1
|
Klein KR, Abrahamsen TJ, Kahkoska AR, Alexander GC, Chute CG, Haendel M, Hong SS, Mehta H, Moffitt R, Stürmer T, Kvist K, Buse JB. Association of Premorbid GLP-1RA and SGLT-2i Prescription Alone and in Combination with COVID-19 Severity. Diabetes Ther 2024; 15:1169-1186. [PMID: 38536629 PMCID: PMC11043305 DOI: 10.1007/s13300-024-01562-1] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/04/2024] [Indexed: 04/26/2024] Open
Abstract
INTRODUCTION People with type 2 diabetes are at heightened risk for severe outcomes related to COVID-19 infection, including hospitalization, intensive care unit admission, and mortality. This study was designed to examine the impact of premorbid use of glucagon-like peptide-1 receptor agonist (GLP-1RA) monotherapy, sodium-glucose cotransporter-2 inhibitor (SGLT-2i) monotherapy, and concomitant GLP1-RA/SGLT-2i therapy on the severity of outcomes in individuals with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. METHODS Utilizing observational data from the National COVID Cohort Collaborative through September 2022, we compared outcomes in 78,806 individuals with a prescription of GLP-1RA and SGLT-2i versus a prescription of dipeptidyl peptidase 4 inhibitors (DPP-4i) within 24 months of a positive SARS-CoV-2 PCR test. We also compared concomitant GLP-1RA/SGLT-2i therapy to GLP-1RA and SGLT-2i monotherapy. The primary outcome was 60-day mortality, measured from the positive test date. Secondary outcomes included emergency room (ER) visits, hospitalization, and mechanical ventilation within 14 days. Using a super learner approach and accounting for baseline characteristics, associations were quantified with odds ratios (OR) estimated with targeted maximum likelihood estimation (TMLE). RESULTS Use of GLP-1RA (OR 0.64, 95% confidence interval [CI] 0.56-0.72) and SGLT-2i (OR 0.62, 95% CI 0.57-0.68) were associated with lower odds of 60-day mortality compared to DPP-4i use. Additionally, the OR of ER visits and hospitalizations were similarly reduced with GLP1-RA and SGLT-2i use. Concomitant GLP-1RA/SGLT-2i use showed similar odds of 60-day mortality when compared to GLP-1RA or SGLT-2i use alone (OR 0.92, 95% CI 0.81-1.05 and OR 0.88, 95% CI 0.76-1.01, respectively). However, lower OR of all secondary outcomes were associated with concomitant GLP-1RA/SGLT-2i use when compared to SGLT-2i use alone. CONCLUSION Among adults who tested positive for SARS-CoV-2, premorbid use of either GLP-1RA or SGLT-2i is associated with lower odds of mortality compared to DPP-4i. Furthermore, concomitant use of GLP-1RA and SGLT-2i is linked to lower odds of other severe COVID-19 outcomes, including ER visits, hospitalizations, and mechanical ventilation, compared to SGLT-2i use alone. Graphical abstract available for this article.
Collapse
Affiliation(s)
- Klara R Klein
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Campus Box #7172, 8072 Burnett Womack, 160 Dental Circle, Chapel Hill, NC, 27599, USA.
| | | | - Anna R Kahkoska
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Campus Box #7172, 8072 Burnett Womack, 160 Dental Circle, Chapel Hill, NC, 27599, USA
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - G Caleb Alexander
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Division of General Internal Medicine, Johns Hopkins Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Stephanie S Hong
- Division of General Internal Medicine, Johns Hopkins Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Hemalkumar Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - John B Buse
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Campus Box #7172, 8072 Burnett Womack, 160 Dental Circle, Chapel Hill, NC, 27599, USA
| |
Collapse
|
2
|
Preiss A, Bhatia A, Aragon LV, Baratta JM, Baskaran M, Blancero F, Brannock MD, Chew RF, Díaz I, Fitzgerald M, Kelly EP, Zhou A, Carton TW, Chute CG, Haendel M, Moffitt R, Pfaff E. EFFECT OF PAXLOVID TREATMENT DURING ACUTE COVID-19 ON LONG COVID ONSET: AN EHR-BASED TARGET TRIAL EMULATION FROM THE N3C AND RECOVER CONSORTIA. medRxiv 2024:2024.01.20.24301525. [PMID: 38343863 PMCID: PMC10854326 DOI: 10.1101/2024.01.20.24301525] [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: 02/17/2024]
Abstract
Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. In this study, we examined whether treatment with Paxlovid in the acute phase of COVID-19 helps prevent the onset of PASC. We used electronic health records from the National Covid Cohort Collaborative (N3C) to define a cohort of 426,352 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation (TTE) framework to estimate the effect of Paxlovid treatment on PASC incidence. We estimated overall PASC incidence using a computable phenotype. We also measured the onset of novel cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment did not have a significant effect on overall PASC incidence (relative risk [RR] = 0.98, 95% confidence interval [CI] 0.95-1.01). However, it had a protective effect on cognitive (RR = 0.90, 95% CI 0.84-0.96) and fatigue (RR = 0.95, 95% CI 0.91-0.98) symptom clusters, which suggests that the etiology of these symptoms may be more closely related to viral load than that of respiratory symptoms.
Collapse
Affiliation(s)
| | - Abhishek Bhatia
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - John M. Baratta
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Monika Baskaran
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | | | - Iván Díaz
- New York University Grossman School of Medicine, New York, NY, USA
| | | | | | - Andrea Zhou
- University of Virginia, Charlottesville, VA, USA
| | - Thomas W. Carton
- Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Christopher G. Chute
- Johns Hopkins University School of Medicine, Public Health, and Nursing, Baltimore, MD, USA
| | - Melissa Haendel
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Emily Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
3
|
Sun J, Zheng Q, Anzalone AJ, Abraham AG, Olex AL, Zhang Y, Mathew J, Safdar N, Haendel MA, Segev D, Islam JY, Singh JA, Mannon RB, Chute CG, Patel RC, Kirk GD. Effectiveness of mRNA Booster Vaccine Against Coronavirus Disease 2019 Infection and Severe Outcomes Among Persons With and Without Immune Dysfunction: A Retrospective Cohort Study of National Electronic Medical Record Data in the United States. Open Forum Infect Dis 2024; 11:ofae019. [PMID: 38379569 PMCID: PMC10878052 DOI: 10.1093/ofid/ofae019] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 01/09/2024] [Indexed: 02/22/2024] Open
Abstract
Background Real-world evidence of coronavirus disease 2019 (COVID-19) messenger RNA (mRNA) booster effectiveness among patients with immune dysfunction are limited. Methods We included data from patients in the United States National COVID Cohort Collaborative (N3C) who completed ≥2 doses of mRNA vaccination between 10 December 2020 and 27 May 2022. Immune dysfunction conditions included human immunodeficiency virus infection, solid organ or bone marrow transplant, autoimmune diseases, and cancer. We defined incident COVID-19 BTI as positive results from laboratory tests or diagnostic codes 14 days after at least 2 doses of mRNA vaccination; and severe COVID-19 BTI as hospitalization, invasive cardiopulmonary support, and/or death. We used propensity scores to match boosted versus nonboosted patients and evaluated hazards of incident and severe COVID-19 BTI using Cox regression after matching. Results Among patients without immune dysfunction, the relative effectiveness of booster (3 doses) after 6 months from the primary (2 doses) vaccination against BTI ranged from 69% to 81% during the Delta-predominant period and from 33% to 39% during the Omicron-predominant period. Relative effectiveness against BTI was lower among patients with immune dysfunction but remained statistically significant in both periods. Boosted patients had lower risk of COVID-19-related hospitalization (hazard ratios [HR] ranged from 0.5 [95% confidence interval {CI}, .48-.53] to 0.63 [95% CI, .56-.70]), invasive cardiopulmonary support, or death (HRs ranged from 0.46 [95% CI, .41-.52] to 0.63 [95% CI, .50-.79]) during both periods. Conclusions Booster vaccines remain effective against severe COVID-19 BTI throughout the Delta- and Omicron-predominant periods, regardless of patients' immune status.
Collapse
Affiliation(s)
- Jing Sun
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Qulu Zheng
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Alfred J Anzalone
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Alison G Abraham
- Department of Epidemiology, University of Colorado, Anschutz Medical Campus, Denver, Colorado, USA
| | - Amy L Olex
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Yifan Zhang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jomol Mathew
- Department of Population Health Sciences, University of Wisconsin–Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Nasia Safdar
- Department of Medicine, University of Wisconsin–Madison, Madison, Wisconsin, USA
- Division of Infectious Diseases, William S. Middleton Veterans Affairs Hospital, Madison, Wisconsin, USA
| | - Melissa A Haendel
- Center for Health Artificial Intelligence, University of Colorado, Denver, Colorado, USA
| | - Dorry Segev
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Jessica Y Islam
- Center for Immunization and Infection in Cancer, Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
- Department of Oncologic Sciences, University of South Florida, Tampa, Florida, USA
| | - Jasvinder A Singh
- Department of Medicine and Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Roslyn B Mannon
- Division of Nephrology, Department of Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Rena C Patel
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Gregory D Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| |
Collapse
|
4
|
Pearson TA, Vitalis D, Pratt C, Campo R, Armoundas AA, Au D, Beech B, Brazhnik O, Chute CG, Davidson KW, Diez-Roux AV, Fine LJ, Gabriel D, Groenveld P, Hall J, Hamilton AB, Hu H, Ji H, Kind A, Kraus WE, Krumholz H, Mensah GA, Merchant RM, Mozaffarian D, Murray DM, Neumark-Sztainer D, Petersen M, Goff D. The Science of Precision Prevention: Research Opportunities and Clinical Applications to Reduce Cardiovascular Health Disparities. JACC Adv 2024; 3:100759. [PMID: 38375059 PMCID: PMC10876066 DOI: 10.1016/j.jacadv.2023.100759] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Precision prevention embraces personalized prevention but includes broader factors such as social determinants of health to improve cardiovascular health. The quality, quantity, precision, and diversity of data relatable to individuals and communities continue to expand. New analytical methods can be applied to these data to create tools to attribute risk, which may allow a better understanding of cardiovascular health disparities. Interventions using these analytic tools should be evaluated to establish feasibility and efficacy for addressing cardiovascular disease disparities in diverse individuals and communities. Training in these approaches is important to create the next generation of scientists and practitioners in precision prevention. This state-of-the-art review is based on a workshop convened to identify current gaps in knowledge and methods used in precision prevention intervention research, discuss opportunities to expand trials of implementation science to close the health equity gaps, and expand the education and training of a diverse precision prevention workforce.
