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Suver C, Harper J, Loomba J, Saltz M, Solway J, Anzalone AJ, Walters K, Pfaff E, Walden A, McMurry J, Chute CG, Haendel M. The N3C governance ecosystem: A model socio-technical partnership for the future of collaborative analytics at scale. J Clin Transl Sci 2023; 7:e252. [PMID: 38229902 PMCID: PMC10789985 DOI: 10.1017/cts.2023.681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/22/2023] [Accepted: 11/06/2023] [Indexed: 01/18/2024] Open
Abstract
The National COVID Cohort Collaborative (N3C) is a public-private-government partnership established during the Coronavirus pandemic to create a centralized data resource called the "N3C data enclave." This resource contains individual-level health data from participating healthcare sites nationwide to support rapid collaborative analytics. N3C has enabled analytics within a cloud-based enclave of data from electronic health records from over 17 million people (with and without COVID-19) in the USA. To achieve this goal of a shared data resource, N3C implemented a shared governance strategy involving stakeholders in decision-making. The approach leveraged best practices in data stewardship and team science to rapidly enable COVID-19-related research at scale while respecting the privacy of data subjects and participating institutions. N3C balanced equitable access to data, team-based scientific productivity, and individual professional recognition - a key incentive for academic researchers. This governance approach makes N3C research sustainable and effective beyond the initial days of the pandemic. N3C demonstrated that shared governance can overcome traditional barriers to data sharing without compromising data security and trust. The governance innovations described herein are a helpful framework for other privacy-preserving data infrastructure programs and provide a working model for effective team science beyond COVID-19.
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Affiliation(s)
- Christine Suver
- Research Governance & Ethics, Sage Bionetworks, Seattle, WA, USA
| | | | - Johanna Loomba
- Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Mary Saltz
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
| | - Julian Solway
- Institute for Translational Medicine, University of Chicago, Chicago, IL, USA
| | - Alfred Jerrod Anzalone
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | | | - Emily Pfaff
- University of North Carolina, Chapel Hill, NC, USA
| | - Anita Walden
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Julie McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Hill EL, Mehta HB, Sharma S, Mane K, Singh SK, Xie C, Cathey E, Loomba J, Russell S, Spratt H, DeWitt PE, Ammar N, Madlock-Brown C, Brown D, McMurry JA, Chute CG, Haendel MA, Moffitt R, Pfaff ER, Bennett TD. Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study. BMC Public Health 2023; 23:2103. [PMID: 37880596 PMCID: PMC10601201 DOI: 10.1186/s12889-023-16916-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 10/05/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. METHODS This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.
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Affiliation(s)
- Elaine L Hill
- Department of Public Health Sciences, University of Rochester Medical Center, 265 Crittenden Boulevard Box 420644, Rochester, NY, 14642, USA.
| | - Hemalkumar B Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA.
| | - Suchetha Sharma
- School of Data Science, University of Virginia, 3 Elliewood Ave, Charlottesville, VA, 22903, USA
| | - Klint Mane
- Department of Economics, University of Rochester, 1232 Mount Hope Ave, Rochester, NY, 14620, USA
| | - Sharad Kumar Singh
- Goergen Institute for Data Science, University of Rochester, 1209 Wegmans Hall, Rochester, NY, 14627, USA
| | - Catherine Xie
- CMC BOX 275184, University of Rochester, 500 Joseph C. Wilson Blvd, Rochester, NY, 14627-5184, USA
| | - Emily Cathey
- Ivy Foundations Building, Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 560 Ray C Hunt Drive RM 2153, Charlottesville, VA, 22903, USA
| | - Johanna Loomba
- Ivy Foundations Building, Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 560 Ray C Hunt Drive RM 2153, Charlottesville, VA, 22903, USA
| | - Seth Russell
- Department of Pediatrics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, Medical Branch, University of Texas, 301 University Blvd, Galveston, TX, 77555-1148, USA
| | - Peter E DeWitt
- Department of Pediatrics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
| | - Nariman Ammar
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 50 N Dunlap St., Memphis, TN, 38103, USA
| | - Charisse Madlock-Brown
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 930 Madison Avenue 6Th Floor, Memphis, TN, 38163, USA
| | - Donald Brown
- Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 151 Engineer's Way Olsson Hall Rm. 102E, PO Box 400747, Charlottesville, VA, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado School of Medicine, 12800 East 19Th Avenue, Aurora, CO, 80045, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, 2024 E Monument St. , Baltimore, MD, 21287, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, East 17Th Place Campus Box C290, Aurora, CO, 1300180045, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, and Stony Brook Cancer Center, Stony Brook, NY, MART L7 081011794, USA
