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Cai CX, Nishimura A, Bowring MG, Westlund E, Tran D, Ng JH, Nagy P, Cook M, McLeggon JA, DuVall SL, Matheny ME, Golozar A, Ostropolets A, Minty E, Desai P, Bu F, Toy B, Hribar M, Falconer T, Zhang L, Lawrence-Archer L, Boland MV, Goetz K, Hall N, Shoaibi A, Reps J, Sena AG, Blacketer C, Swerdel J, Jhaveri KD, Lee E, Gilbert Z, Zeger SL, Crews DC, Suchard MA, Hripcsak G, Ryan PB. Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study. Ophthalmol Retina 2024:S2468-6530(24)00118-0. [PMID: 38519026 DOI: 10.1016/j.oret.2024.03.014] [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] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/24/2024]
Abstract
PURPOSE To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Cindy X Cai
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland.
| | - Akihiko Nishimura
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Mary G Bowring
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Erik Westlund
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Diep Tran
- Wilmer Eye Institute, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Jia H Ng
- Division of Kidney Diseases and Hypertension, Donald and Barbara School of Medicine at Hofstra/Northwell, New York
| | - Paul Nagy
- Department of Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, Maryland
| | | | - Jody-Ann McLeggon
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, Utah; Department of Internal Medicine Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah
| | - Michael E Matheny
- VA Informatics and Computing Infrastructure, Tennessee Valley Healthcare System, Nashville, Tennessee; Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee
| | - Asieh Golozar
- Odysseus Data Services, Inc., Cambridge, Massachusetts; OHDSI Center at the Roux Institute, Northeastern University, Boston, Massachusetts
| | | | - Evan Minty
- O'Brien Center for Public Health, Department of Medicine, University of Calgary, Canada
| | - Priya Desai
- Technology / Digital Solutions, Stanford Health Care and Stanford University School of Medicine, Palo Alto, California
| | - Fan Bu
- Department of Biostatistics, University of California - Los Angeles, Los Angeles, California
| | - Brian Toy
- Roski Eye Institute, Keck School of Medicine, University of Southern California; Los Angeles, California
| | - Michelle Hribar
- National Eye Institute, National Institutes of Health, Bethesda, Maryland; Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Linying Zhang
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Laurence Lawrence-Archer
- Odysseus Data Services, Inc., Cambridge, Massachusetts; OHDSI Center at the Roux Institute, Northeastern University, Boston, Massachusetts
| | - Michael V Boland
- Mass Eye and Ear, and Harvard Medical School, Boston, Massachusetts
| | - Kerry Goetz
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan Hall
- Janssen Research and Development, Titusville, New Jersey
| | - Azza Shoaibi
- Janssen Research and Development, Titusville, New Jersey
| | - Jenna Reps
- Janssen Research and Development, Titusville, New Jersey
| | - Anthony G Sena
- Janssen Research and Development, Titusville, New Jersey; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | | | - Joel Swerdel
- Janssen Research and Development, Titusville, New Jersey
| | - Kenar D Jhaveri
- Glomerular Center at Northwell Health, Division of Kidney Diseases and Hypertension, Donald and Barbara School of Medicine at Hofstra/Northwell, New York
| | - Edward Lee
- Roski Eye Institute, Keck School of Medicine, University of Southern California; Los Angeles, California
| | - Zachary Gilbert
- Roski Eye Institute, Keck School of Medicine, University of Southern California; Los Angeles, California
| | - Scott L Zeger
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Deidra C Crews
- Division of Nephrology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Marc A Suchard
- VA Informatics and Computing Infrastructure, US Department of Veterans Affairs, Salt Lake City, Utah; Department of Biostatistics, University of California - Los Angeles, Los Angeles, California
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Patrick B Ryan
- Janssen Research and Development, Titusville, New Jersey
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Makadia R, Shoaibi A, Rao GA, Ostropolets A, Rijnbeek PR, Voss EA, Duarte-Salles T, Ramírez-Anguita JM, Mayer MA, Maljković F, Denaxas S, Nyberg F, Papez V, Sena AG, Alshammari TM, Lai LYH, Haynes K, Suchard MA, Hripcsak G, Ryan PB. Evaluating the impact of alternative phenotype definitions on incidence rates across a global data network. JAMIA Open 2023; 6:ooad096. [PMID: 38028730 PMCID: PMC10662662 DOI: 10.1093/jamiaopen/ooad096] [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: 02/06/2023] [Revised: 07/25/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Objective Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome. Materials and Methods We used 16 data sources to systematically generate and evaluate outcomes for 13 adverse events and their overall background rates. We examined the effect of different modifications (inpatient setting, standardization of code set, and code set changes) to the computable phenotype on background incidence rates. Results Rate ratios (RRs) of the incidence rates from each computable phenotype definition varied across outcomes, with inpatient restriction showing the highest variation from 1 to 11.93. Standardization of code set RRs ranges from 1 to 1.64, and code set changes range from 1 to 2.52. Discussion The modification that has the highest impact is requiring inpatient place of service, leading to at least a 2-fold higher incidence rate in the base definition. Standardization showed almost no change when using source code variations. The strength of the effect in the inpatient restriction is highly dependent on the outcome. Changing definitions from broad to narrow showed the most variability by age/gender/database across phenotypes and less than a 2-fold increase in rate compared to the base definition. Conclusion Characterization of outcomes across a network of databases yields insights into sensitivity and specificity trade-offs when definitions are altered. Outcomes should be thoroughly evaluated prior to use for background rates for their plausibility for use across a global network.