Collapse
Affiliation(s)
- Thomas A. Pearson
- College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida, USA
| | - Debbie Vitalis
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Charlotte Pratt
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Rebecca Campo
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital and Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - David Au
- Center of Innovation for Veteran-Centered and Value-Driven Care, University of Washington, Seattle, Washington, USA
| | - Bettina Beech
- UH Population Health, University of Houston, Houston, Texas, USA
| | - Olga Brazhnik
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Christopher G. Chute
- Johns Hopkins Medicine, Institute for Clinical and Translational Research, Baltimore, Maryland, USA
| | - Karina W. Davidson
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New Hyde Park, New York, USA
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York, USA
| | - Ana V. Diez-Roux
- Urban Health Collaborative, Drexel Dornsife School of Public Health, Philadelphia, Pennsylvania, USA
| | - Lawrence J. Fine
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Davera Gabriel
- Biomedical Informatics and Data Science Section, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Peter Groenveld
- Center for Health Care Transformation and Innovation, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Jaclyn Hall
- Department of Health Outcomes and Biomedical Informatics, Institute for Child Health Policy, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Alison B. Hamilton
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Hui Hu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Heng Ji
- Department of Computer Science, University of Illinois Urbana-Champaign, Champaign, Illinois, USA
| | - Amy Kind
- Center for Health Disparities Research (CHDR), University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - William E. Kraus
- Duke Molecular Physiology Institute, School of Medicine, Duke University, Durham, North Carolina, USA
| | - Harlan Krumholz
- Institute for Social and Policy Studies, of Investigative Medicine and of Public Health (Health Policy), Yale University, New Haven, Connecticut, USA
| | - George A. Mensah
- Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Raina M. Merchant
- Center for Health Care Transformation and Innovation, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Dariush Mozaffarian
- Friedman School of Nutrition Science & Policy, Tufts University, Medford, Massachusetts, USA
| | - David M. Murray
- Office of Disease Prevention, National Institutes of Health, Bethesda, Maryland, USA
| | - Dianne Neumark-Sztainer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
| | - Maya Petersen
- Division of Biostatistics, and UCSF-UC Berkeley Program in Computational Precision Health, School of Public Health, University of California-Berkeley, Berkeley, California, USA
- University of California-San Francisco, San Francisco, California, USA
| | - David Goff
- Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| |
Collapse
|
5
|
Sharathkumar A, Wendt L, Ortman C, Srinivasan R, Chute CG, Chrischilles E, Takemoto CM. COVID-19 outcomes in persons with hemophilia: results from a US-based national COVID-19 surveillance registry. J Thromb Haemost 2024; 22:61-75. [PMID: 37182697 PMCID: PMC10181864 DOI: 10.1016/j.jtha.2023.04.040] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/29/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023]
Abstract
BACKGROUND Hypercoagulable state contributing to thrombotic complications worsens COVID-19 severity and outcomes, whereas anticoagulation improves outcomes by alleviating hypercoagulability. OBJECTIVES To examine whether hemophilia, an inherent hypocoagulable condition, offers protection against COVID-19 severity and reduces venous thromboembolism (VTE) risk in persons with hemophilia (PwH). METHODS A 1:3 propensity score-matched retrospective cohort study used national COVID-19 registry data (January 2020 through January 2022) to compare outcomes between 300 male PwH and 900 matched controls without hemophilia. RESULTS Analyses of PwH demonstrated that known risk factors (older age, heart failure, hypertension, cancer/malignancy, dementia, and renal and liver disease) contributed to severe COVID-19 and/or 30-day all-cause mortality. Non-central nervous system bleeding was an additional risk factor for poor outcomes in PwH. Odds of developing VTE with COVID-19 in PwH were associated with pre-COVID VTE diagnosis (odds ratio [OR], 51.9; 95% CI, 12.8-266; p < .001), anticoagulation therapy (OR, 12.7; 95% CI, 3.01-48.6; p < .001), and pulmonary disease (OR, 16.1; 95% CI, 10.4-25.4; p < .001). Thirty-day all-cause mortality (OR, 1.27; 95% CI, 0.75-2.11; p = .3) and VTE events (OR, 1.32; 95% CI, 0.64-2.73; p = .4) were not significantly different between the matched cohorts; however, hospitalizations (OR, 1.58; 95% CI, 1.20-2.10; p = .001) and non-central nervous system bleeding events (OR, 4.78; 95% CI, 2.98-7.48; p < .001) were increased in PwH. In multivariate analyses, hemophilia did not reduce adverse outcomes (OR, 1.32; 95% CI, 0.74-2.31; p = .2) or VTE (OR, 1.14; 95% CI, 0.44-2.67; p = .8) but increased bleeding risk (OR, 4.70; 95% CI, 2.98-7.48; p < .001). CONCLUSION After adjusting for patient characteristics/comorbidities, hemophilia increased bleeding risk with COVID-19 but did not protect against severe disease and VTE.
Collapse
Affiliation(s)
- Anjali Sharathkumar
- Stead Family Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.
| | - Linder Wendt
- Institute for Clinical and Translational Science, University of Iowa, Iowa City, Iowa, USA
| | - Chris Ortman
- Department of Bioinformatics, University of Iowa, Iowa City, Iowa, USA; Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ragha Srinivasan
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | | | - Elizabeth Chrischilles
- Department of Bioinformatics, University of Iowa, Iowa City, Iowa, USA; Department of Epidemiology, School of Public Health, University of Iowa, Iowa, USA
| | - Clifford M Takemoto
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| |
Collapse
|
6
|
Levitt EB, Patch DA, Hess MC, Terrero A, Jaeger B, Haendel MA, Chute CG, Yeager MT, Ponce BA, Theiss SM, Spitler CA, Johnson JP. Outcomes of SARS-CoV-2 infection among patients with orthopaedic fracture surgery in the National COVID Cohort Collaborative (N3C). Injury 2023; 54:111092. [PMID: 37871347 DOI: 10.1016/j.injury.2023.111092] [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] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/02/2023] [Indexed: 10/25/2023]
Abstract
BACKGROUND The objective of this study was to investigate the outcomes of COVID-19-positive patients undergoing orthopaedic fracture surgery using data from a national database of U.S. adults with a COVID-19 test for SARS-CoV-2. METHODS This is a retrospective cohort study using data from a national database to compare orthopaedic fracture surgery outcomes between COVID-19-positive and COVID-19-negative patients in the United States. Participants aged 18-99 with orthopaedic fracture surgery between March and December 2020 were included. The main exposure was COVID-19 status. Outcomes included perioperative complications, 30-day all-cause mortality, and overall all-cause mortality. Multivariable adjusted models were fitted to determine the association of COVID-positivity with all-cause mortality. RESULTS The total population of 6.5 million patient records was queried, identifying 76,697 participants with a fracture. There were 7,628 participants in the National COVID Cohort who had a fracture and operative management. The Charlson Comorbidity Index was higher in the COVID-19-positive group (n = 476, 6.2 %) than the COVID-19-negative group (n = 7,152, 93.8 %) (2.2 vs 1.4, p<0.001). The COVID-19-positive group had higher mortality (13.2 % vs 5.2 %, p<0.001) than the COVID-19-negative group with higher odds of death in the fully adjusted model (Odds Ratio=1.59; 95 % Confidence Interval: 1.16-2.18). CONCLUSION COVID-19-positive participants with a fracture requiring surgery had higher mortality and perioperative complications than COVID-19-negative patients in this national cohort of U.S. adults tested for COVID-19. The risks associated with COVID-19 can guide potential treatment options and counseling of patients and their families. Future studies can be conducted as data accumulates. LEVEL OF EVIDENCE Level III.
Collapse
Affiliation(s)
- Eli B Levitt
- Department of Orthopaedic Surgery, University of Alabama, Birmingham, AL, USA; Department of Translational Medicine, Florida International University Herbert Wertheim College of Medicine, Miami, FL, USA
| | - David A Patch
- Department of Orthopaedic Surgery, University of Alabama, Birmingham, AL, USA
| | - Matthew C Hess
- Department of Orthopaedic Surgery, University of Alabama, Birmingham, AL, USA
| | - Alfredo Terrero
- Department of Translational Medicine, Florida International University Herbert Wertheim College of Medicine, Miami, FL, USA; Department of Translational Medicine, School of Medicine, University of Miami Miller, Miami, FL, USA
| | - Byron Jaeger
- Department of Epidemiology, University of Alabama, Birmingham, AL, USA
| | - Melissa A Haendel
- 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
| | - Matthew T Yeager
- Department of Orthopaedic Surgery, University of Alabama, Birmingham, AL, USA
| | | | - Steven M Theiss
- Department of Orthopaedic Surgery, University of Alabama, Birmingham, AL, USA
| | - Clay A Spitler
- Department of Orthopaedic Surgery, University of Alabama, Birmingham, AL, USA
| | - Joey P Johnson
- Department of Orthopaedic Surgery, University of Alabama, Birmingham, AL, USA.
| |
Collapse
|
7
|
Liu S, Wen A, Wang L, He H, Fu S, Miller R, Williams A, Harris D, Kavuluru R, Liu M, Abu-el-Rub N, Schutte D, Zhang R, Rouhizadeh M, Osborne JD, He Y, Topaloglu U, Hong SS, Saltz JH, Schaffter T, Pfaff E, Chute CG, Duong T, Haendel MA, Fuentes R, Szolovits P, Xu H, Liu H. An open natural language processing (NLP) framework for EHR-based clinical research: a case demonstration using the National COVID Cohort Collaborative (N3C). J Am Med Inform Assoc 2023; 30:2036-2040. [PMID: 37555837 PMCID: PMC10654844 DOI: 10.1093/jamia/ocad134] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 06/28/2023] [Accepted: 08/08/2023] [Indexed: 08/10/2023] Open
Abstract
Despite recent methodology advancements in clinical natural language processing (NLP), the adoption of clinical NLP models within the translational research community remains hindered by process heterogeneity and human factor variations. Concurrently, these factors also dramatically increase the difficulty in developing NLP models in multi-site settings, which is necessary for algorithm robustness and generalizability. Here, we reported on our experience developing an NLP solution for Coronavirus Disease 2019 (COVID-19) signs and symptom extraction in an open NLP framework from a subset of sites participating in the National COVID Cohort (N3C). We then empirically highlight the benefits of multi-site data for both symbolic and statistical methods, as well as highlight the need for federated annotation and evaluation to resolve several pitfalls encountered in the course of these efforts.
Collapse
Affiliation(s)
- Sijia Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Andrew Wen
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Liwei Wang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Huan He
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Sunyang Fu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Robert Miller
- Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts, USA
| | - Andrew Williams
- Tufts Clinical and Translational Science Institute, Tufts Medical Center, Boston, Massachusetts, USA
| | - Daniel Harris
- Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Ramakanth Kavuluru
- Department of Internal Medicine, University of Kentucky, Lexington, Kentucky, USA
| | - Mei Liu
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Noor Abu-el-Rub
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Dalton Schutte
- Department of Pharmaceutical Care & Health Systems, University of Minnesota at Twin Cities, Minneapolis, Minnesota, USA
| | - Rui Zhang
- Department of Pharmaceutical Care & Health Systems, University of Minnesota at Twin Cities, Minneapolis, Minnesota, USA
| | - Masoud Rouhizadeh
- Department of Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, Florida, USA
| | - John D Osborne
- Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Yongqun He
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Umit Topaloglu
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Stephanie S Hong
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Joel H Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | | | - Emily Pfaff
- Department of Medicine, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Tim Duong
- Department of Radiology, Albert Einstein College of Medicine, Bronx, New York, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | | | - Peter Szolovits
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Hua Xu
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| |
Collapse
|
8
|
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.
Collapse
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
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Pfaff ER, Girvin AT, Crosskey M, Gangireddy S, Master H, Wei WQ, Kerchberger VE, Weiner M, Harris PA, Basford M, Lunt C, Chute CG, Moffitt RA, Haendel M. De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository. J Am Med Inform Assoc 2023; 30:1305-1312. [PMID: 37218289 PMCID: PMC10280348 DOI: 10.1093/jamia/ocad077] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 01/18/2023] [Revised: 03/28/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH's All of Us study partnered to reproduce the output of N3C's trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.
Collapse
Affiliation(s)
- Emily R Pfaff
- Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | | | | | - Srushti Gangireddy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Hiral Master
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - V Eric Kerchberger
- Department of Medicine, Division of Allergy, Pulmonary & Critical Care Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Mark Weiner
- Department of Medicine, Weill Cornell Medicine, New York, USA
| | - Paul A Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Melissa Basford
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Chris Lunt
- National Institutes of Health, Bethesda, Maryland, USA
| | - Christopher G Chute
- Johns Hopkins Schools of Medicine, Public Health, and Nursing. Baltimore, Maryland, USA
| | - Richard A Moffitt
- Departments of Hematology and Medical Oncology and Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| | - Melissa Haendel
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | | |
Collapse
|
12
|
Brannock MD, Chew RF, Preiss AJ, Hadley EC, Redfield S, McMurry JA, Leese PJ, Girvin AT, Crosskey M, Zhou AG, Moffitt RA, Funk MJ, Pfaff ER, Haendel MA, Chute CG. Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program. Nat Commun 2023; 14:2914. [PMID: 37217471 PMCID: PMC10201472 DOI: 10.1038/s41467-023-38388-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [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: 11/04/2022] [Accepted: 04/28/2023] [Indexed: 05/24/2023] Open
Abstract
Long COVID, or complications arising from COVID-19 weeks after infection, has become a central concern for public health experts. The United States National Institutes of Health founded the RECOVER initiative to better understand long COVID. We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. Among patients with a COVID-19 infection between August 1, 2021 and January 31, 2022, we defined two cohorts using distinct definitions of long COVID-a clinical diagnosis (n = 47,404) or a previously described computational phenotype (n = 198,514)-to compare unvaccinated individuals to those with a complete vaccine series prior to infection. Evidence of long COVID was monitored through June or July of 2022, depending on patients' data availability. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history.