| | - Emily R Pfaff
- Department of Medicine, North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, 160 N Medical Drive, Chapel Hill, NC, 27599, USA
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
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L Mandel H, Colleen G, Abedian S, Ammar N, Charles Bailey L, Bennett TD, Daniel Brannock M, Brosnahan SB, Chen Y, Chute CG, Divers J, Evans MD, Haendel M, Hall MA, Hirabayashi K, Hornig M, Katz SD, Krieger AC, Loomba J, Lorman V, Mazzotti DR, McMurry J, Moffitt RA, Pajor NM, Pfaff E, Radwell J, Razzaghi H, Redline S, Seibert E, Sekar A, Sharma S, Thaweethai T, Weiner MG, Jae Yoo Y, Zhou A, Thorpe LE. Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative. Sleep 2023; 46:zsad126. [PMID: 37166330 PMCID: PMC10485569 DOI: 10.1093/sleep/zsad126] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/20/2023] [Indexed: 05/12/2023] Open
Abstract
STUDY OBJECTIVES Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC). METHODS We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities. RESULTS Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis. CONCLUSIONS Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae.
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Affiliation(s)
- Hannah L Mandel
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Gunnar Colleen
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Sajjad Abedian
- Information Technologies and Services Department, Weill Cornell Medicine, New York, NY, USA
| | - Nariman Ammar
- Department of Pediatrics, University of Tennessee Health Science Center College of Medicine Memphis, Memphis, TN, USA
| | - L Charles Bailey
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Tellen D Bennett
- Department of Pediatrics, Children’s Hospital Colorado, Aurora, CO, USA
| | | | - Shari B Brosnahan
- Division of Pulmonary, Department of Medicine, Critical Care and Sleep Medicine, NYU Langone Health, New York, NY, USA¸
| | - Yu Chen
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Christopher G Chute
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jasmin Divers
- Department of Foundations of Medicine, New York University Long Island School of Medicine, Mineola, NY, USA
| | - Michael D Evans
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
| | - Melissa Haendel
- Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Margaret A Hall
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Kathryn Hirabayashi
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mady Hornig
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Stuart D Katz
- Leon H. Charney Division of Cardiology, Department of Medicine, NYU Langone Health, New York, NY, USA
| | - Ana C Krieger
- Departments of Medicine, Neurology, and Genetic Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Johanna Loomba
- Integrated Translational Health Research Institute, University of Virginia, Charlottesville, VA, USA
| | - Vitaly Lorman
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Diego R Mazzotti
- Division of Pulmonary Critical Care and Sleep Medicine, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | - Julie McMurry
- Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Nathan M Pajor
- Division of Pulmonary Medicine Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Emily Pfaff
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Jeff Radwell
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Hanieh Razzaghi
- Applied Clinical Research Center, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | | | | | - Suchetha Sharma
- Integrated Translational Health Research Institute, University of Virginia, Charlottesville, VA, USA
| | - Tanayott Thaweethai
- Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Mark G Weiner
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Yun Jae Yoo
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Andrea Zhou
- Integrated Translational Health Research Institute, University of Virginia, Charlottesville, VA, USA
| | - Lorna E Thorpe
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
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4
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Pfaff ER, Madlock-Brown C, Baratta JM, Bhatia A, Davis H, Girvin A, Hill E, Kelly E, Kostka K, Loomba J, McMurry JA, Wong R, Bennett TD, Moffitt R, Chute CG, Haendel M. Coding long COVID: characterizing a new disease through an ICD-10 lens. BMC Med 2023; 21:58. [PMID: 36793086 PMCID: PMC9931566 DOI: 10.1186/s12916-023-02737-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 01/13/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. RESULTS We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. CONCLUSIONS This work offers insight into potential subtypes and current practice patterns around long COVID and speaks to the existence of disparities in the diagnosis of patients with long COVID. This latter finding in particular requires further research and urgent remediation.