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Affiliation(s)
- Rupa Makadia
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States
| | - Azza Shoaibi
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States
| | - Gowtham A Rao
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States
| | - Anna Ostropolets
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10027, United States
| | - Peter R Rijnbeek
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands
| | - Erica A Voss
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States
| | - Talita Duarte-Salles
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, 08007, Spain
| | - Juan Manuel Ramírez-Anguita
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, 08003, Spain
| | - Miguel A Mayer
- Management Control Department, Parc de Salut Mar (PSMAR), Barcelona, 08007, Spain
| | - Filip Maljković
- Research and Development, Heliant d.o.o, Belgrade, 11000, Serbia
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, NW1 2DA, United Kingdom
- British Heart Foundation Data Science Centre, HDR, London, NW1 2DA, United Kingdom
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, 40530, Sweden
| | - Vaclav Papez
- Institute of Health Informatics, University College London, London, NW1 2DA, United Kingdom
| | - Anthony G Sena
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, 3000 CA, The Netherlands
| | - Thamir M Alshammari
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- College of Pharmacy, Prince Sattam Bin Abdulaziz University, Riyadh, 11942, Saudi Arabia
| | - Lana Y H Lai
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, M13 9PL, United Kingdom
| | - Kevin Haynes
- Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States
| | - Marc A Suchard
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA 90025, United States
| | - George Hripcsak
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10027, United States
| | - Patrick B Ryan
- OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY 10027, United States
- Global Epidemiology, Janssen Pharmaceutical Research and Development, LLC, Titusville, NJ 08560, United States
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10027, United States
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3
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Yang C, Williams RD, Swerdel JN, Almeida JR, Brouwer ES, Burn E, Carmona L, Chatzidionysiou K, Duarte-Salles T, Fakhouri W, Hottgenroth A, Jani M, Kolde R, Kors JA, Kullamaa L, Lane J, Marinier K, Michel A, Stewart HM, Prats-Uribe A, Reisberg S, Sena AG, Torre CO, Verhamme K, Vizcaya D, Weaver J, Ryan P, Prieto-Alhambra D, Rijnbeek PR. Development and external validation of prediction models for adverse health outcomes in rheumatoid arthritis: A multinational real-world cohort analysis. Semin Arthritis Rheum 2022; 56:152050. [PMID: 35728447 DOI: 10.1016/j.semarthrit.2022.152050] [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: 02/21/2022] [Revised: 05/11/2022] [Accepted: 06/10/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Identification of rheumatoid arthritis (RA) patients at high risk of adverse health outcomes remains a major challenge. We aimed to develop and validate prediction models for a variety of adverse health outcomes in RA patients initiating first-line methotrexate (MTX) monotherapy. METHODS Data from 15 claims and electronic health record databases across 9 countries were used. Models were developed and internally validated on Optum® De-identified Clinformatics® Data Mart Database using L1-regularized logistic regression to estimate the risk of adverse health outcomes within 3 months (leukopenia, pancytopenia, infection), 2 years (myocardial infarction (MI) and stroke), and 5 years (cancers [colorectal, breast, uterine] after treatment initiation. Candidate predictors included demographic variables and past medical history. Models were externally validated on all other databases. Performance was assessed using the area under the receiver operator characteristic curve (AUC) and calibration plots. FINDINGS Models were developed and internally validated on 21,547 RA patients and externally validated on 131,928 RA patients. Models for serious infection (AUC: internal 0.74, external ranging from 0.62 to 0.83), MI (AUC: internal 0.76, external ranging from 0.56 to 0.82), and stroke (AUC: internal 0.77, external ranging from 0.63 to 0.95), showed good discrimination and adequate calibration. Models for the other outcomes showed modest internal discrimination (AUC < 0.65) and were not externally validated. INTERPRETATION We developed and validated prediction models for a variety of adverse health outcomes in RA patients initiating first-line MTX monotherapy. Final models for serious infection, MI, and stroke demonstrated good performance across multiple databases and can be studied for clinical use. FUNDING This activity under the European Health Data & Evidence Network (EHDEN) has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 806968. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA.
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Affiliation(s)
- Cynthia Yang
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.
| | - Ross D Williams
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Joel N Swerdel
- Janssen Research and Development, Titusville, NJ, United States
| | | | - Emily S Brouwer
- Janssen Research and Development, Titusville, NJ, United States
| | - Edward Burn
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom; Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Walid Fakhouri
- Eli Lilly and Company, Windlesham, Surrey, United Kingdom
| | | | - Meghna Jani
- Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom
| | - Raivo Kolde
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Jan A Kors
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lembe Kullamaa
- Department of Epidemiology and Biostatistics, National Institute for Health Development, Tallinn, Estonia; Institute of Family Medicine and Public Health, University of Tartu, Tartu, Estonia; European Patients' Forum, Brussels, Belgium
| | - Jennifer Lane
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | | | - Albert Prats-Uribe
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Sulev Reisberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia; STACC, Tartu, Estonia; Quretec, Tartu, Estonia
| | - Anthony G Sena
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; Janssen Research and Development, Titusville, NJ, United States
| | | | - Katia Verhamme
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - James Weaver
- Janssen Research and Development, Titusville, NJ, United States; Observational Health Data Sciences and Informatics, New York, NY, United States
| | - Patrick Ryan
- Janssen Research and Development, Titusville, NJ, United States; Observational Health Data Sciences and Informatics, New York, NY, United States
| | - Daniel Prieto-Alhambra
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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4
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Nishimura A, Xie J, Kostka K, Duarte-Salles T, Fernández Bertolín S, Aragón M, Blacketer C, Shoaibi A, DuVall SL, Lynch K, Matheny ME, Falconer T, Morales DR, Conover MM, Chan You S, Pratt N, Weaver J, Sena AG, Schuemie MJ, Reps J, Reich C, Rijnbeek PR, Ryan PB, Hripcsak G, Prieto-Alhambra D, Suchard MA. International cohort study indicates no association between alpha-1 blockers and susceptibility to COVID-19 in benign prostatic hyperplasia patients. Front Pharmacol 2022; 13:945592. [PMID: 36188566 PMCID: PMC9518954 DOI: 10.3389/fphar.2022.945592] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/25/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose: Alpha-1 blockers, often used to treat benign prostatic hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storm release. The proposed treatment based on this hypothesis currently lacks support from reliable real-world evidence, however. We leverage an international network of large-scale healthcare databases to generate comprehensive evidence in a transparent and reproducible manner. Methods: In this international cohort study, we deployed electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We assessed association between alpha-1 blocker use and risks of three COVID-19 outcomes—diagnosis, hospitalization, and hospitalization requiring intensive services—using a prevalent-user active-comparator design. We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We pooled database-specific estimates through random effects meta-analysis. Results: Our study overall included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH medications. We observed no significant difference in their risks for any of the COVID-19 outcomes, with our meta-analytic HR estimates being 1.02 (95% CI: 0.92–1.13) for diagnosis, 1.00 (95% CI: 0.89–1.13) for hospitalization, and 1.15 (95% CI: 0.71–1.88) for hospitalization requiring intensive services. Conclusion: We found no evidence of the hypothesized reduction in risks of the COVID-19 outcomes from the prevalent-use of alpha-1 blockers—further research is needed to identify effective therapies for this novel disease.