Collapse
Affiliation(s)
| | | | | | | | | | - Julie A McMurry
- University of Colorado Anschutz Medical Campus, Denver, CO, USA
| | - Peter J Leese
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | | | - Andrea G Zhou
- iTHRIV, University of Virginia, Charlottesville, VA, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
- Departments of Biomedical Informatics and Hematology and Medical Ontology, Emory University, Atlanta, GA, USA
| | | | - Emily R Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| |
Collapse
|
13
|
Leese P, Anand A, Girvin A, Manna A, Patel S, Yoo YJ, Wong R, Haendel M, Chute CG, Bennett T, Hajagos J, Pfaff E, Moffitt R. Clinical encounter heterogeneity and methods for resolving in networked EHR data: a study from N3C and RECOVER programs. J Am Med Inform Assoc 2023; 30:1125-1136. [PMID: 37087110 PMCID: PMC10198518 DOI: 10.1093/jamia/ocad057] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 10/14/2022] [Revised: 01/31/2023] [Accepted: 03/22/2023] [Indexed: 04/24/2023] Open
Abstract
OBJECTIVE Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multisite electronic health record (EHR) data are networked together. This article presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite "macrovisits." MATERIALS AND METHODS Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. RESULTS Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay (LOS) and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, LOS and measurement variance decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. DISCUSSION Encounters are a complex and heterogeneous component of EHR data and native data issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations, and developments are necessary to realize the full potential of modern real-world data. CONCLUSION This article presents method developments to manipulate and resolve EHR encounter data issues in a generalizable way as a foundation for future research and analysis.
Collapse
Affiliation(s)
- Peter Leese
- NC TraCS Institute, UNC-School of Medicine, Chapel Hill, North Carolina, USA
| | - Adit Anand
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | | | - Amin Manna
- Palantir Technologies, Denver, Colorado, USA
| | - Saaya Patel
- 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
| | - Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Melissa Haendel
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tellen Bennett
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Denver, Colorado, USA
| | - Janos Hajagos
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Emily Pfaff
- Department of Medicine, UNC Chapel Hill, Chapel Hill, North Carolina, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
- Department of Hematology and Medical Oncology, Emory University, Atlanta, Georgia, USA
| |
Collapse
|
14
|
Bhatia A, Preiss AJ, Xiao X, Brannock MD, Alexander GC, Chew RF, Fitzgerald M, Hill E, Kelly EP, Mehta HB, Madlock-Brown C, Wilkins KJ, Chute CG, Haendel M, Moffitt R, Pfaff ER. Effect of Nirmatrelvir/Ritonavir (Paxlovid) on Hospitalization among Adults with COVID-19: an EHR-based Target Trial Emulation from N3C. medRxiv 2023:2023.05.03.23289084. [PMID: 37205340 PMCID: PMC10187454 DOI: 10.1101/2023.05.03.23289084] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing COVID-19 hospitalization rates. From an eligible population of 632,822 COVID-19 patients seen at 33 clinical sites across the United States between December 23, 2021 and December 31, 2022, patients were matched across observed treatment groups, yielding an analytical sample of 410,642 patients. We estimate a 65% reduced odds of hospitalization among Paxlovid-treated patients within a 28-day follow-up period, and this effect did not vary by patient vaccination status. Notably, we observe disparities in Paxlovid treatment, with lower rates among Black and Hispanic or Latino patients, and within socially vulnerable communities. Ours is the largest study of Paxlovid's real-world effectiveness to date, and our primary findings are consistent with previous randomized control trials and real-world studies.
Collapse
Affiliation(s)
- Abhishek Bhatia
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Xuya Xiao
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - G Caleb Alexander
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | | | - Elaine Hill
- University of Rochester, Department of Public Health Sciences and Department of Economics, Rochester, NY, USA
| | | | - Hemalkumar B Mehta
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Kenneth J Wilkins
- National Institute of Diabetes & Digestive & Kidney Diseases, Office of the Director, National Institutes of Health, Bethesda, MD, USA
- F. Edward Hébert School of Medicine, Department of Preventive Medicine & Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Christopher G Chute
- School of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Emily R Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| |
Collapse
|
15
|
Forster AJ, Chute CG, Pincus HA, Ghali WA. ICD-11: A catalyst for advancing patient safety surveillance globally. BMC Med Inform Decis Mak 2023; 21:383. [PMID: 36894925 PMCID: PMC9999485 DOI: 10.1186/s12911-023-02134-2] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 02/06/2023] [Indexed: 03/11/2023] Open
Abstract
The World Health Organization's (WHO) international classification of disease version 11 (ICD-11) contains several features which enable improved classification of patient safety events. We have identified three suggestions to facilitate adoption of ICD-11 from the patient safety perspective. One, health system leaders at national, regional, and local levels should incorporate ICD-11 into all approaches to monitor patient safety. This will allow them to take advantage of the innovative patient safety classification methods embedded in ICD-11 to overcome several limitations related to existing patient safety surveillance methods. Two, application developers should incorporate ICD-11 into software solutions. This will accelerate adoption and utility of software-enabled clinical and administrative workflows relevant to patient safety management. This is enabled as a result of the ICD-11 application programming interface (or API) developed by the WHO. Third, health system leaders should adopt the ICD-11 using a continuous improvement framework. This will help leaders at national, regional and local levels to take advantage of specific existing initiatives which will be strengthened by ICD-11, including peer review comparisons, clinician engagement, and alignment of front-line safety efforts with post marketing surveillance of medical technologies. While the investment to adopt ICD-11 will be considerable, these will be offset by reducing the ongoing costs related to a lack of accurate routine information.
Collapse
Affiliation(s)
- Alan J Forster
- The Ottawa Hospital Ottawa; Ottawa Hospital Research Institute, Clinical Epidemiology Program; and Faculty of Medicine, University of Ottawa, Ottawa, Canada.
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, USA
| | - Harold Alan Pincus
- Irving Institute for Clinical and Translational Research and Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - William A Ghali
- The Ottawa Hospital Ottawa; Ottawa Hospital Research Institute, Clinical Epidemiology Program; and Faculty of Medicine, University of Ottawa, Ottawa, Canada
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, USA
- Irving Institute for Clinical and Translational Research and Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, NY, USA
- Office of the Vice President Research; and, The O'Brien Institute of Public Health, University of Calgary, Calgary, AB, Canada
| |
Collapse
|
16
|
Fu S, Wang L, Moon S, Zong N, He H, Pejaver V, Relevo R, Walden A, Haendel M, Chute CG, Liu H. Recommended practices and ethical considerations for natural language processing-assisted observational research: A scoping review. Clin Transl Sci 2023; 16:398-411. [PMID: 36478394 PMCID: PMC10014687 DOI: 10.1111/cts.13463] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/03/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
An increasing number of studies have reported using natural language processing (NLP) to assist observational research by extracting clinical information from electronic health records (EHRs). Currently, no standardized reporting guidelines for NLP-assisted observational studies exist. The absence of detailed reporting guidelines may create ambiguity in the use of NLP-derived content, knowledge gaps in the current research reporting practices, and reproducibility challenges. To address these issues, we conducted a scoping review of NLP-assisted observational clinical studies and examined their reporting practices, focusing on NLP methodology and evaluation. Through our investigation, we discovered a high variation regarding the reporting practices, such as inconsistent use of references for measurement studies, variation in the reporting location (reference, appendix, and manuscript), and different granularity of NLP methodology and evaluation details. To promote the wide adoption and utilization of NLP solutions in clinical research, we outline several perspectives that align with the six principles released by the World Health Organization (WHO) that guide the ethical use of artificial intelligence for health.
Collapse
Affiliation(s)
- Sunyang Fu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Liwei Wang
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Sungrim Moon
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Nansu Zong
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Huan He
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Vikas Pejaver
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rose Relevo
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Anita Walden
- The National Center for Data to Health, Bethesda, Maryland, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Hongfang Liu
- Department of AI and Informatics Research, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
17
|
Brandt PS, Kho A, Luo Y, Pacheco JA, Walunas TL, Hakonarson H, Hripcsak G, Liu C, Shang N, Weng C, Walton N, Carrell DS, Crane PK, Larson EB, Chute CG, Kullo IJ, Carroll R, Denny J, Ramirez A, Wei WQ, Pathak J, Wiley LK, Richesson R, Starren JB, Rasmussen LV. Characterizing variability of electronic health record-driven phenotype definitions. J Am Med Inform Assoc 2023; 30:427-437. [PMID: 36474423 PMCID: PMC9933077 DOI: 10.1093/jamia/ocac235] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [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: 08/21/2022] [Revised: 10/19/2022] [Accepted: 11/23/2022] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE The aim of this study was to analyze a publicly available sample of rule-based phenotype definitions to characterize and evaluate the variability of logical constructs used. MATERIALS AND METHODS A sample of 33 preexisting phenotype definitions used in research that are represented using Fast Healthcare Interoperability Resources and Clinical Quality Language (CQL) was analyzed using automated analysis of the computable representation of the CQL libraries. RESULTS Most of the phenotype definitions include narrative descriptions and flowcharts, while few provide pseudocode or executable artifacts. Most use 4 or fewer medical terminologies. The number of codes used ranges from 5 to 6865, and value sets from 1 to 19. We found that the most common expressions used were literal, data, and logical expressions. Aggregate and arithmetic expressions are the least common. Expression depth ranges from 4 to 27. DISCUSSION Despite the range of conditions, we found that all of the phenotype definitions consisted of logical criteria, representing both clinical and operational logic, and tabular data, consisting of codes from standard terminologies and keywords for natural language processing. The total number and variety of expressions are low, which may be to simplify implementation, or authors may limit complexity due to data availability constraints. CONCLUSIONS The phenotype definitions analyzed show significant variation in specific logical, arithmetic, and other operators but are all composed of the same high-level components, namely tabular data and logical expressions. A standard representation for phenotype definitions should support these formats and be modular to support localization and shared logic.
Collapse
Affiliation(s)
- Pascal S Brandt
- Department of Biomedical and Medical Education, University of Washington, Seattle, Washington, USA
| | - Abel Kho
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Jennifer A Pacheco
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Theresa L Walunas
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Ning Shang
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Nephi Walton
- Intermountain Precision Genomics, Intermountain Healthcare, St George, Utah, USA
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Paul K Crane
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Eric B Larson
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Health Services, University of Washington, Seattle, Washington, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Robert Carroll
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Josh Denny
- All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Andrea Ramirez
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jyoti Pathak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Laura K Wiley
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Rachel Richesson
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Justin B Starren
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| |
Collapse
|
18
|
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.
Collapse
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
| | | | | | | | | | | | | | | | | |
Collapse
|
19
|
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).
Collapse
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
| | | | | |
Collapse
|
20
|
Reese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, Laraway B, Antonescu C, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Caufield JH, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine 2023; 87:104413. [PMID: 36563487 PMCID: PMC9769411 DOI: 10.1016/j.ebiom.2022.104413] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [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] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 11/23/2022] [Accepted: 11/29/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. METHODS We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. FINDINGS We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. INTERPRETATION Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. FUNDING NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.
