and smart probes). New technical developments in optical imaging are briefly discussed. In addition, current research results are put into a clinical perspective to elucidate the potential merits one might expect from this new research field.
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Affiliation(s)
- T Persigehl
- Department of Clinical Radiology, University Hospital Muenster, Albert-Schweitzer-Str. 33, D-48129 Münster, Germany
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50
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Matuszewski CC, Persigehl T, Wall A, Meier N, Kessler T, Tombach B, Mesters R, Heindel W, Bremer C. Abschätzung der medullären Angiogenese im Rahmen der akuten myeloischen Leukämie (AML) mithilfe der Eisenoxid-unterstützten KM-MRT. ROFO-FORTSCHR RONTG 2005. [DOI: 10.1055/s-2005-864014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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