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Affiliation(s)
- Akihiko Nishimura
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Junqing Xie
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, United Kingdom
| | - Kristin Kostka
- Real World Solutions, IQVIA, Cambridge, MA, United States
- The OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, United States
| | - Talita Duarte-Salles
- Fundació Institut Universitari Per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernández Bertolín
- Fundació Institut Universitari Per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - María Aragón
- Fundació Institut Universitari Per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
| | - Azza Shoaibi
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
| | - Scott L. DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, United States
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Kristine Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, United States
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States
| | - Michael E. Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, United States
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Daniel R. Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, United Kingdom
- Department of Public Health, University of Southern Denmark, Southern Denmark, Denmark
| | - Mitchell M. Conover
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
| | - Seng Chan You
- Department of Preventive Medicine and Public Health, Yonsei University College of Medicine, Seoul, South Korea
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - James Weaver
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
| | - Anthony G. Sena
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Martijn J. Schuemie
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jenna Reps
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
| | | | - Peter R. Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Patrick B. Ryan
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, United States
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, United Kingdom
- *Correspondence: Daniel Prieto-Alhambra,
| | - Marc A. Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States
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5
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Ostropolets A, Li X, Makadia R, Rao G, Rijnbeek PR, Duarte-Salles T, Sena AG, Shaoibi A, Suchard MA, Ryan PB, Prieto-Alhambra D, Hripcsak G. Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases. Front Pharmacol 2022; 13:814198. [PMID: 35559254 PMCID: PMC9087898 DOI: 10.3389/fphar.2022.814198] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [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: 11/12/2021] [Accepted: 03/17/2022] [Indexed: 01/01/2023] Open
Abstract
Objective: Background incidence rates are routinely used in safety studies to evaluate an association of an exposure and outcome. Systematic research on sensitivity of rates to the choice of the study parameters is lacking. Materials and Methods: We used 12 data sources to systematically examine the influence of age, race, sex, database, time-at-risk, season and year, prior observation and clean window on incidence rates using 15 adverse events of special interest for COVID-19 vaccines as an example. For binary comparisons we calculated incidence rate ratios and performed random-effect meta-analysis. Results: We observed a wide variation of background rates that goes well beyond age and database effects previously observed. While rates vary up to a factor of 1,000 across age groups, even after adjusting for age and sex, the study showed residual bias due to the other parameters. Rates were highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start. Anchoring on a healthcare encounter yielded higher incidence comparing to a random date, especially for short time-at-risk. Incidence rates were highly influenced by the choice of the database (varying by up to a factor of 100), clean window choice and time-at-risk duration, and less so by secular or seasonal trends. Conclusion: Comparing background to observed rates requires appropriate adjustment and careful time-at-risk start and duration choice. Results should be interpreted in the context of study parameter choices.
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Affiliation(s)
| | - Xintong Li
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom
| | - Rupa Makadia
- Janssen Research and Development, Titusville, NJ, United States
| | - Gowtham Rao
- Janssen Research and Development, Titusville, NJ, United States
| | - Peter R. Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Talita Duarte-Salles
- Fundacio Institut Universitari per a la Recerca a L’Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Anthony G. Sena
- Janssen Research and Development, Titusville, NJ, United States
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Azza Shaoibi
- Janssen Research and Development, Titusville, NJ, United States
| | - Marc A. Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick B. Ryan
- Columbia University Medical Center, New York, NY, United States
- Janssen Research and Development, Titusville, NJ, United States
| | | | - George Hripcsak
- Columbia University Medical Center, New York, NY, United States
- New York-Presbyterian Hospital, New York, NY, United States
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6
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Kostka K, Duarte-Salles T, Prats-Uribe A, Sena AG, Pistillo A, Khalid S, Lai LYH, Golozar A, Alshammari TM, Dawoud DM, Nyberg F, Wilcox AB, Andryc A, Williams A, Ostropolets A, Areia C, Jung CY, Harle CA, Reich CG, Blacketer C, Morales DR, Dorr DA, Burn E, Roel E, Tan EH, Minty E, DeFalco F, de Maeztu G, Lipori G, Alghoul H, Zhu H, Thomas JA, Bian J, Park J, Martínez Roldán J, Posada JD, Banda JM, Horcajada JP, Kohler J, Shah K, Natarajan K, Lynch KE, Liu L, Schilling LM, Recalde M, Spotnitz M, Gong M, Matheny ME, Valveny N, Weiskopf NG, Shah N, Alser O, Casajust P, Park RW, Schuff R, Seager S, DuVall SL, You SC, Song S, Fernández-Bertolín S, Fortin S, Magoc T, Falconer T, Subbian V, Huser V, Ahmed WUR, Carter W, Guan Y, Galvan Y, He X, Rijnbeek PR, Hripcsak G, Ryan PB, Suchard MA, Prieto-Alhambra D. Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS. Clin Epidemiol 2022; 14:369-384. [PMID: 35345821 PMCID: PMC8957305 DOI: 10.2147/clep.s323292] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [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: 06/18/2021] [Accepted: 01/27/2022] [Indexed: 01/20/2023] Open
Abstract
Purpose Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Patients and Methods We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. Results We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. Conclusion We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.