Collapse
Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Elena Casiraghi
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Johanna J Loomba
- The Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Tiffany J Callahan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Bryan Laraway
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Kenneth J Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | - Nariman Ammar
- Health Science Center, University of Tennessee, Memphis, TN, USA
| | - Blessy Antony
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - J Harry Caufield
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Julie A McMurry
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Andrew Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA; Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Richard Moffitt
- Department of Biomedical Informatics and Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | | | | | | | - Kristin Kostka
- Northeastern University, OHDSI Center at the Roux Institute, Boston, MA, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università Degli Studi di Milano, Milan, Italy
| | | | - Christopher G Chute
- Schools of Medicine, Public Health and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA.
| |
Collapse
|
21
|
Brannock MD, Chew RF, Preiss AJ, Hadley EC, McMurry JA, Leese PJ, Girvin AT, Crosskey M, Zhou AG, Moffitt RA, Funk MJ, Pfaff ER, Haendel MA, Chute CG. Long COVID Risk and Pre-COVID Vaccination: An EHR-Based Cohort Study from the RECOVER Program. medRxiv 2022:2022.10.06.22280795. [PMID: 36238713 PMCID: PMC9558440 DOI: 10.1101/2022.10.06.22280795] [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: 11/07/2022]
Abstract
Importance Characterizing the effect of vaccination on long COVID allows for better healthcare recommendations. Objective To determine if, and to what degree, vaccination prior to COVID-19 is associated with eventual long COVID onset, among those a documented COVID-19 infection. Design Settings and Participants Retrospective cohort study of adults with evidence of COVID-19 between August 1, 2021 and January 31, 2022 based on electronic health records from eleven healthcare institutions taking part in the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, a project of the National Covid Cohort Collaborative (N3C). Exposures Pre-COVID-19 receipt of a complete vaccine series versus no pre-COVID-19 vaccination. Main Outcomes and Measures Two approaches to the identification of long COVID were used. In the clinical diagnosis cohort (n=47,752), ICD-10 diagnosis codes or evidence of a healthcare encounter at a long COVID clinic were used. In the model-based cohort (n=199,498), a computable phenotype was used. The association between pre-COVID vaccination and long COVID was estimated using IPTW-adjusted logistic regression and Cox proportional hazards. Results In both cohorts, when adjusting for demographics and medical history, pre-COVID vaccination was associated with a reduced risk of long COVID (clinic-based cohort: HR, 0.66; 95% CI, 0.55-0.80; OR, 0.69; 95% CI, 0.59-0.82; model-based cohort: HR, 0.62; 95% CI, 0.56-0.69; OR, 0.70; 95% CI, 0.65-0.75). Conclusions and Relevance Long COVID has become a central concern for public health experts. Prior studies have considered the effect of vaccination on the prevalence of future long COVID symptoms, but ours is the first to thoroughly characterize the association between vaccination and clinically diagnosed or computationally derived long COVID. Our results bolster the growing consensus that vaccines retain protective effects against long COVID even in breakthrough infections. Key Points Question: Does vaccination prior to COVID-19 onset change the risk of long COVID diagnosis?Findings: Four observational analyses of EHRs showed a statistically significant reduction in long COVID risk associated with pre-COVID vaccination (first cohort: HR, 0.66; 95% CI, 0.55-0.80; OR, 0.69; 95% CI, 0.59-0.82; second cohort: HR, 0.62; 95% CI, 0.56-0.69; OR, 0.70; 95% CI, 0.65-0.75).Meaning: Vaccination prior to COVID onset has a protective association with long COVID even in the case of breakthrough infections.
Collapse
Affiliation(s)
| | | | | | | | - Julie A McMurry
- University of Colorado Anschutz Medical Campus, Denver, CO, US
| | - Peter J Leese
- University of North Carolina at Chapel Hill, Chapel Hill, NC, US
| | | | | | | | | | | | - Emily R Pfaff
- University of North Carolina at Chapel Hill, Chapel Hill, NC, US
| | | | | |
Collapse
|
22
|
Xiao G, Pfaff E, Prud'hommeaux E, Booth D, Sharma DK, Huo N, Yu Y, Zong N, Ruddy KJ, Chute CG, Jiang G. FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model. J Biomed Inform 2022; 134:104201. [PMID: 36089199 PMCID: PMC9561043 DOI: 10.1016/j.jbi.2022.104201] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [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: 05/23/2022] [Revised: 08/04/2022] [Accepted: 09/04/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by the research and healthcare AI communities. From the standardization perspective, community-based standards such as the Fast Healthcare Interoperability Resources (FHIR) and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) are increasingly used to represent and standardize EHR data for clinical data analytics, however, the potential of such a standard on building CKG has not been well investigated. OBJECTIVE To develop and evaluate methods and tools that expose the OMOP CDM-based clinical data repositories into virtual clinical KGs that are compliant with FHIR Resource Description Framework (RDF) specification. METHODS We developed a system called FHIR-Ontop-OMOP to generate virtual clinical KGs from the OMOP relational databases. We leveraged an OMOP CDM-based Medical Information Mart for Intensive Care (MIMIC-III) data repository to evaluate the FHIR-Ontop-OMOP system in terms of the faithfulness of data transformation and the conformance of the generated CKGs to the FHIR RDF specification. RESULTS A beta version of the system has been released. A total of more than 100 data element mappings from 11 OMOP CDM clinical data, health system and vocabulary tables were implemented in the system, covering 11 FHIR resources. The generated virtual CKG from MIMIC-III contains 46,520 instances of FHIR Patient, 716,595 instances of Condition, 1,063,525 instances of Procedure, 24,934,751 instances of MedicationStatement, 365,181,104 instances of Observations, and 4,779,672 instances of CodeableConcept. Patient counts identified by five pairs of SQL (over the MIMIC database) and SPARQL (over the virtual CKG) queries were identical, ensuring the faithfulness of the data transformation. Generated CKG in RDF triples for 100 patients were fully conformant with the FHIR RDF specification. CONCLUSION The FHIR-Ontop-OMOP system can expose OMOP database as a FHIR-compliant RDF graph. It provides a meaningful use case demonstrating the potentials that can be enabled by the interoperability between FHIR and OMOP CDM. Generated clinical KGs in FHIR RDF provide a semantic foundation to enable explainable AI applications in healthcare.
Collapse
Affiliation(s)
- Guohui Xiao
- University of Bergen, Norway; University of Oslo, Norway; Ontopic S.r.l., Italy.
| | - Emily Pfaff
- University of North Carolina, Chapel Hill, NC, USA
| | | | | | | | - Nan Huo
- Mayo Clinic, Rochester, MN, USA
| | - Yue Yu
- Mayo Clinic, Rochester, MN, USA
| | | | | | | | | |
Collapse
|
23
|
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.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Liz Kelly
- University of North Carolina at Chapel Hill
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
Reese JT, Blau H, Bergquist T, Loomba JJ, Callahan T, Laraway B, Antonescu C, Casiraghi E, Coleman B, Gargano M, Wilkins KJ, Cappelletti L, Fontana T, Ammar N, Antony B, Murali TM, Karlebach G, McMurry JA, Williams A, Moffitt R, Banerjee J, Solomonides AE, Davis H, Kostka K, Valentini G, Sahner D, Chute CG, Madlock-Brown C, Haendel MA, Robinson PN. Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs. medRxiv 2022:2022.05.24.22275398. [PMID: 35665012 PMCID: PMC9164456 DOI: 10.1101/2022.05.24.22275398] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Accurate stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning procedures. Using k-means clustering of this similarity matrix, we found six distinct clusters of PASC patients, each with distinct profiles of phenotypic abnormalities. There was a significant association of cluster membership with a range of pre-existing conditions and with measures of severity during acute COVID-19. Two of the clusters were associated with severe manifestations and displayed increased mortality. We assigned new patients from other healthcare centers to one of the six clusters on the basis of maximum semantic similarity to the original patients. We show that the identified clusters were generalizable across different hospital systems and that the increased mortality rate was consistently observed in two of the clusters. Semantic phenotypic clustering can provide a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC.
Collapse
|
26
|
Salah HM, Fudim M, O'Neil ST, Manna A, Chute CG, Caughey MC. Post-recovery COVID-19 and incident heart failure in the National COVID Cohort Collaborative (N3C) study. Nat Commun 2022; 13:4117. [PMID: 35840623 PMCID: PMC9284961 DOI: 10.1038/s41467-022-31834-y] [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] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 07/04/2022] [Indexed: 11/22/2022] Open
Abstract
Cardiac involvement has been noted in COVID-19 infection. However, the relationship between post-recovery COVID-19 and development of de novo heart failure has not been investigated in a large, nationally representative population. We examined post-recovery outcomes of 587,330 patients hospitalized in the United States (257,075 with COVID-19 and 330,255 without), using data from the National COVID Cohort Collaborative study. Patients hospitalized with COVID-19 were older (51 vs. 46 years), more often male (49% vs. 42%), and less often White (61% vs. 69%). Over a median follow up of 367 days, 10,979 incident heart failure events occurred. After adjustments, COVID-19 hospitalization was associated with a 45% higher hazard of incident heart failure (hazard ratio = 1.45; 95% confidence interval: 1.39-1.51), with more pronounced associations among patients who were younger (P-interaction = 0.003), White (P-interaction = 0.005), or who had established cardiovascular disease (P-interaction = 0.005). In conclusion, COVID-19 hospitalization is associated with increased risk of incident heart failure.
Collapse
Affiliation(s)
- Husam M Salah
- Department of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Marat Fudim
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA.
| | - Shawn T O'Neil
- 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 C Caughey
- Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Chapel Hill, NC, USA
| |
Collapse
|
27
|
Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, Dekermanjian JP, Jolley SE, Kahn MG, Kostka K, McMurry JA, Moffitt R, Walden A, Chute CG, Haendel MA. Identifying who has long COVID in the USA: a machine learning approach using N3C data. Lancet Digit Health 2022; 4:e532-e541. [PMID: 35589549 PMCID: PMC9110014 DOI: 10.1016/s2589-7500(22)00048-6] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/19/2022] [Accepted: 03/08/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Post-acute sequelae of SARS-CoV-2 infection, known as long COVID, have severely affected recovery from the COVID-19 pandemic for patients and society alike. Long COVID is characterised by evolving, heterogeneous symptoms, making it challenging to derive an unambiguous definition. Studies of electronic health records are a crucial element of the US National Institutes of Health's RECOVER Initiative, which is addressing the urgent need to understand long COVID, identify treatments, and accurately identify who has it-the latter is the aim of this study. METHODS Using the National COVID Cohort Collaborative's (N3C) electronic health record repository, we developed XGBoost machine learning models to identify potential patients with long COVID. We defined our base population (n=1 793 604) as any non-deceased adult patient (age ≥18 years) with either an International Classification of Diseases-10-Clinical Modification COVID-19 diagnosis code (U07.1) from an inpatient or emergency visit, or a positive SARS-CoV-2 PCR or antigen test, and for whom at least 90 days have passed since COVID-19 index date. We examined demographics, health-care utilisation, diagnoses, and medications for 97 995 adults with COVID-19. We used data on these features and 597 patients from a long COVID clinic to train three machine learning models to identify potential long COVID among all patients with COVID-19, patients hospitalised with COVID-19, and patients who had COVID-19 but were not hospitalised. Feature importance was determined via Shapley values. We further validated the models on data from a fourth site. FINDINGS Our models identified, with high accuracy, patients who potentially have long COVID, achieving areas under the receiver operator characteristic curve of 0·92 (all patients), 0·90 (hospitalised), and 0·85 (non-hospitalised). Important features, as defined by Shapley values, include rate of health-care utilisation, patient age, dyspnoea, and other diagnosis and medication information available within the electronic health record. INTERPRETATION Patients identified by our models as potentially having long COVID can be interpreted as patients warranting care at a specialty clinic for long COVID, which is an essential proxy for long COVID diagnosis as its definition continues to evolve. We also achieve the urgent goal of identifying potential long COVID in patients for clinical trials. As more data sources are identified, our models can be retrained and tuned based on the needs of individual studies. FUNDING US National Institutes of Health and National Center for Advancing Translational Sciences through the RECOVER Initiative.