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Affiliation(s)
- Emily R Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, USA.
| | | | - John M Baratta
- University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Abhishek Bhatia
- University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Hannah Davis
- Patient-Led Research Collaborative, New York, USA
| | | | | | - Elizabeth Kelly
- University of North Carolina at Chapel Hill, Chapel Hill, USA
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5
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Hadley E, Yoo YJ, Patel S, Zhou A, Laraway B, Wong R, Preiss A, Chew R, Davis H, Chute CG, Pfaff ER, Loomba J, Haendel M, Hill E, Moffitt R. SARS-CoV-2 Reinfection is Preceded by Unique Biomarkers and Related to Initial Infection Timing and Severity: an N3C RECOVER EHR-Based Cohort Study. medRxiv 2023:2023.01.03.22284042. [PMID: 36656776 PMCID: PMC9844020 DOI: 10.1101/2023.01.03.22284042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer's V: 0.18, DoF = 4).
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Affiliation(s)
| | | | | | - Andrea Zhou
- University of Virginia, Charlottesville, VA, US
| | | | | | | | - Rob Chew
- RTI International, Durham, NC, US
| | - Hannah Davis
- RECOVER Patient Led Research Collaborative (PLRC), US
| | | | | | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Denver, CO, US
| | - Elaine Hill
- University of Rochester Medical Center, Rochester, NY, US
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6
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Jiang S, Loomba J, Sharma S, Brown D. Vital Measurements of Hospitalized COVID-19 Patients as a Predictor of Long COVID: An EHR-based Cohort Study from the RECOVER Program in N3C. ArXiv 2022:arXiv:2211.08485v1. [PMID: 36415203 PMCID: PMC9681042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of Long COVID could play an important role in understanding the characteristic of this new disease. Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could reflect body's most basic functions and are measured regularly during hospitalization, so among patients diagnosed COVID positive and hospitalized, we analyze the vital measurements of first 7 days since the hospitalization start date to study the pattern of the vital measurements and predict Long COVID with the information from vital measurements.
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Affiliation(s)
- Sihang Jiang
- Department of Engineering of Systems and Environment, University of Virginia
| | - Johanna Loomba
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia
| | | | - Donald Brown
- Department of Engineering of Systems and Environment, University of Virginia
- School of Data Science, University of Virginia
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7
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Sharma P, Shah K, Loomba J, Patel A, Mallawaarachchi I, Blazek O, Ratcliffe S, Breathett K, Johnson AE, Taylor AM, Salerno M, Ragosta M, Sodhi N, Addison D, Mohammed S, Bilchick KC, Mazimba S. The impact of COVID-19 on clinical outcomes among acute myocardial infarction patients undergoing early invasive treatment strategy. Clin Cardiol 2022; 45:1070-1078. [PMID: 36040721 PMCID: PMC9538930 DOI: 10.1002/clc.23908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 07/29/2022] [Accepted: 08/15/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND The implications of coronavirus disease 2019 (COVID-19) infection on outcomes after invasive therapeutic strategies among patients presenting with acute myocardial infarction (AMI) are not well studied. HYPOTHESIS To assess the outcomes of COVID-19 patients presenting with AMI undergoing an early invasive treatment strategy. METHODS This study was a cross-sectional, retrospective analysis of the National COVID Cohort Collaborative database including all patients presenting with a recorded diagnosis of AMI (ST-elevation myocardial infarction (MI) and non-ST elevation MI). COVID-19 positive patients with AMI were stratified into one of four groups: (1a) patients who had a coronary angiogram with percutaneous coronary intervention (PCI) within 3 days of their AMI; (1b) PCI within 3 days of AMI with coronary artery bypass graft (CABG) within 30 days; (2a) coronary angiogram without PCI and without CABG within 30 days; and (2b) coronary angiogram with CABG within 30 days. The main outcomes were respiratory failure, cardiogenic shock, prolonged length of stay, rehospitalization, and death. RESULTS There were 10 506 COVID-19 positive patients with a diagnosis of AMI. COVID-19 positive patients with PCI had 8.2 times higher odds of respiratory failure than COVID-19 negative patients (p = .001). The odds of prolonged length of stay were 1.7 times higher in COVID-19 patients who underwent PCI (p = .024) and 1.9 times higher in patients who underwent coronary angiogram followed by CABG (p = .001). CONCLUSION These data demonstrate that COVID-19 positive patients with AMI undergoing early invasive coronary angiography had worse outcomes than COVID-19 negative patients.