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Affiliation(s)
- Kristin Kostka
- IQVIA, Cambridge, MA, USA
- OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Anthony G Sena
- Janssen Research & Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sara Khalid
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Lana Y H Lai
- School of Medical Sciences, University of Manchester, Manchester, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Dalia M Dawoud
- National Institute for Health and Care Excellence, London, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Adam B Wilcox
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- Unviersity of Washington Medicine, Seattle, WA, USA
| | - Alan Andryc
- Janssen Research & Development, Titusville, NJ, USA
| | - Andrew Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Chi Young Jung
- Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea
| | | | - Christian G Reich
- IQVIA, Cambridge, MA, USA
- OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA
| | - Clair Blacketer
- Janssen Research & Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - David A Dorr
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Eng Hooi Tan
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Evan Minty
- O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada
| | | | | | - Gigi Lipori
- University of Florida Health, Gainesville, FL, USA
| | - Hiba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | - Hong Zhu
- Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Jason A Thomas
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Jiang Bian
- University of Florida Health, Gainesville, FL, USA
| | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
| | - Jordi Martínez Roldán
- Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain
| | - Jose D Posada
- Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA
| | - Juan M Banda
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Juan P Horcajada
- Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain
| | - Julianna Kohler
- United States Agency for International Development, Washington, DC, USA
| | - Karishma Shah
- Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Li Liu
- Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China
| | - Lisa M Schilling
- Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | - Mengchun Gong
- Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Nicole G Weiskopf
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Nigam Shah
- Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Robert Schuff
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | | | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Seng Chan You
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea
| | - Seokyoung Song
- Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Tanja Magoc
- University of Florida Health, Gainesville, FL, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, AZ, USA
| | - Vojtech Huser
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Waheed-Ul-Rahman Ahmed
- Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK
- College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK
| | - William Carter
- Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yin Guan
- DHC Technologies Co. Ltd., Beijing, People’s Republic of China
| | | | - Xing He
- University of Florida Health, Gainesville, FL, USA
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Patrick B Ryan
- Janssen Research & Development, Titusville, NJ, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Marc A Suchard
- Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USA
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7
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Reyes C, Pistillo A, Fernández-Bertolín S, Recalde M, Roel E, Puente D, Sena AG, Blacketer C, Lai L, Alshammari TM, Ahmed WUR, Alser O, Alghoul H, Areia C, Dawoud D, Prats-Uribe A, Valveny N, de Maeztu G, Sorlí Redó L, Martinez Roldan J, Lopez Montesinos I, Schilling LM, Golozar A, Reich C, Posada JD, Shah N, You SC, Lynch KE, DuVall SL, Matheny ME, Nyberg F, Ostropolets A, Hripcsak G, Rijnbeek PR, Suchard MA, Ryan P, Kostka K, Duarte-Salles T. Characteristics and outcomes of patients with COVID-19 with and without prevalent hypertension: a multinational cohort study. BMJ Open 2021; 11:e057632. [PMID: 34937726 PMCID: PMC8704062 DOI: 10.1136/bmjopen-2021-057632] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 11/09/2021] [Indexed: 12/29/2022] Open
Abstract
OBJECTIVE To characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients. DESIGN AND SETTING This is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020. PARTICIPANTS Two non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days. OUTCOMES Demographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported. RESULTS We identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension. CONCLUSIONS COVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.
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Affiliation(s)
- Carlen Reyes
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Diana Puente
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Anthony G Sena
- Janssen Research and Development Titusville, Titusville, New Jersey, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Clair Blacketer
- Janssen Research and Development Titusville, Titusville, New Jersey, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lana Lai
- School of Medical Sciences, The University of Manchester, Manchester, UK
| | | | - Waheed-Ui-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Botnar Research Center, Oxford, UK
- College of Medicine and Health, University of Exeter, St Luke's Campus, Exeter, UK
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Dalia Dawoud
- National Institute for Health and Care Excellence (NICE), London, UK
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Albert Prats-Uribe
- Center for Statistics in Medicine, NDORMS, University of Oxford, Botnar Research Center, Nuffield Orthopaedic Center, Oxford, UK
| | | | | | - Luisa Sorlí Redó
- Universitat Autonoma de Barcelona, Barcelona, Spain
- Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d'Investigació Mèdica (IMIM), Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Jordi Martinez Roldan
- Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain
| | - Inmaculada Lopez Montesinos
- Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d'Investigació Mèdica (IMIM), Barcelona, Spain
| | - Lisa M Schilling
- University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA
| | - Asieh Golozar
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
- Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | | | - Jose D Posada
- Stanford University School of Medicine, Stanford, California, USA
| | - Nigam Shah
- Stanford University School of Medicine, Stanford, California, USA
| | - Seng Chan You
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea (the Republic of)
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, The University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, The University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Michael E Matheny
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah, USA
- Department of Internal Medicine, The University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
- Medical Informatics Services, New York-Presbyterial Hospital, New York, NY, USA
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Publich Health, University of California, Los Angeles, California, USA
| | - Patrick Ryan
- Janssen Research and Development Titusville, Titusville, New Jersey, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA
| | - Kristin Kostka
- Real-World Solutions, IQVIA, Cambridge, Massachusetts, USA
- The OHDSI Center at the Roux Institute, Northeastern University, Portland, ME, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
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8
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Khalid S, Yang C, Blacketer C, Duarte-Salles T, Fernández-Bertolín S, Kim C, Park RW, Park J, Schuemie MJ, Sena AG, Suchard MA, You SC, Rijnbeek PR, Reps JM. A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data. Comput Methods Programs Biomed 2021; 211:106394. [PMID: 34560604 PMCID: PMC8420135 DOI: 10.1016/j.cmpb.2021.106394] [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] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). METHODS We show step-by-step how to implement the analytics pipeline for the question: 'In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?'. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. RESULTS Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. CONCLUSION Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.
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Affiliation(s)
- Sara Khalid
- Botnar Research Centre, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Cynthia Yang
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a ľAtenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a ľAtenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Chungsoo Kim
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea; Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea
| | - Martijn J Schuemie
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Anthony G Sena
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands; Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Marc A Suchard
- Departments of Biomathematics, University of California, Los Angeles, USA
| | - Seng Chan You
- Department of Preventive Medicine and Public Health, Yonsei University College of Medicine, Republic of Korea
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jenna M Reps
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA.
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9
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Tan EH, Sena AG, Prats-Uribe A, You SC, Ahmed WUR, Kostka K, Reich C, Duvall SL, Lynch KE, Matheny ME, Duarte-Salles T, Bertolin SF, Hripcsak G, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Blacketer C, Alshammari TM, Alghoul H, Alser O, Lane JCE, Dawoud DM, Shah K, Yang Y, Zhang L, Areia C, Golozar A, Recalde M, Casajust P, Jonnagaddala J, Subbian V, Vizcaya D, Lai LYH, Nyberg F, Morales DR, Posada JD, Shah NH, Gong M, Vivekanantham A, Abend A, Minty EP, Suchard M, Rijnbeek P, Ryan PB, Prieto-Alhambra D. COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries. Rheumatology (Oxford) 2021; 60:SI37-SI50. [PMID: 33725121 PMCID: PMC7989171 DOI: 10.1093/rheumatology/keab250] [Citation(s) in RCA: 21] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/07/2021] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVE Patients with autoimmune diseases were advised to shield to avoid coronavirus disease 2019 (COVID-19), but information on their prognosis is lacking. We characterized 30-day outcomes and mortality after hospitalization with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. METHODS A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center [USA, Optum (USA), Department of Veterans Affairs (USA), Information System for Research in Primary Care-Hospitalization Linked Data (Spain) and claims data from IQVIA Open Claims (USA) and Health Insurance and Review Assessment (South Korea). All patients with prevalent autoimmune diseases, diagnosed and/or hospitalized between January and June 2020 with COVID-19, and similar patients hospitalized with influenza in 2017-18 were included. Outcomes were death and complications within 30 days of hospitalization. RESULTS We studied 133 589 patients diagnosed and 48 418 hospitalized with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalized vs diagnosed patients with COVID-19. Compared with 70 660 hospitalized with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2-4.3% vs 6.32-24.6%). CONCLUSION Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.