Collapse
Affiliation(s)
- Emily R Pfaff
- Department of Medicine, UNC Chapel Hill School of Medicine, Chapel Hill, NC, USA.
| | | | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Section of Critical Care Medicine, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Abhishek Bhatia
- Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ian M Brooks
- Colorado Center for Personalised Medicine, Division of Biomedical Informatics & Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Rachel R Deer
- Department of Nutrition, Metabolism, and Rehabilitation Sciences, University of Texas Medical Branch, Galveston, TX, USA
| | - Jonathan P Dekermanjian
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Sarah Elizabeth Jolley
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Michael G Kahn
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Kristin Kostka
- The OHDSI Center at the Roux Institute, Northeastern University, Portland, ME, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook Cancer Center, Stony Brook University, Stony Brook, NY, USA
| | - Anita Walden
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher G Chute
- Section of Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| |
Collapse
|
28
|
Matentzoglu N, Balhoff JP, Bello SM, Bizon C, Brush M, Callahan TJ, Chute CG, Duncan WD, Evelo CT, Gabriel D, Graybeal J, Gray A, Gyori BM, Haendel M, Harmse H, Harris NL, Harrow I, Hegde HB, Hoyt AL, Hoyt CT, Jiao D, Jiménez-Ruiz E, Jupp S, Kim H, Koehler S, Liener T, Long Q, Malone J, McLaughlin JA, McMurry JA, Moxon S, Munoz-Torres MC, Osumi-Sutherland D, Overton JA, Peters B, Putman T, Queralt-Rosinach N, Shefchek K, Solbrig H, Thessen A, Tudorache T, Vasilevsky N, Wagner AH, Mungall CJ. A Simple Standard for Sharing Ontological Mappings (SSSOM). Database (Oxford) 2022; 2022:6591806. [PMID: 35616100 PMCID: PMC9216545 DOI: 10.1093/database/baac035] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [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: 12/15/2021] [Revised: 03/08/2022] [Accepted: 05/11/2022] [Indexed: 02/03/2023]
Abstract
Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.
Collapse
Affiliation(s)
| | - James P Balhoff
- RENCI, University of North Carolina, Chapel Hill, NC 27517, USA
| | | | - Chris Bizon
- RENCI, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Matthew Brush
- University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | | | | | | | - Chris T Evelo
- Maastricht University, Maastricht 6211 LK, The Netherlands
| | | | | | - Alasdair Gray
- Department of Computer Science, Heriot-Watt University, Edinburgh, Currie EH14 4AS, UK
| | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | - Henriette Harmse
- European Bioinformatics Institute (EMBL-EBI), Hinxton CB10 1SD, UK
| | - Nomi L Harris
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | - Harshad B Hegde
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Amelia L Hoyt
- Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | | | - Dazhi Jiao
- Johns Hopkins University, Baltimore, MD 21210, USA
| | - Ernesto Jiménez-Ruiz
- City University of London, London EC1V 0HB, UK,University of Oslo, Oslo 0315, Norway
| | - Simon Jupp
- SciBite Limited, Bio Data Innovation Centre, Wellcome Genome Campus, Hinxton, Saffron Walden CB10 1DR, UK
| | | | | | | | - Qinqin Long
- Leiden University Medical Center, Leiden 2333 ZA, The Netherlands
| | - James Malone
- BenchSci, 25 York St Suite 1100, Toronto, ON M5J 2V5, Canada
| | | | - Julie A McMurry
- University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | - Sierra Moxon
- Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | | | | | | | - Bjoern Peters
- La Jolla Institute for Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA
| | - Tim Putman
- University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | | | - Kent Shefchek
- University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | | | - Anne Thessen
- University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | | | - Nicole Vasilevsky
- University of Colorado Anschutz Medical Campus, Aurora, CO 80217, USA
| | - Alex H Wagner
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA,The Ohio State University College of Medicine, Columbus, OH 43210, USA
| | | |
Collapse
|
29
|
Abstract
A new coding feature introduced with ICD-11, the 11th revision of the International Classification of Diseases (ICD), is postcoordination, which supports combining (linking) two or more codes into a cluster that describes a clinical concept. Postcoordination allows for coded data to be reported to a greater level of specificity than was possible in previous version of ICD. The linked codes are kept together in a cluster when submitted for reporting. This article presents background detail on the postcoordination feature in ICD and the postcoordination tool. Also presented are several examples that demonstrate the flexibility that ICD-11 provides for enriching coded health information.
Collapse
Affiliation(s)
- Kristy Mabon
- Canadian Institute for Health Information, 495 Richmond Road, Suite 600, Ottawa, ON, K2A 4H6, Canada.
| | - Olafr Steinum
- Nordic Centre for Classifications in Health Care, Rörviksvägen 19 SE-45197, Uddevalla, Sweden
| | - Christopher G Chute
- Division of General Internal Medicine, Public Health, and Nursing, Institute for Clinical and Translational Research, Johns Hopkins University, 2024 E Monument St, Suite 1-200, Baltimore, MD, 21287, USA
| |
Collapse
|
30
|
Abstract
BACKGROUND The International Classification of Diseases (ICD) has progressed from a short list of causes of death to become the predominant classification of human diseases, syndromes, and conditions around the world. The World Health Organization has now explored how the ICD could be revised to leverage the advances in computer science, ontology, and knowledge representation that had accelerated in the twentieth and early twenty-first centuries. METHODS Many teams of clinical specialists and domain leaders worked to fundamentally revise the science and knowledge base of ICD-11. Development of the ICD-11 architecturally was a fundamental revision. The architecture for ICD-11 proposed in 2007 included three layers: a semantic network of biomedical concepts (Foundation), a traditional tabulation of hierarchical codes that would derive from that network (Linearization), and a formal ontology that would anchor the meaning of terms in the semantic network. Additionally, each entry in the semantic network would have an associated information model of required and optional content (Content Model). RESULTS This paper describes the innovative architecture developed for ICD-11. CONCLUSION ICD11 is a revolutionary transformation of a century long medical classification that retains is historical rendering and interface while expanding the opportunity for multiple linearization and underpinning its content with a formally constructed semantic network. The new artifact can enable modern data science and analyses with content encoded with ICD11.
Collapse
Affiliation(s)
- Christopher G. Chute
- Johns Hopkins Schools of Medicine, Public Health, and Nursing, 2024 E Monument St, Suite 1-200, Baltimore, MD 21287 USA
| | - Can Çelik
- grid.3575.40000000121633745World Health Organization, Geneva, Switzerland
| |
Collapse
|
31
|
Reese JT, Coleman B, Chan L, Blau H, Callahan TJ, Cappelletti L, Fontana T, Bradwell KR, Harris NL, Casiraghi E, Valentini G, Karlebach G, Deer R, McMurry JA, Haendel MA, Chute CG, Pfaff E, Moffitt R, Spratt H, Singh JA, Mungall CJ, Williams AE, Robinson PN. NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study. Virol J 2022; 19:84. [PMID: 35570298 PMCID: PMC9107579 DOI: 10.1186/s12985-022-01813-2] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/04/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.
Collapse
Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Lauren Chan
- Translational and Integrative Sciences Center, Oregon State University, Corvallis, OR, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Tiffany J Callahan
- Computational Bioscience, University of Colorado Anschutz Medical Campus, Boulder, CO, USA
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
| | | | - Nomi L Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Rome, Italy
| | - Giorgio Valentini
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Rome, Italy
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Rachel Deer
- University of Texas Medical Branch, Galveston, TX, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Melissa A Haendel
- 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
| | - Emily Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Heidi Spratt
- University of Texas Medical Branch, Galveston, TX, USA
| | - Jasvinder A Singh
- University of Alabama at Birmingham, Birmingham, AL, USA
- Medicine Service, VA Medical Center, Birmingham, AL, USA
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew E Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts University School of Medicine, Boston, USA
- OHDSI Center at the Roux Institute, Northeastern University, Boston, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA.
| |
Collapse
|
32
|
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.
Collapse
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
| |
Collapse
|
33
|
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.
Collapse
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
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
34
|
Yu Y, Zong N, Wen A, Liu S, Stone DJ, Knaack D, Chamberlain AM, Pfaff E, Gabriel D, Chute CG, Shah N, Jiang G. Developing an ETL tool for converting the PCORnet CDM into the OMOP CDM to facilitate the COVID-19 data integration. J Biomed Inform 2022; 127:104002. [PMID: 35077901 PMCID: PMC8791245 DOI: 10.1016/j.jbi.2022.104002] [Citation(s) in RCA: 6] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 11/01/2022]
Abstract
OBJECTIVE The large-scale collection of observational data and digital technologies could help curb the COVID-19 pandemic. However, the coexistence of multiple Common Data Models (CDMs) and the lack of data extract, transform, and load (ETL) tool between different CDMs causes potential interoperability issue between different data systems. The objective of this study is to design, develop, and evaluate an ETL tool that transforms the PCORnet CDM format data into the OMOP CDM. METHODS We developed an open-source ETL tool to facilitate the data conversion from the PCORnet CDM and the OMOP CDM. The ETL tool was evaluated using a dataset with 1000 patients randomly selected from the PCORnet CDM at Mayo Clinic. Information loss, data mapping accuracy, and gap analysis approaches were conducted to assess the performance of the ETL tool. We designed an experiment to conduct a real-world COVID-19 surveillance task to assess the feasibility of the ETL tool. We also assessed the capacity of the ETL tool for the COVID-19 data surveillance using data collection criteria of the MN EHR Consortium COVID-19 project. RESULTS After the ETL process, all the records of 1000 patients from 18 PCORnet CDM tables were successfully transformed into 12 OMOP CDM tables. The information loss for all the concept mapping was less than 0.61%. The string mapping process for the unit concepts lost 2.84% records. Almost all the fields in the manual mapping process achieved 0% information loss, except the specialty concept mapping. Moreover, the mapping accuracy for all the fields were 100%. The COVID-19 surveillance task collected almost the same set of cases (99.3% overlaps) from the original PCORnet CDM and target OMOP CDM separately. Finally, all the data elements for MN EHR Consortium COVID-19 project could be captured from both the PCORnet CDM and the OMOP CDM. CONCLUSION We demonstrated that our ETL tool could satisfy the data conversion requirements between the PCORnet CDM and the OMOP CDM. The outcome of the work would facilitate the data retrieval, communication, sharing, and analysis between different institutions for not only COVID-19 related project, but also other real-world evidence-based observational studies.
Collapse
Affiliation(s)
- Yue Yu
- Mayo Clinic, Rochester, MN, USA
| | | | | | | | | | | | | | - Emily Pfaff
- University of North Carolina, Chapel Hill, NC, USA
| | | | | | | | | |
Collapse
|
35
|
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: 88] [Impact Index Per Article: 44.0] [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.