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Affiliation(s)
- Prerna Sharma
- Division of Cardiovascular MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
| | - Kajal Shah
- Department of Internal MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
| | - Johanna Loomba
- Integrated Translational Health Research Institute (iTHRIV)University of VirginiaCharlottesvilleVirginiaUSA
| | - Arti Patel
- Integrated Translational Health Research Institute (iTHRIV)University of VirginiaCharlottesvilleVirginiaUSA
| | | | - Olivia Blazek
- Department of Internal MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
- Division of CardiologyUniversity of Connecticut—Hartford HospitalMansfieldConnecticutUSA
| | - Sarah Ratcliffe
- Department of Public Health SciencesUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Khadijah Breathett
- Division of CardiologyUniversity of Arizona Medical CenterTucsonArizonaUSA
| | - Amber E. Johnson
- Division of Cardiology, University of Pittsburgh Medical CenterPittsburghPennsylvaniaUSA
| | - Angela M. Taylor
- Division of Cardiovascular MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
| | - Michael Salerno
- Division of Cardiovascular MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
| | - Michael Ragosta
- Division of Cardiovascular MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
| | - Nishtha Sodhi
- Division of Cardiovascular MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
| | - Daniel Addison
- Division of Cardiology, Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - Selma Mohammed
- Division of CardiologyCreighton University School of MedicineOmahaNebraskaUSA
| | - Kenneth C. Bilchick
- Division of Cardiovascular MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
| | - Sula Mazimba
- Division of Cardiovascular MedicineUniversity of Virginia Medical CenterCharlottesvilleVirginiaUSA
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8
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Pfaff ER, Madlock-Brown C, Baratta JM, Bhatia A, Davis H, Girvin A, Hill E, Kelly L, Kostka K, Loomba J, McMurry JA, Wong R, Bennett TD, Moffitt R, Chute CG, Haendel M. Coding Long COVID: Characterizing a new disease through an ICD-10 lens. medRxiv 2022:2022.04.18.22273968. [PMID: 36093345 PMCID: PMC9460974 DOI: 10.1101/2022.04.18.22273968] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes Long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of Long COVID are still in flux, and the deployment of an ICD-10-CM code for Long COVID in the US took nearly two years after patients had begun to describe their condition. Here we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." Methods We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code ( n = 21,072), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan. Results We established the diagnoses most commonly co-occurring with U09.9, and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty, high education, and high access to medical care. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients. Conclusions This work offers insight into potential subtypes and current practice patterns around Long COVID, and speaks to the existence of disparities in the diagnosis of patients with Long COVID. This latter finding in particular requires further research and urgent remediation.
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Affiliation(s)
| | | | | | | | | | | | | | - Liz Kelly
- University of North Carolina at Chapel Hill
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9
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Hill E, Mehta H, Sharma S, Mane K, Xie C, Cathey E, Loomba J, Russell S, Spratt H, DeWitt PE, Ammar N, Madlock-Brown C, Brown D, McMurry JA, Chute CG, Haendel MA, Moffitt R, Pfaff ER, Bennett TD. Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program. medRxiv 2022:2022.08.15.22278603. [PMID: 36032983 PMCID: PMC9413724 DOI: 10.1101/2022.08.15.22278603] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). Objective To identify risk factors associated with PASC/long-COVID. Design Retrospective case-control study. Setting 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). Patients 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system. Measurements Risk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results Among 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. Conclusions This national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.