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Affiliation(s)
- Eng Hooi Tan
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | - Anthony G Sena
- Janssen Research and Development, Titusville, NJ USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, OX3, 7LD, UK
- College of Medicine and Health, University of Exeter, St Luke’s, 2LU, USA
| | | | | | - Scott L Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernandez Bertolin
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Clair Blacketer
- Janssen Research and Development, Titusville, NJ USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, 02114, MA, USA
| | - Jennifer C E Lane
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | | | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, OX3, 7LD, UK
| | - Yue Yang
- Digital China Health Technologies Co., LTD, Beijing 100085, China
| | - Lin Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China
- Melbourne School of Population and Global Health, The University of Melbourne, Victoria 3015, Australia
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, NY, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health,, Baltimore, MD, USA
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autonoma de Barcelona, Bellaterra, Spain
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | | | - Vignesh Subbian
- College of Engineering, The University of Arizona Tucson, Arizona, USA
| | - David Vizcaya
- Bayer Pharmaceuticals, Sant Joan Despi, Barcelona, Spain
| | - Lana Y H Lai
- School of Medical Sciences, University of Manchester, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel R Morales
- Division of Population Health Sciences, University of Dundee, Dundee, Scotland, UK
| | - Jose D Posada
- Stanford Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Nigam H Shah
- Stanford Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Mengchun Gong
- Health Management Institute, Southern Medical University, Guangzhou, China
| | - Arani Vivekanantham
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, OX3, 7LD, UK
| | - Aaron Abend
- Autoimmune Registry Inc., Guilford, CT 06437, USA
| | - Evan P Minty
- O’Brien School for Public Health, Faculty of Medicine, University of Calgary, Calgary, Alberta, T2N, 1N4, Canada
| | - Marc Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Patrick B Ryan
- Janssen Research and Development, Titusville, NJ USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
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10
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Roel E, Pistillo A, Recalde M, Sena AG, Fernández-Bertolín S, Aragón M, Puente D, Ahmed WUR, Alghoul H, Alser O, Alshammari TM, Areia C, Blacketer C, Carter W, Casajust P, Culhane AC, Dawoud D, DeFalco F, DuVall SL, Falconer T, Golozar A, Gong M, Hester L, Hripcsak G, Tan EH, Jeon H, Jonnagaddala J, Lai LYH, Lynch KE, Matheny ME, Morales DR, Natarajan K, Nyberg F, Ostropolets A, Posada JD, Prats-Uribe A, Reich CG, Rivera DR, Schilling LM, Soerjomataram I, Shah K, Shah NH, Shen Y, Spotniz M, Subbian V, Suchard MA, Trama A, Zhang L, Zhang Y, Ryan PB, Prieto-Alhambra D, Kostka K, Duarte-Salles T. Characteristics and Outcomes of Over 300,000 Patients with COVID-19 and History of Cancer in the United States and Spain. Cancer Epidemiol Biomarkers Prev 2021; 30:1884-1894. [PMID: 34272262 PMCID: PMC8974356 DOI: 10.1158/1055-9965.epi-21-0266] [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] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/26/2021] [Accepted: 07/07/2021] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. METHODS We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. RESULTS We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%-18% and 1%-14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. CONCLUSIONS Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. IMPACT This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.
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Affiliation(s)
- Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Spain
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Spain
| | - Anthony G Sena
- Janssen Research and Development, Titusville, New Jersey
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Maria Aragón
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Diana Puente
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Spain
| | - Waheed-Ul-Rahman Ahmed
- NDORMS, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, United Kingdom
- College of Medicine and Health, University of Exeter, St Luke's Campus, Heavitree Road, Exeter, United Kingdom
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | | | - William Carter
- Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Aedin C Culhane
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Dalia Dawoud
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Frank DeFalco
- Janssen Research and Development, Titusville, New Jersey
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, New York
- New York-Presbyterian Hospital, New York, New York
| | - Asieh Golozar
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, Maryland
- Pharmacoepidemiology, Regeneron Pharmaceuticals, Westchester County, New York
| | - Mengchun Gong
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Laura Hester
- Janssen Research and Development, LLC, Raritan, New Jersey
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York
- New York-Presbyterian Hospital, New York, New York
| | - Eng Hooi Tan
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom
| | - Hokyun Jeon
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Gyeonggi-do, Republic of Korea
| | | | - Lana Y H Lai
- School of Medical Sciences, University of Manchester, Manchester, United Kingdom
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, Utah
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, United Kingdom
- University of Southern Denmark, Odense, Denmark
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, New York
- New York-Presbyterian Hospital, New York, New York
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - José D Posada
- Department of Medicine, School of Medicine, Stanford University, Redwood City, California
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom
| | | | - Donna R Rivera
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, Maryland
| | - Lisa M Schilling
- Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Isabelle Soerjomataram
- Section of Cancer Surveillance, International Agency for Research on Cancer, Lyon, France
| | - Karishma Shah
- NDORMS, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, United Kingdom
| | - Nigam H Shah
- Department of Medicine, School of Medicine, Stanford University, Redwood City, California
| | - Yang Shen
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Matthew Spotniz
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Vignesh Subbian
- College of Engineering, University of Arizona, Tucson, Arizona
| | - Marc A Suchard
- Fielding School of Public Health, University of California, Los Angeles, California
| | - Annalisa Trama
- Fondazione IRCSS Istituto Nazionale dei Tumori, Milan, Italy
| | - Lin Zhang
- School of Population Medicine and Public Health, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- School of Population Health and Global Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Ying Zhang