Collapse
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
| |
Collapse
|
36
|
Sun J, Zheng Q, Madhira V, Olex AL, Anzalone AJ, Vinson A, Singh JA, French E, Abraham AG, Mathew J, Safdar N, Agarwal G, Fitzgerald KC, Singh N, Topaloglu U, Chute CG, Mannon RB, Kirk GD, Patel RC. Association Between Immune Dysfunction and COVID-19 Breakthrough Infection After SARS-CoV-2 Vaccination in the US. JAMA Intern Med 2022; 182:153-162. [PMID: 34962505 PMCID: PMC8715386 DOI: 10.1001/jamainternmed.2021.7024] [Citation(s) in RCA: 150] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 10/09/2021] [Indexed: 12/30/2022]
Abstract
Importance Persons with immune dysfunction have a higher risk for severe COVID-19 outcomes. However, these patients were largely excluded from SARS-CoV-2 vaccine clinical trials, creating a large evidence gap. Objective To identify the incidence rate and incidence rate ratio (IRR) for COVID-19 breakthrough infection after SARS-CoV-2 vaccination among persons with or without immune dysfunction. Design, Setting, and Participants This retrospective cohort study analyzed data from the National COVID Cohort Collaborative (N3C), a partnership that developed a secure, centralized electronic medical record-based repository of COVID-19 clinical data from academic medical centers across the US. Persons who received at least 1 dose of a SARS-CoV-2 vaccine between December 10, 2020, and September 16, 2021, were included in the sample. Main Outcomes and Measures Vaccination, COVID-19 diagnosis, immune dysfunction diagnoses (ie, HIV infection, multiple sclerosis, rheumatoid arthritis, solid organ transplant, and bone marrow transplantation), other comorbid conditions, and demographic data were accessed through the N3C Data Enclave. Breakthrough infection was defined as a COVID-19 infection that was contracted on or after the 14th day of vaccination, and the risk after full or partial vaccination was assessed for patients with or without immune dysfunction using Poisson regression with robust SEs. Poisson regression models were controlled for a study period (before or after [pre- or post-Delta variant] June 20, 2021), full vaccination status, COVID-19 infection before vaccination, demographic characteristics, geographic location, and comorbidity burden. Results A total of 664 722 patients in the N3C sample were included. These patients had a median (IQR) age of 51 (34-66) years and were predominantly women (n = 378 307 [56.9%]). Overall, the incidence rate for COVID-19 breakthrough infection was 5.0 per 1000 person-months among fully vaccinated persons but was higher after the Delta variant became the dominant SARS-CoV-2 strain (incidence rate before vs after June 20, 2021, 2.2 [95% CI, 2.2-2.2] vs 7.3 [95% CI, 7.3-7.4] per 1000 person-months). Compared with partial vaccination, full vaccination was associated with a 28% reduced risk for breakthrough infection (adjusted IRR [AIRR], 0.72; 95% CI, 0.68-0.76). People with a breakthrough infection after full vaccination were more likely to be older and women. People with HIV infection (AIRR, 1.33; 95% CI, 1.18-1.49), rheumatoid arthritis (AIRR, 1.20; 95% CI, 1.09-1.32), and solid organ transplant (AIRR, 2.16; 95% CI, 1.96-2.38) had a higher rate of breakthrough infection. Conclusions and Relevance This cohort study found that full vaccination was associated with reduced risk of COVID-19 breakthrough infection, regardless of the immune status of patients. Despite full vaccination, persons with immune dysfunction had substantially higher risk for COVID-19 breakthrough infection than those without such a condition. For persons with immune dysfunction, continued use of nonpharmaceutical interventions (eg, mask wearing) and alternative vaccine strategies (eg, additional doses or immunogenicity testing) are recommended even after full vaccination.
Collapse
Affiliation(s)
- Jing Sun
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Qulu Zheng
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Amy L. Olex
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond
| | - Alfred J. Anzalone
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha
| | - Amanda Vinson
- Division of Nephrology, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Jasvinder A. Singh
- Department of Medicine at the School of Medicine, University of Alabama at Birmingham (UAB), Birmingham
- Department of Epidemiology at the UAB School of Public Health, Birmingham
| | - Evan French
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond
| | - Alison G. Abraham
- Department of Epidemiology, University of Colorado, Anschutz Medical Campus, Denver
| | - Jomol Mathew
- Department of Population Health Sciences, University of Wisconsin−Madison School of Medicine and Public Health, Madison
| | - Nasia Safdar
- Department of Medicine, University of Wisconsin−Madison, Madison
| | - Gaurav Agarwal
- Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham
| | - Kathryn C. Fitzgerald
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland
| | - Namrata Singh
- Division of Rheumatology, Department of Medicine, University of Washington, Seattle
| | - Umit Topaloglu
- Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Christopher G. Chute
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- School of Medicine, Public Health and Nursing, Johns Hopkins University, Baltimore, Maryland
| | - Roslyn B. Mannon
- Department of Medicine, University of Nebraska Medical Center, Omaha
| | - Gregory D. Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | - Rena C. Patel
- Division of Allergy and Infectious Diseases, Departments of Medicine and Global Health, University of Washington, Seattle
| |
Collapse
|
37
|
Pitts CC, Levitt EB, Patch DA, Mihas AK, Terrero A, Haendel MA, Chute CG, Ponce BA, Theiss SM, Spitler CA, Johnson MD. Ankle Fracture and Length of Stay in US Adult Population Using Data From the National COVID Cohort Collaborative. Foot & Ankle Orthopaedics 2022; 7:24730114221077282. [PMID: 35237737 PMCID: PMC8883310 DOI: 10.1177/24730114221077282] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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] [Indexed: 12/23/2022] Open
Abstract
Background: The National COVID Cohort Collaborative (N3C) is an innovative approach to integrate real-world clinical observations into a harmonized database during the time of the COVID-19 pandemic when clinical research on ankle fracture surgery is otherwise mostly limited to expert opinion and research letters. The purpose of this manuscript is to introduce the largest cohort of US ankle fracture surgery patients to date with a comparison between lab-confirmed COVID-19–positive and COVID-19–negative. Methods: A retrospective cohort of adults with ankle fracture surgery using data from the N3C database with patients undergoing surgery between March 2020 and June 2021. The database is an NIH-funded platform through which the harmonized clinical data from 46 sites is stored. Patient characteristics included body mass index, Charlson Comorbidity Index, and smoking status. Outcomes included 30-day mortality, overall mortality, surgical site infection (SSI), deep SSI, acute kidney injury, pulmonary embolism, deep vein thrombosis, sepsis, time to surgery, and length of stay. COVID-19–positive patients were compared to COVID-19–negative controls to investigate perioperative outcomes during the pandemic. Results: A total population of 8.4 million patient records was queried, identifying 4735 adults with ankle fracture surgery. The COVID-19–positive group (n=158, 3.3%) had significantly longer times to surgery (6.5 ± 6.6 vs 5.1 ± 5.5 days, P = .001) and longer lengths of stay (8.3 ± 23.5 vs 4.3 ± 7.4 days, P < .001), compared to the COVID-19–negative group. The COVID-19–positive group also had a higher rate of 30-day mortality. Conclusion: Patients with ankle fracture surgery had longer time to surgery and prolonged hospitalizations in COVID-19–positive patients compared to those who tested negative (average delay was about 1 day and increased length of hospitalization was about 4 days). Few perioperative events were observed in either group. Overall, the risks associated with COVID-19 were measurable but not substantial. Level of Evidence: Level III, retrospective cohort study.
Collapse
Affiliation(s)
- Charles C. Pitts
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eli B. Levitt
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Translational Medicine, Florida International University Herbert Wertheim College of Medicine, Miami, FL, USA
| | - David A. Patch
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Alexander K. Mihas
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
- Department of Translational Medicine, Florida International University Herbert Wertheim College of Medicine, Miami, FL, USA
| | - Alfredo Terrero
- Department of Translational Medicine, Florida International University Herbert Wertheim College of Medicine, Miami, FL, USA
- Department of Translational Medicine, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Melissa A. Haendel
- 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
| | | | - Steven M. Theiss
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Clay A. Spitler
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Michael D. Johnson
- Department of Orthopaedic Surgery, University of Alabama at Birmingham, Birmingham, AL, USA
| |
Collapse
|
38
|
Reese JT, Coleman B, Chan L, Blau H, Callahan TJ, Cappelletti L, Fontana T, Bradwell KR, Harris NL, Casiraghi E, Valentini G, Karlebach G, Deer R, McMurry JA, Haendel MA, Chute CG, Pfaff E, Moffitt R, Spratt H, Singh J, Mungall CJ, Williams AE, Robinson PN. NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study. medRxiv 2021:2021.04.13.21255438. [PMID: 33907758 PMCID: PMC8077581 DOI: 10.1101/2021.04.13.21255438] [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] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.
Collapse
Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Lauren Chan
- Translational and Integrative Sciences Center, Oregon State University, Corvallis, OR, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Tiffany J Callahan
- Computational Bioscience, University of Colorado Anschutz Medical Campus, Boulder, CO, USA
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | | | - Nomi L Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Roma, Italy
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Roma, Italy
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Rachel Deer
- University of Texas Medical Branch, Galveston, TX, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Melissa A Haendel
- 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
| | - Emily Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Heidi Spratt
- University of Texas Medical Branch, Galveston, TX, USA
| | - Jasvinder Singh
- University of Alabama at Birmingham, Birmingham, AL, USA
- Medicine Service, VA Medical Center, Birmingham, AL, USA
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew E Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA
- Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies
- Northeastern University, OHDSI Center at the Roux Institute
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| |
Collapse
|
39
|
Deer RR, Rock MA, Vasilevsky N, Carmody L, Rando H, Anzalone AJ, Basson MD, Bennett TD, Bergquist T, Boudreau EA, Bramante CT, Byrd JB, Callahan TJ, Chan LE, Chu H, Chute CG, Coleman BD, Davis HE, Gagnier J, Greene CS, Hillegass WB, Kavuluru R, Kimble WD, Koraishy FM, Köhler S, Liang C, Liu F, Liu H, Madhira V, Madlock-Brown CR, Matentzoglu N, Mazzotti DR, McMurry JA, McNair DS, Moffitt RA, Monteith TS, Parker AM, Perry MA, Pfaff E, Reese JT, Saltz J, Schuff RA, Solomonides AE, Solway J, Spratt H, Stein GS, Sule AA, Topaloglu U, Vavougios GD, Wang L, Haendel MA, Robinson PN. Characterizing Long COVID: Deep Phenotype of a Complex Condition. EBioMedicine 2021; 74:103722. [PMID: 34839263 PMCID: PMC8613500 DOI: 10.1016/j.ebiom.2021.103722] [Citation(s) in RCA: 102] [Impact Index Per Article: 34.0] [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] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/22/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or "long COVID"), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. METHODS The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. FUNDING We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. INTERPRETATION Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. FUNDING U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411.
Collapse
Affiliation(s)
- Rachel R Deer
- University of Texas Medical Branch, Galveston, TX, USA.
| | | | - Nicole Vasilevsky
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Monarch Initiative
| | - Leigh Carmody
- Monarch Initiative; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Halie Rando
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Alfred J Anzalone
- Department of Neurological Sciences, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Marc D Basson
- Department of Surgery, University of North Dakota School of Medicine and Health Sciences
| | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Eilis A Boudreau
- Department of Neurology; Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239
| | - Carolyn T Bramante
- Departments of Internal Medicine and Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455
| | - James Brian Byrd
- Department of Internal Medicine, Division of Cardiovascular Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109
| | - Tiffany J Callahan
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lauren E Chan
- Monarch Initiative; College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Haitao Chu
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN USA
| | - Christopher G Chute
- Johns Hopkins University, Schools of Medicine, Public Health, and Nursing, Baltimore, MD, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | | | - Joel Gagnier
- Departments of Orthopaedic Surgery & Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Casey S Greene
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - William B Hillegass
- University of Mississippi Medical Center, University of Mississippi Medical Center, Jackson, MS, USA; Departments of Data Science and Medicine
| | | | - Wesley D Kimble
- West Virginia Clinical and Translational Science Institute, West Virginia University, Morgantown, WV, USA
| | | | | | - Chen Liang
- Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Feifan Liu
- Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, MN, USA
| | | | - Charisse R Madlock-Brown
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 920 Madison Ave. Suite 518N, Memphis TN 38613
| | - Nicolas Matentzoglu
- Monarch Initiative; Semanticly Ltd; European Bioinformatics Institute (EMBL-EBI)
| | - Diego R Mazzotti
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Monarch Initiative
| | - Douglas S McNair
- Quantitative Sciences, Global Health Div., Gates Foundation, Seattle, WA 98109, USA
| | | | | | - Ann M Parker
- Pulmonary and Critical Care Medicine, Johns Hopkins University, Schools of Medicine, Baltimore, MD, USA
| | - Mallory A Perry
- Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
| | | | - Justin T Reese
- Monarch Initiative; Lawrence Berkeley National Laboratory
| | - Joel Saltz
- Stony Brook University; Biomedical Informatics
| | | | - Anthony E Solomonides
- Outcomes Research Network, Research Institute, NorthShore University HealthSystem, Evanston, IL 60201, USA; Institute for Translational Medicine, University of Chicago, Chicago, IL, USA
| | - Julian Solway
- Institute for Translational Medicine, University of Chicago, Chicago, IL, USA
| | - Heidi Spratt
- University of Texas Medical Branch, Galveston, TX, USA
| | - Gary S Stein
- University of Vermont Larner College of Medicine, Departments of Biochemistry and Surgery, Burlington, Vermont 05405
| | | | | | - George D Vavougios
- Department of Computer Science and Telecommunications, University of Thessaly, Papasiopoulou 2 - 4, P.C.; 131 - Galaneika, Lamia, Greece; Department of Neurology, Athens Naval Hospital 70 Deinokratous Street, P.C. 115 21 Athens, Greece; Department of Respiratory Medicine, Faculty of Medicine, University of Thessaly, Biopolis, P.C. 41500 Larissa, Greece
| | - Liwei Wang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, MN, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Monarch Initiative.