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Affiliation(s)
- Elaine Hill
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Hemal Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Suchetha Sharma
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Klint Mane
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Catherine Xie
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Emily Cathey
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Johanna Loomba
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Seth Russell
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX, USA
| | - Peter E DeWitt
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Nariman Ammar
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Charisse Madlock-Brown
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Donald Brown
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, and Stony Brook Cancer Center, Stony Brook, NY, USA
| | - Emily R Pfaff
- Department of Medicine, North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tellen D Bennett
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
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10
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Pfaff ER, Girvin AT, Gabriel DL, Kostka K, Morris M, Palchuk MB, Lehmann HP, Amor B, Bissell M, Bradwell KR, Gold S, Hong SS, Loomba J, Manna A, McMurry JA, Niehaus E, Qureshi N, Walden A, Zhang XT, Zhu RL, Moffitt RA, Haendel MA, Chute CG, Adams WG, Al-Shukri S, Anzalone A, Baghal A, Bennett TD, Bernstam EV, Bernstam EV, Bissell MM, Bush B, Campion TR, Castro V, Chang J, Chaudhari DD, Chen W, Chu S, Cimino JJ, Crandall KA, Crooks M, Davies SJD, DiPalazzo J, Dorr D, Eckrich D, Eltinge SE, Fort DG, Golovko G, Gupta S, Haendel MA, Hajagos JG, Hanauer DA, Harnett BM, Horswell R, Huang N, Johnson SG, Kahn M, Khanipov K, Kieler C, Luzuriaga KRD, Maidlow S, Martinez A, Mathew J, McClay JC, McMahan G, Melancon B, Meystre S, Miele L, Morizono H, Pablo R, Patel L, Phuong J, Popham DJ, Pulgarin C, Santos C, Sarkar IN, Sazo N, Setoguchi S, Soby S, Surampalli S, Suver C, Vangala UMR, Visweswaran S, von Oehsen J, Walters KM, Wiley L, Williams DA, Zai A. Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative. J Am Med Inform Assoc 2022; 29:609-618. [PMID: 34590684 PMCID: PMC8500110 DOI: 10.1093/jamia/ocab217] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/19/2021] [Accepted: 09/23/2021] [Indexed: 02/01/2023] Open
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.
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Affiliation(s)
- Emily R Pfaff
- Department of Medicine, UNC Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | | | - Davera L Gabriel
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kristin Kostka
- The OHDSI Center at the Roux Institute, Northeastern University, Portland, Maine, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Harold P Lehmann
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | | | | | | | - Sigfried Gold
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Stephanie S Hong
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Amin Manna
- Palantir Technologies, Denver, Colorado, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | | | - Anita Walden
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Richard L Zhu
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Melissa A Haendel
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
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11
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Miller TW, Traphagen NA, Li J, Lewis LD, Lopes B, Asthagiri A, Loomba J, De Jong J, Schiff D, Patel SH, Purow BW, Fadul CE. Tumor pharmacokinetics and pharmacodynamics of the CDK4/6 inhibitor ribociclib in patients with recurrent glioblastoma. J Neurooncol 2019; 144:563-572. [PMID: 31399936 DOI: 10.1007/s11060-019-03258-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 08/02/2019] [Indexed: 01/05/2023]
Abstract
INTRODUCTION We conducted a phase Ib study (NCT02345824) to determine whether ribociclib, an inhibitor of cyclin-dependent kinases 4 and 6 (CDK4/6), penetrates tumor tissue and modulates downstream signaling pathways including retinoblastoma protein (Rb) in patients with recurrent glioblastoma (GBM). METHODS Study participants received ribociclib (600 mg QD) for 8-21 days before surgical resection of their recurrent GBM. Total and unbound concentrations of ribociclib were measured in samples of tumor tissue, plasma, and cerebrospinal fluid (CSF). We analyzed tumor specimens obtained from the first (initial/pre-study) and second (recurrent/on-study) surgery by immunohistochemistry for Rb status and downstream signaling of CDK4/6 inhibition. Participants with Rb-positive recurrent tumors continued ribociclib treatment on a 21-day-on, 7-day-off schedule after surgery, and were monitored for toxicity and disease progression. RESULTS Three participants with recurrent Rb-positive GBM participated in this study. Mean unbound (pharmacologically active) ribociclib concentrations in plasma, CSF, MRI-enhancing, MRI-non-enhancing, and tumor core regions were 0.337 μM, 0.632 μM, 1.242 nmol/g, 0.484 nmol/g, and 1.526 nmol/g, respectively, which exceeded the in vitro IC50 (0.04 μM) for inhibition of CDK4/6 in cell-free assay. Modulation of pharmacodynamic markers of ribociclib CDK 4/6 inhibition in tumor tissues were inconsistent between study participants. No participants experienced serious adverse events, but all experienced early disease progression. CONCLUSIONS This study suggests that ribociclib penetrated recurrent GBM tissue at concentrations predicted to be therapeutically beneficial. Our study was unable to demonstrate tumor pharmacodynamic correlates of drug activity. Although well tolerated, ribociclib monotherapy seemed ineffective for the treatment of recurrent GBM.