- Digital Health China Technologies Co., Ltd., Beijing, China
| | - Patrick B Ryan
- Janssen Research and Development, Titusville, New Jersey
- Department of Biomedical Informatics, Columbia University, New York, New York
| | | | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
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11
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Duarte-Salles T, Vizcaya D, Pistillo A, Casajust P, Sena AG, Lai LYH, Prats-Uribe A, Ahmed WUR, Alshammari TM, Alghoul H, Alser O, Burn E, You SC, Areia C, Blacketer C, DuVall S, Falconer T, Fernandez-Bertolin S, Fortin S, Golozar A, Gong M, Tan EH, Huser V, Iveli P, Morales DR, Nyberg F, Posada JD, Recalde M, Roel E, Schilling LM, Shah NH, Shah K, Suchard MA, Zhang L, Zhang Y, Williams AE, Reich CG, Hripcsak G, Rijnbeek P, Ryan P, Kostka K, Prieto-Alhambra D. Thirty-Day Outcomes of Children and Adolescents With COVID-19: An International Experience. Pediatrics 2021; 148:peds.2020-042929. [PMID: 34049958 DOI: 10.1542/peds.2020-042929] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/13/2021] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease 2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017-2018. METHODS International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age <18 between January and June 2020. We described baseline demographics, comorbidities, symptoms, 30-day in-hospital treatments, and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome, multisystem inflammatory syndrome in children, and death. RESULTS A total of 242 158 children and adolescents diagnosed and 9769 hospitalized with COVID-19 and 2 084 180 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were more common among those hospitalized with versus diagnosed with COVID-19. Dyspnea, bronchiolitis, anosmia, and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital prevalent treatments for COVID-19 included repurposed medications (<10%) and adjunctive therapies: systemic corticosteroids (6.8%-7.6%), famotidine (9.0%-28.1%), and antithrombotics such as aspirin (2.0%-21.4%), heparin (2.2%-18.1%), and enoxaparin (2.8%-14.8%). Hospitalization was observed in 0.3% to 1.3% of the cohort diagnosed with COVID-19, with undetectable (n < 5 per database) 30-day fatality. Thirty-day outcomes including pneumonia and hypoxemia were more frequent in COVID-19 than influenza. CONCLUSIONS Despite negligible fatality, complications including hospitalization, hypoxemia, and pneumonia were more frequent in children and adolescents with COVID-19 than with influenza. Dyspnea, anosmia, and gastrointestinal symptoms could help differentiate diagnoses. A wide range of medications was used for the inpatient management of pediatric COVID-19.
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Affiliation(s)
- Talita Duarte-Salles
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | | | - Andrea Pistillo
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Anthony G Sena
- Janssen Research & Development, Titusville, New Jersey.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Lana Yin Hui Lai
- School of Medical Sciences, University of Manchester, Manchester, United Kingdom
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences
| | - Waheed-Ul-Rahman Ahmed
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences.,College of Medicine and Health, St Luke's Campus, University of Exeter, Exeter, United Kingdom
| | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital and Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Edward Burn
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain.,Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences
| | - Seng Chan You
- Department of Biomedical Informatics, School of Medicine, Ajou University, Suwon, South Korea
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Clair Blacketer
- Janssen Research & Development, Titusville, New Jersey.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Scott DuVall
- Department of Veterans Affairs, Salt Lake City, Utah.,School of Medicine, University of Utah, Salt Lake City, Utah
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Sergio Fernandez-Bertolin
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | | | - Asieh Golozar
- Regeneron Pharmaceuticals, Tarrytown, New York.,Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | | | - Eng Hooi Tan
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences
| | - Vojtech Huser
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | | | - Daniel R Morales
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jose D Posada
- Department of Medicine, School of Medicine, Stanford University, Stanford, California
| | - Martina Recalde
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain.,Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | - Lisa M Schilling
- Data Science to Patient Value Program, Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Nigam H Shah
- Department of Medicine, School of Medicine, Stanford University, Stanford, California
| | - Karishma Shah
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences
| | - Marc A Suchard
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain.,Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | - Lin Zhang
- Bayer Pharmaceuticals, Sant Joan Despi, Spain.,Bayer Pharmaceuticals, Sant Joan Despi, Spain.,Real-World Evidence, Trial Form Support, Barcelona, Spain.,Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Ying Zhang
- DHC Technologies, Co, Ltd, Beijing, China
| | - Andrew E Williams
- Janssen Research & Development, Titusville, New Jersey.,Janssen Research & Development, Titusville, New Jersey
| | - Christian G Reich
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Patrick Ryan
- Janssen Research & Development, Titusville, New Jersey.,Department of Biomedical Informatics, Columbia University, New York, New York
| | - Kristin Kostka
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences
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12
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Recalde M, Roel E, Pistillo A, Sena AG, Prats-Uribe A, Ahmed WUR, Alghoul H, Alshammari TM, Alser O, Areia C, Burn E, Casajust P, Dawoud D, DuVall SL, Falconer T, Fernández-Bertolín S, Golozar A, Gong M, Lai LYH, Lane JCE, Lynch KE, Matheny ME, Mehta PP, Morales DR, Natarjan K, Nyberg F, Posada JD, Reich CG, Rijnbeek PR, Schilling LM, Shah K, Shah NH, Subbian V, Zhang L, Zhu H, Ryan P, Prieto-Alhambra D, Kostka K, Duarte-Salles T. Characteristics and outcomes of 627 044 COVID-19 patients living with and without obesity in the United States, Spain, and the United Kingdom. Int J Obes (Lond) 2021; 45:2347-2357. [PMID: 34267326 PMCID: PMC8281807 DOI: 10.1038/s41366-021-00893-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [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: 11/23/2020] [Revised: 06/07/2021] [Accepted: 06/24/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. METHODS We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. RESULTS We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8-40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0-33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. CONCLUSION We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.