| | - Peter N Robinson
- Monarch Initiative; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA.
| |
Collapse
|
40
|
Drösler SE, Weber S, Chute CG. ICD-11 extension codes support detailed clinical abstraction and comprehensive classification. BMC Med Inform Decis Mak 2021; 21:278. [PMID: 34753461 PMCID: PMC8577174 DOI: 10.1186/s12911-021-01635-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 09/21/2021] [Indexed: 11/25/2022] Open
Abstract
Background The new International Classification of Diseases—11th revision (ICD-11) succeeds ICD-10. In the three decades since ICD-10 was released, demands for detailed information on the clinical history of a morbid patient have increased. Methods ICD-11 has now implemented an addendum chapter X called “Extension Codes”. This chapter contains numerous codes containing information on concepts including disease stage, severity, histopathology, medicaments, and anatomical details. When linked to a stem code representing a clinical state, the extension codes add significant detail and allow for multidimensional coding. Results This paper discusses the purposes and uses of extension codes and presents three examples of how extension codes can be used in coding clinical detail. Conclusion ICD-11 with its extension codes implemented has the potential to improve precision and evidence based health care worldwide.
Collapse
Affiliation(s)
- Saskia E Drösler
- Faculty of Health Care, Niederrhein University of Applied Sciences, Reinarzstr 49, 47805, Krefeld, Germany.
| | - Stefanie Weber
- Federal Institute for Drugs and Medical Devices, Kurt-Georg-Kiesinger-Allee 3, 53175, Bonn, Germany
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, 2024 E Monument St, Suite 1-200, Baltimore, MD, 21287, USA
| |
Collapse
|
41
|
Harrison JE, Weber S, Jakob R, Chute CG. ICD-11: an international classification of diseases for the twenty-first century. BMC Med Inform Decis Mak 2021; 21:206. [PMID: 34753471 PMCID: PMC8577172 DOI: 10.1186/s12911-021-01534-6] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [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: 12/02/2020] [Accepted: 05/20/2021] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND The International Classification of Diseases (ICD) has long been the main basis for comparability of statistics on causes of mortality and morbidity between places and over time. This paper provides an overview of the recently completed 11th revision of the ICD, focusing on the main innovations and their implications. MAIN TEXT Changes in content reflect knowledge and perspectives on diseases and their causes that have emerged since ICD-10 was developed about 30 years ago. Changes in design and structure reflect the arrival of the networked digital era, for which ICD-11 has been prepared. ICD-11's information framework comprises a semantic knowledge base (the Foundation), a biomedical ontology linked to the Foundation and classifications derived from the Foundation. ICD-11 for Mortality and Morbidity Statistics (ICD-11-MMS) is the primary derived classification and the main successor to ICD-10. Innovations enabled by the new architecture include an online coding tool (replacing the index and providing additional functions), an application program interface to enable remote access to ICD-11 content and services, enhanced capability to capture and combine clinically relevant characteristics of cases and integrated support for multiple languages. CONCLUSIONS ICD-11 was adopted by the World Health Assembly in May 2019. Transition to implementation is in progress. ICD-11 can be accessed at icd.who.int.
Collapse
Affiliation(s)
- James E Harrison
- College of Medicine and Public Health, Flinders University, Adelaide, Australia.
| | - Stefanie Weber
- Federal Institute for Drugs and Medical Devices, Bonn, Germany
| | | | - Christopher G Chute
- Schools of Medicine, Public Health and Nursing, JohnsHopkins University, Baltimore, MD, USA
| |
Collapse
|
42
|
Yang X, Sun J, Patel RC, Zhang J, Guo S, Zheng Q, Olex AL, Olatosi B, Weissman SB, Islam JY, Chute CG, Haendel M, Kirk GD, Li X. Associations between HIV infection and clinical spectrum of COVID-19: a population level analysis based on US National COVID Cohort Collaborative (N3C) data. Lancet HIV 2021; 8:e690-e700. [PMID: 34655550 PMCID: PMC8514200 DOI: 10.1016/s2352-3018(21)00239-3] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 10/31/2022]
Abstract
BACKGROUND Evidence of whether people living with HIV are at elevated risk of adverse COVID-19 outcomes is inconclusive. We aimed to investigate this association using the population-based National COVID Cohort Collaborative (N3C) data in the USA. METHODS We included all adult (aged ≥18 years) COVID-19 cases with any health-care encounter from 54 clinical sites in the USA, with data being deposited into the N3C. The outcomes were COVID-19 disease severity, hospitalisation, and mortality. Encounters in the same health-care system beginning on or after January 1, 2018, were also included to provide information about pre-existing health conditions (eg, comorbidities). Logistic regression models were employed to estimate the association of HIV infection and HIV markers (CD4 cell count, viral load) with hospitalisation, mortality, and clinical severity of COVID-19 (multinomial). The models were initially adjusted for demographic characteristics, then subsequently adjusted for smoking, obesity, and a broad range of comorbidities. Interaction terms were added to assess moderation effects by demographic characteristics. FINDINGS In the harmonised N3C data release set from Jan 1, 2020, to May 8, 2021, there were 1 436 622 adult COVID-19 cases, of these, 13 170 individuals had HIV infection. A total of 26 130 COVID-19 related deaths occurred, with 445 among people with HIV. After adjusting for all the covariates, people with HIV had higher odds of COVID-19 death (adjusted odds ratio 1·29, 95% CI 1·16-1·44) and hospitalisation (1·20, 1·15-1·26), but lower odds of mild or moderate COVID-19 (0·61, 0·59-0·64) than people without HIV. Interaction terms revealed that the elevated odds were higher among older age groups, male, Black, African American, Hispanic, or Latinx adults. A lower CD4 cell count (<200 cells per μL) was associated with all the adverse COVID-19 outcomes, while viral suppression was only associated with reduced hospitalisation. INTERPRETATION Given the COVID-19 pandemic's exacerbating effects on health inequities, public health and clinical communities must strengthen services and support to prevent aggravated COVID-19 outcomes among people with HIV, particularly for those with pronounced immunodeficiency. FUNDING National Center for Advancing Translational Sciences, National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA.
Collapse
Affiliation(s)
- Xueying Yang
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA.
| | - Jing Sun
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rena C Patel
- Department of Medicine, University of Washington, Seattle, WA, USA; Department of Global Health, University of Washington, Seattle, WA, USA
| | - Jiajia Zhang
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Siyuan Guo
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Qulu Zheng
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Amy L Olex
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, USA
| | - Bankole Olatosi
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Sharon B Weissman
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Internal Medicine, School of Medicine, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Jessica Y Islam
- Center for Immunization and Infection in Cancer, Cancer Epidemiology Program, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Melissa Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Gregory D Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Xiaoming Li
- South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA; Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| |
Collapse
|
43
|
Mehta HB, An H, Andersen KM, Mansour O, Madhira V, Rashidi ES, Bates B, Setoguchi S, Joseph C, Kocis PT, Moffitt R, Bennett TD, Chute CG, Garibaldi BT, Alexander GC. Use of Hydroxychloroquine, Remdesivir, and Dexamethasone Among Adults Hospitalized With COVID-19 in the United States : A Retrospective Cohort Study. Ann Intern Med 2021; 174:1395-1403. [PMID: 34399060 PMCID: PMC8372837 DOI: 10.7326/m21-0857] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Relatively little is known about the use patterns of potential pharmacologic treatments of COVID-19 in the United States. OBJECTIVE To use the National COVID Cohort Collaborative (N3C), a large, multicenter, longitudinal cohort, to characterize the use of hydroxychloroquine, remdesivir, and dexamethasone, overall as well as across individuals, health systems, and time. DESIGN Retrospective cohort study. SETTING 43 health systems in the United States. PARTICIPANTS 137 870 adults hospitalized with COVID-19 between 1 February 2020 and 28 February 2021. MEASUREMENTS Inpatient use of hydroxychloroquine, remdesivir, or dexamethasone. RESULTS Among 137 870 persons hospitalized with confirmed or suspected COVID-19, 8754 (6.3%) received hydroxychloroquine, 29 272 (21.2%) remdesivir, and 53 909 (39.1%) dexamethasone during the study period. Since the release of results from the RECOVERY (Randomised Evaluation of COVID-19 Therapy) trial in mid-June, approximately 78% to 84% of people who have had invasive mechanical ventilation have received dexamethasone or other glucocorticoids. The use of hydroxychloroquine increased during March 2020, peaking at 42%, and started declining by April 2020. By contrast, remdesivir and dexamethasone use gradually increased over the study period. Dexamethasone and remdesivir use varied substantially across health centers (intraclass correlation coefficient, 14.2% for dexamethasone and 84.6% for remdesivir). LIMITATION Because most N3C data contributors are academic medical centers, findings may not reflect the experience of community hospitals. CONCLUSION Dexamethasone, an evidence-based treatment of COVID-19, may be underused among persons who are mechanically ventilated. The use of remdesivir and dexamethasone varied across health systems, suggesting variation in patient case mix, drug access, treatment protocols, and quality of care. PRIMARY FUNDING SOURCE National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; and National Institute on Aging.
Collapse
Affiliation(s)
- Hemalkumar B Mehta
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (H.B.M., H.A., K.M.A., E.S.R., C.J.)
| | - Huijun An
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (H.B.M., H.A., K.M.A., E.S.R., C.J.)
| | - Kathleen M Andersen
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (H.B.M., H.A., K.M.A., E.S.R., C.J.)
| | | | | | - Emaan S Rashidi
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (H.B.M., H.A., K.M.A., E.S.R., C.J.)
| | - Benjamin Bates
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, New Jersey (B.B., S.S.)
| | - Soko Setoguchi
- Rutgers Center for Pharmacoepidemiology and Treatment Science, New Brunswick, New Jersey (B.B., S.S.)
| | - Corey Joseph
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (H.B.M., H.A., K.M.A., E.S.R., C.J.)
| | - Paul T Kocis
- Penn State Health, Milton S. Hershey Medical Center, Hershey, Pennsylvania (P.T.K.)
| | | | - Tellen D Bennett
- University of Colorado School of Medicine, University of Colorado, Aurora, Colorado (T.D.B.)