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Affiliation(s)
- Todd W Miller
- Department of Molecular & Systems Biology, Norris Cotton Cancer Center, Geisel School of Medicine At Dartmouth, Lebanon, NH, USA
| | - Nicole A Traphagen
- Department of Molecular & Systems Biology, Norris Cotton Cancer Center, Geisel School of Medicine At Dartmouth, Lebanon, NH, USA
| | - Jing Li
- Pharmacology Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI, USA
| | - Lionel D Lewis
- Section of Clinical Pharmacology, Department of Medicine, Norris Cotton Cancer Center, Geisel School of Medicine At Dartmouth, Lebanon, NH, USA
| | - Beatriz Lopes
- Department of Pathology, Divisions of Neuropathology and Molecular Diagnostics, University of Virginia Health System, Charlottesville, VA, USA
| | - Ashok Asthagiri
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, VA, USA
| | - Johanna Loomba
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, VA, USA
| | - Jenny De Jong
- Department of Neurological Surgery, University of Virginia Health System, Charlottesville, VA, USA
| | - David Schiff
- Department of Neurology, Division of Neuro-Oncology, University of Virginia Health System, P.O. Box 800432, Charlottesville, VA, 22908, USA
| | - Sohil H Patel
- Department of Radiology and Medical Imaging, Division of Neuroradiology, University of Virginia Health System, Charlottesville, VA, USA
| | - Benjamin W Purow
- Department of Neurology, Division of Neuro-Oncology, University of Virginia Health System, P.O. Box 800432, Charlottesville, VA, 22908, USA
| | - Camilo E Fadul
- Department of Neurology, Division of Neuro-Oncology, University of Virginia Health System, P.O. Box 800432, Charlottesville, VA, 22908, USA.
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12
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Elias WJ, Huss D, Voss T, Loomba J, Khaled M, Zadicario E, Frysinger RC, Sperling SA, Wylie S, Monteith SJ, Druzgal J, Shah BB, Harrison M, Wintermark M. A pilot study of focused ultrasound thalamotomy for essential tremor. N Engl J Med 2013; 369:640-8. [PMID: 23944301 DOI: 10.1056/nejmoa1300962] [Citation(s) in RCA: 561] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
BACKGROUND Recent advances have enabled delivery of high-intensity focused ultrasound through the intact human cranium with magnetic resonance imaging (MRI) guidance. This preliminary study investigates the use of transcranial MRI-guided focused ultrasound thalamotomy for the treatment of essential tremor. METHODS From February 2011 through December 2011, in an open-label, uncontrolled study, we used transcranial MRI-guided focused ultrasound to target the unilateral ventral intermediate nucleus of the thalamus in 15 patients with severe, medication-refractory essential tremor. We recorded all safety data and measured the effectiveness of tremor suppression using the Clinical Rating Scale for Tremor to calculate the total score (ranging from 0 to 160), hand subscore (primary outcome, ranging from 0 to 32), and disability subscore (ranging from 0 to 32), with higher scores indicating worse tremor. We assessed the patients' perceptions of treatment efficacy with the Quality of Life in Essential Tremor Questionnaire (ranging from 0 to 100%, with higher scores indicating greater perceived disability). RESULTS Thermal ablation of the thalamic target occurred in all patients. Adverse effects of the procedure included transient sensory, cerebellar, motor, and speech abnormalities, with persistent paresthesias in four patients. Scores for hand tremor improved from 20.4 at baseline to 5.2 at 12 months (P=0.001). Total tremor scores improved from 54.9 to 24.3 (P=0.001). Disability scores improved from 18.2 to 2.8 (P=0.001). Quality-of-life scores improved from 37% to 11% (P=0.001). CONCLUSIONS In this pilot study, essential tremor improved in 15 patients treated with MRI-guided focused ultrasound thalamotomy. Large, randomized, controlled trials will be required to assess the procedure's efficacy and safety. (Funded by the Focused Ultrasound Surgery Foundation; ClinicalTrials.gov number, NCT01304758.).
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Affiliation(s)
- W Jeffrey Elias
- Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA 22908, USA.
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