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Affiliation(s)
- Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Anthony G Sena
- Janssen Research & Development, Titusville, NJ, USA.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, UK.,College of Medicine and Health, University of Exeter, St Luke's Campus, Exeter, UK
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | | | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Dalia Dawoud
- Cairo University, Faculty of Pharmacy, Cairo, Egypt
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Asieh Golozar
- Department of Epidemiology, Johns Hopkins School of Public, Baltimore, MD, USA.,Pharmacoepidemiology, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | | | - Lana Yin Hui Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester, Manchester, UK
| | - Jennifer C E Lane
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, UK
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA.,Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paras P Mehta
- College of Medicine, The University of Arizona, Tucson, AZ, USA
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Karthik Natarjan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.,New York-Presbyterian Hospital, New York, NY, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jose D Posada
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | | | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lisa M Schilling
- Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Oxford, UK
| | - Nigam H Shah
- Department of Medicine, Stanford University, Palo Alto, CA, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, AZ, USA
| | - Lin Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Hong Zhu
- Institute of Health Management, Southern Medical University, Guangzhou, China.,Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Patrick Ryan
- Janssen Research & Development, Titusville, NJ, USA.,Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Kristin Kostka
- Real World Solutions, IQVIA, Cambridge, MA, USA.,The OHDSI Center at the Roux Institute, Northeastern University, Portland, ME, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
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13
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Lane JCE, Weaver J, Kostka K, Duarte-Salles T, Abrahao MTF, Alghoul H, Alser O, Alshammari TM, Areia C, Biedermann P, Banda JM, Burn E, Casajust P, Fister K, Hardin J, Hester L, Hripcsak G, Kaas-Hansen BS, Khosla S, Kolovos S, Lynch KE, Makadia R, Mehta PP, Morales DR, Morgan-Stewart H, Mosseveld M, Newby D, Nyberg F, Ostropolets A, Woong Park R, Prats-Uribe A, Rao GA, Reich C, Rijnbeek P, Sena AG, Shoaibi A, Spotnitz M, Subbian V, Suchard MA, Vizcaya D, Wen H, de Wilde M, Xie J, You SC, Zhang L, Lovestone S, Ryan P, Prieto-Alhambra D. Risk of depression, suicide and psychosis with hydroxychloroquine treatment for rheumatoid arthritis: a multinational network cohort study. Rheumatology (Oxford) 2021; 60:3222-3234. [PMID: 33367863 PMCID: PMC7798671 DOI: 10.1093/rheumatology/keaa771] [Citation(s) in RCA: 12] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 10/19/2020] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES Concern has been raised in the rheumatology community regarding recent regulatory warnings that HCQ used in the coronavirus disease 2019 pandemic could cause acute psychiatric events. We aimed to study whether there is risk of incident depression, suicidal ideation or psychosis associated with HCQ as used for RA. METHODS We performed a new-user cohort study using claims and electronic medical records from 10 sources and 3 countries (Germany, UK and USA). RA patients ≥18 years of age and initiating HCQ were compared with those initiating SSZ (active comparator) and followed up in the short (30 days) and long term (on treatment). Study outcomes included depression, suicide/suicidal ideation and hospitalization for psychosis. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate database-specific calibrated hazard ratios (HRs), with estimates pooled where I2 <40%. RESULTS A total of 918 144 and 290 383 users of HCQ and SSZ, respectively, were included. No consistent risk of psychiatric events was observed with short-term HCQ (compared with SSZ) use, with meta-analytic HRs of 0.96 (95% CI 0.79, 1.16) for depression, 0.94 (95% CI 0.49, 1.77) for suicide/suicidal ideation and 1.03 (95% CI 0.66, 1.60) for psychosis. No consistent long-term risk was seen, with meta-analytic HRs of 0.94 (95% CI 0.71, 1.26) for depression, 0.77 (95% CI 0.56, 1.07) for suicide/suicidal ideation and 0.99 (95% CI 0.72, 1.35) for psychosis. CONCLUSION HCQ as used to treat RA does not appear to increase the risk of depression, suicide/suicidal ideation or psychosis compared with SSZ. No effects were seen in the short or long term. Use at a higher dose or for different indications needs further investigation. TRIAL REGISTRATION Registered with EU PAS (reference no. EUPAS34497; http://www.encepp.eu/encepp/viewResource.htm? id=34498). The full study protocol and analysis source code can be found at https://github.com/ohdsi-studies/Covid19EstimationHydroxychloroquine2.
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Affiliation(s)
- Jennifer C E Lane
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - James Weaver
- Janssen Research and Development, Titusville, NJ, USA
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | | | - Edward Burn
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona,Spain
| | - Kristina Fister
- School of Medicine, Andrija Štampar School of Public Health, University of Zagreb, Zagreb, Croatia
| | - Jill Hardin
- Janssen Research and Development, Titusville, NJ, USA
| | - Laura Hester
- Janssen Research and Development, Titusville, NJ, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Benjamin Skov Kaas-Hansen
- Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark
- NNF Centre for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Sajan Khosla
- Real World Science & Digital, AstraZeneca, Cambridge, UK
| | - Spyros Kolovos
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Kristine E Lynch
- Department of Veterans Affairs, Salt Lake City, UT, USA
- University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Rupa Makadia
- Janssen Research and Development, Titusville, NJ, USA
| | - Paras P Mehta
- College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | | | - Mees Mosseveld
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon-si, Gyeonggi-do, South Korea
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Gowtham A Rao
- Janssen Research and Development, Titusville, NJ, USA
| | | | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Anthony G Sena
- Janssen Research and Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Azza Shoaibi
- Janssen Research and Development, Titusville, NJ, USA
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Vignesh Subbian
- College of Engineering, University of Arizona, Tucson, AZ, USA
| | - Marc A Suchard
- Departments of Biomathematics and Human Genetics David Geffen School of Medicine at UCLA, and Department of Biostatistics, UCLA School of Public Health, South Los Angeles, CA, USA
| | - David Vizcaya
- Bayer Pharmaceuticals, Sant Joan Despi, Barcelona, Spain
| | - Haini Wen
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Marcel de Wilde
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Junqing Xie
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon-si, Gyeonggi-do, South Korea
| | - Lin Zhang
- School of Public Health, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, P.R. China
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Simon Lovestone
- Janssen-Cilag, 50-100 Holmers Farm Way, High Wycombe HP12 4EG, UK
| | - Patrick Ryan
- Janssen Research and Development, Titusville, NJ, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
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14
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Li X, Ostropolets A, Makadia R, Shoaibi A, Rao G, Sena AG, Martinez-Hernandez E, Delmestri A, Verhamme K, Rijnbeek PR, Duarte-Salles T, Suchard MA, Ryan PB, Hripcsak G, Prieto-Alhambra D. Characterising the background incidence rates of adverse events of special interest for covid-19 vaccines in eight countries: multinational network cohort study. BMJ 2021; 373:n1435. [PMID: 35727911 PMCID: PMC8193077 DOI: 10.1136/bmj.n1435] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [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] [Accepted: 06/03/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To quantify the background incidence rates of 15 prespecified adverse events of special interest (AESIs) associated with covid-19 vaccines. DESIGN Multinational network cohort study. SETTING Electronic health records and health claims data from eight countries: Australia, France, Germany, Japan, the Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model. PARTICIPANTS 126 661 070 people observed for at least 365 days before 1 January 2017, 2018, or 2019 from 13 databases. MAIN OUTCOME MEASURES Events of interests were 15 prespecified AESIs (non-haemorrhagic and haemorrhagic stroke, acute myocardial infarction, deep vein thrombosis, pulmonary embolism, anaphylaxis, Bell's palsy, myocarditis or pericarditis, narcolepsy, appendicitis, immune thrombocytopenia, disseminated intravascular coagulation, encephalomyelitis (including acute disseminated encephalomyelitis), Guillain-Barré syndrome, and transverse myelitis). Incidence rates of AESIs were stratified by age, sex, and database. Rates were pooled across databases using random effects meta-analyses and classified according to the frequency categories of the Council for International Organizations of Medical Sciences. RESULTS Background rates varied greatly between databases. Deep vein thrombosis ranged from 387 (95% confidence interval 370 to 404) per 100 000 person years in UK CPRD GOLD data to 1443 (1416 to 1470) per 100 000 person years in US IBM MarketScan Multi-State Medicaid data among women aged 65 to 74 years. Some AESIs increased with age. For example, myocardial infarction rates in men increased from 28 (27 to 29) per 100 000 person years among those aged 18-34 years to 1400 (1374 to 1427) per 100 000 person years in those older than 85 years in US Optum electronic health record data. Other AESIs were more common in young people. For example, rates of anaphylaxis among boys and men were 78 (75 to 80) per 100 000 person years in those aged 6-17 years and 8 (6 to 10) per 100 000 person years in those older than 85 years in Optum electronic health record data. Meta-analytic estimates of AESI rates were classified according to age and sex. CONCLUSION This study found large variations in the observed rates of AESIs by age group and sex, showing the need for stratification or standardisation before using background rates for safety surveillance. Considerable population level heterogeneity in AESI rates was found between databases.