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland (C.G.C.)
| | - Brian T Garibaldi
- Johns Hopkins University School of Medicine, Baltimore, Maryland (B.T.G.)
| | - G Caleb Alexander
- Johns Hopkins Bloomberg School of Public Health and Johns Hopkins School of Medicine, Baltimore, Maryland (G.C.A.)
| |
Collapse
|
44
|
Sun J, Patel RC, Zheng Q, Madhira V, Olex AL, Islam JY, French E, Chiang TPY, Akselrod H, Moffitt R, Alexander GC, Andersen KM, Vinson AJ, Brown TT, Chute CG, Crandall KA, Franceschini N, Mannon RB, Kirk GD. COVID-19 Disease Severity among People with HIV Infection or Solid Organ Transplant in the United States: A Nationally-representative, Multicenter, Observational Cohort Study. medRxiv 2021:2021.07.26.21261028. [PMID: 34341798 PMCID: PMC8328066 DOI: 10.1101/2021.07.26.21261028] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [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: 01/08/2023]
Abstract
Background Individuals with immune dysfunction, including people with HIV (PWH) or solid organ transplant recipients (SOT), might have worse outcomes from COVID-19. We compared odds of COVID-19 outcomes between patients with and without immune dysfunction. Methods We evaluated data from the National COVID-19 Cohort Collaborative (N3C), a multicenter retrospective cohort of electronic medical record (EMR) data from across the United States, on. 1,446,913 adult patients with laboratory-confirmed SARS-CoV-2 infection. HIV, SOT, comorbidity, and HIV markers were identified from EMR data prior to SARS-CoV-2 infection. COVID-19 disease severity within 45 days of SARS-CoV-2 infection was classified into 5 categories: asymptomatic/mild disease with outpatient care; mild disease with emergency department (ED) visit; moderate disease requiring hospitalization; severe disease requiring ventilation or extracorporeal membrane oxygenation (ECMO); and death. We used multivariable, multinomial logistic regression models to compare odds of COVID-19 outcomes between patients with and without immune dysfunction. Findings Compared to patients without immune dysfunction, PWH and SOT had a greater likelihood of having ED visits (adjusted odds ratio [aOR]: 1.28, 95% confidence interval [CI] 1.27-1.29; aOR: 2.61, CI: 2.58-2.65, respectively), requiring ventilation or ECMO (aOR: 1.43, CI: 1.43-1.43; aOR: 4.82, CI: 4.78-4.86, respectively), and death (aOR: 1.20, CI: 1.19-1.20; aOR: 3.38, CI: 3.35-3.41, respectively). Associations were independent of sociodemographic and comorbidity burden. Compared to PWH with CD4>500 cells/mm3, PWH with CD4<350 cells/mm3 were independently at 4.4-, 5.4-, and 7.6-times higher odds for hospitalization, requiring ventilation, and death, respectively. Increased COVID-19 severity was associated with higher levels of HIV viremia. Interpretation Individuals with immune dysfunction have greater risk for severe COVID-19 outcomes. More advanced HIV disease (greater immunosuppression and HIV viremia) was associated with higher odds of severe COVID-19 outcomes. Appropriate prevention and treatment strategies should be investigated to reduce the higher morbidity and mortality associated with COVID-19 among PWH and SOT.
Collapse
Affiliation(s)
- Jing Sun
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Rena C. Patel
- Departments of Medicine and Global Health, University of Washington, Seattle WA, USA
| | - Qulu Zheng
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Amy L. Olex
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, USA
| | - Jessica Y. Islam
- Center for Immunization and Infection in Cancer, Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Evan French
- Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, USA
| | - Teresa Po-Yu Chiang
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Hana Akselrod
- Division of Infectious Diseases, George Washington University School of Medicine and Health Sciences, Washington DC, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook Cancer Center, New York, NY, USA
| | - G. Caleb Alexander
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kathleen M. Andersen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Amanda J. Vinson
- Department of Medicine, Division of Nephrology, Dalhousie University, Halifax, NS, Canada
| | - Todd T. Brown
- Division of Endocrinology, Diabetes, and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Keith A. Crandall
- Computational Biology Institute, Department of Biostatistics & Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington DC, USA
| | - Nora Franceschini
- Department of Epidemiology, UNC Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Roslyn B. Mannon
- Department of Medicine, Division of Nephrology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Gregory D. Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Medicine, Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | |
Collapse
|
45
|
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.
Collapse
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
| |
Collapse
|
46
|
White JML, Lui H, Chute CG, Jakob R, Chalmers RJG. The WHO ICD-11 Classification of Dermatological Disorders: a new comprehensive online skin disease taxonomy designed by and for dermatologists. Br J Dermatol 2021; 186:178-179. [PMID: 34289080 DOI: 10.1111/bjd.20656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 07/13/2021] [Accepted: 07/13/2021] [Indexed: 11/24/2022]
Abstract
In May 2019, the World Health Assembly officially adopted the Eleventh Revision of the International Classification of Diseases (ICD-11)1 . When the current 10th Revision (ICD-10) was released some three decades ago, the world was at the beginning of the modern information technology era. The World Health Organization (WHO) had long recognised that the "one size fits all" nature of ICD-10 and its inability to adapt to change seriously hampered its usefulness in healthcare research and management. The initial designs for ICD-11 were formulated by the WHO in 2007. Since then, many individuals from around the globe have participated in its development.
Collapse
Affiliation(s)
- J M L White
- Department of Dermatology, Erasmus Hospital, Brussels, Belgium.,Ecole de Santé Publique, Université libre de Bruxelles, Belgium.,International League of Dermatological Societies
| | - H Lui
- International League of Dermatological Societies.,Department of Dermatology and Skin Science, Vancouver Coastal Health Research Institute, University of British Columbia, Vancouver, Canada
| | - C G Chute
- Johns Hopkins University Schools of Medicine, Public Health and Nursing, Baltimore, USA
| | - R Jakob
- Team Leader Classifications and Terminologies, World Health Organization, Geneva, Switzerland
| | - R J G Chalmers
- International League of Dermatological Societies.,Centre for Dermatology, University of Manchester, Manchester, United Kingdom
| |
Collapse
|
47
|
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: 153] [Impact Index Per Article: 51.0] [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.
Collapse
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
| |
Collapse
|
48
|
Kahkoska AR, Abrahamsen TJ, Alexander GC, Bennett TD, Chute CG, Haendel MA, Klein KR, Mehta H, Miller JD, Moffitt RA, Stürmer T, Kvist K, Buse JB. Association Between Glucagon-Like Peptide 1 Receptor Agonist and Sodium-Glucose Cotransporter 2 Inhibitor Use and COVID-19 Outcomes. Diabetes Care 2021; 44:1564-1572. [PMID: 34135013 PMCID: PMC8323175 DOI: 10.2337/dc21-0065] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/23/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To determine the respective associations of premorbid glucagon-like peptide-1 receptor agonist (GLP1-RA) and sodium-glucose cotransporter 2 inhibitor (SGLT2i) use, compared with premorbid dipeptidyl peptidase 4 inhibitor (DPP4i) use, with severity of outcomes in the setting of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. RESEARCH DESIGN AND METHODS We analyzed observational data from SARS-CoV-2-positive adults in the National COVID Cohort Collaborative (N3C), a multicenter, longitudinal U.S. cohort (January 2018-February 2021), with a prescription for GLP1-RA, SGLT2i, or DPP4i within 24 months of positive SARS-CoV-2 PCR test. The primary outcome was 60-day mortality, measured from positive SARS-CoV-2 test date. Secondary outcomes were total mortality during the observation period and emergency room visits, hospitalization, and mechanical ventilation within 14 days. Associations were quantified with odds ratios (ORs) estimated with targeted maximum likelihood estimation using a super learner approach, accounting for baseline characteristics. RESULTS The study included 12,446 individuals (53.4% female, 62.5% White, mean ± SD age 58.6 ± 13.1 years). The 60-day mortality was 3.11% (387 of 12,446), with 2.06% (138 of 6,692) for GLP1-RA use, 2.32% (85 of 3,665) for SGLT2i use, and 5.67% (199 of 3,511) for DPP4i use. Both GLP1-RA and SGLT2i use were associated with lower 60-day mortality compared with DPP4i use (OR 0.54 [95% CI 0.37-0.80] and 0.66 [0.50-0.86], respectively). Use of both medications was also associated with decreased total mortality, emergency room visits, and hospitalizations. CONCLUSIONS Among SARS-CoV-2-positive adults, premorbid GLP1-RA and SGLT2i use, compared with DPP4i use, was associated with lower odds of mortality and other adverse outcomes, although DPP4i users were older and generally sicker.
Collapse
Affiliation(s)
- Anna R Kahkoska
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - G Caleb Alexander
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Division of General Internal Medicine, Johns Hopkins Medicine, Baltimore, MD
| | - Tellen D Bennett
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO
| | - Klara R Klein
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC
| | - Hemalkumar Mehta
- Center for Drug Safety and Effectiveness, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Joshua D Miller
- Division of Endocrinology and Metabolism, Department of Medicine, Renaissance School of Medicine at Stony Brook University, Stony Brook, NY
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | | | - John B Buse
- Division of Endocrinology and Metabolism, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC .,NC Translational and Clinical Sciences Institute, University of North Carolina School of Medicine, Chapel Hill, NC
| | | |
Collapse
|
49
|
Taylor CO, Manov NF, Crew KD, Weng C, Connolly JJ, Chute CG, Ford DE, Lehmann H, Rahm AK, Kullo IJ, Caraballo PJ, Holm IA, Mathews D. Preferences for Updates on General Research Results: A Survey of Participants in Genomic Research from Two Institutions. J Pers Med 2021; 11:399. [PMID: 34065005 PMCID: PMC8151672 DOI: 10.3390/jpm11050399] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 04/23/2021] [Accepted: 05/03/2021] [Indexed: 01/11/2023] Open
Abstract
There is a need for multimodal strategies to keep research participants informed about study results. Our aim was to characterize preferences of genomic research participants from two institutions along four dimensions of general research result updates: content, timing, mechanism, and frequency. METHODS We conducted a web-based cross-sectional survey that was administered from 25 June 2018 to 5 December 2018. RESULTS 397 participants completed the survey, most of whom (96%) expressed a desire to receive research updates. Preferences with high endorsement included: update content (brief descriptions of major findings, descriptions of purpose and goals, and educational material); update timing (when the research is completed, when findings are reviewed, when findings are published, and when the study status changes); update mechanism (email with updates, and email newsletter); and update frequency (every three months). Hierarchical cluster analyses based on the four update preferences identified four profiles of participants with similar preference patterns. Very few participants in the largest profile were comfortable with budgeting less money for research activities so that researchers have money to set up services to send research result updates to study participants. CONCLUSION Future studies may benefit from exploring preferences for research result updates, as we have in our study. In addition, this work provides evidence of a need for funders to incentivize researchers to communicate results to participants.
Collapse
Affiliation(s)
- Casey Overby Taylor
- Department of Medicine, Department of Biomedical Engineering, and The Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Natalie Flaks Manov
- Department of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (N.F.M.); (D.E.F.)
| | - Katherine D. Crew
- Department of Medicine and Epidemiology, Columbia University, New York, NY 10032, USA;
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA;
| | - John J. Connolly
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA;
| | - Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD 21205, USA;
| | - Daniel E. Ford
- Department of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA; (N.F.M.); (D.E.F.)
| | - Harold Lehmann
- Department of Medicine, Division of Health Sciences Informatics, Johns Hopkins University, Baltimore, MD 21205, USA;
| | | | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | | | - Ingrid A. Holm
- Division of Genetics and Genomics, Boston Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA;
| | - Debra Mathews
- Johns Hopkins University Berman Institute of Bioethics, Baltimore, MD 21205, USA;
| |
Collapse
|
50
|
Alamgir J, Yajima M, Ergas R, Chen X, Hill N, Munir N, Saeed M, Gersing K, Haendel M, Chute CG, Abid MR. Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records. medRxiv 2021:2021.03.22.21254110. [PMID: 33851170 PMCID: PMC8043466 DOI: 10.1101/2021.03.22.21254110] [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: 11/25/2022]
Abstract
Background Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects. Methods Using a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect. Results We identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients. Conclusions Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.
Collapse
Affiliation(s)
| | - Masanao Yajima
- Boston University, Department of Mathematics and Statistics
| | | | | | | | | | | | | | | | | | | |
Collapse
|