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Affiliation(s)
- Xintong Li
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Rupa Makadia
- Janssen Research and Development, Titusville, NJ, USA
| | - Azza Shoaibi
- Janssen Research and Development, Titusville, NJ, USA
| | - Gowtham Rao
- Janssen Research and Development, Titusville, NJ, USA
| | - Anthony G Sena
- Janssen Research and Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | | | | | - Katia Verhamme
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
- Department of Bio-Analysis, Faculty of Pharmaceutical Sciences, Ghent University, Ottergemsesteenweg, Gent, Belgium
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Talita Duarte-Salles
- Fundacio Institut Universitari per a la recerca a l'Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA
| | - Patrick B Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- Janssen Research and Development, Titusville, NJ, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
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15
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Prats-Uribe A, Sena AG, Lai LYH, Ahmed WUR, Alghoul H, Alser O, Alshammari TM, Areia C, Carter W, Casajust P, Dawoud D, Golozar A, Jonnagaddala J, Mehta PP, Gong M, Morales DR, Nyberg F, Posada JD, Recalde M, Roel E, Shah K, Shah NH, Schilling LM, Subbian V, Vizcaya D, Zhang L, Zhang Y, Zhu H, Liu L, Cho J, Lynch KE, Matheny ME, You SC, Rijnbeek PR, Hripcsak G, Lane JC, Burn E, Reich C, Suchard MA, Duarte-Salles T, Kostka K, Ryan PB, Prieto-Alhambra D. Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study. BMJ 2021; 373:n1038. [PMID: 33975825 PMCID: PMC8111167 DOI: 10.1136/bmj.n1038] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [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] [Accepted: 04/16/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To investigate the use of repurposed and adjuvant drugs in patients admitted to hospital with covid-19 across three continents. DESIGN Multinational network cohort study. SETTING Hospital electronic health records from the United States, Spain, and China, and nationwide claims data from South Korea. PARTICIPANTS 303 264 patients admitted to hospital with covid-19 from January 2020 to December 2020. MAIN OUTCOME MEASURES Prescriptions or dispensations of any drug on or 30 days after the date of hospital admission for covid-19. RESULTS Of the 303 264 patients included, 290 131 were from the US, 7599 from South Korea, 5230 from Spain, and 304 from China. 3455 drugs were identified. Common repurposed drugs were hydroxychloroquine (used in from <5 (<2%) patients in China to 2165 (85.1%) in Spain), azithromycin (from 15 (4.9%) in China to 1473 (57.9%) in Spain), combined lopinavir and ritonavir (from 156 (<2%) in the VA-OMOP US to 2,652 (34.9%) in South Korea and 1285 (50.5%) in Spain), and umifenovir (0% in the US, South Korea, and Spain and 238 (78.3%) in China). Use of adjunctive drugs varied greatly, with the five most used treatments being enoxaparin, fluoroquinolones, ceftriaxone, vitamin D, and corticosteroids. Hydroxychloroquine use increased rapidly from March to April 2020 but declined steeply in May to June and remained low for the rest of the year. The use of dexamethasone and corticosteroids increased steadily during 2020. CONCLUSIONS Multiple drugs were used in the first few months of the covid-19 pandemic, with substantial geographical and temporal variation. Hydroxychloroquine, azithromycin, lopinavir-ritonavir, and umifenovir (in China only) were the most prescribed repurposed drugs. Antithrombotics, antibiotics, H2 receptor antagonists, and corticosteroids were often used as adjunctive treatments. Research is needed on the comparative risk and benefit of these treatments in the management of covid-19.
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Affiliation(s)
- Albert Prats-Uribe
- Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Anthony G Sena
- Janssen Research and Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Lana Yin Hui Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester, Manchester, UK
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- College of Medicine and Health, University of Exeter, Exeter, UK
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza City, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Carlos Areia
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - William Carter
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Dalia Dawoud
- Faculty of Pharmacy, Cairo University, Cairo, Egypt
- National Institute for Health and Care Excellence, London, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, Tarrytown, NY, US
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Paras P Mehta
- College of Medicine, University of Arizona, Tucson, AZ, USA
| | | | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
- Department of Public Health, University of Southern Denmark, Odense, Denmark
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Jose D Posada
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Nigam H Shah
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Vignesh Subbian
- College of Engineering, University of Arizona Tucson, AZ, USA
| | | | - Lin Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia
| | | | - Hong Zhu
- Nanfang University, Southern Medical University, Guangzhou, China
| | - Li Liu
- Nanfang University, Southern Medical University, Guangzhou, China
| | - Jaehyeong Cho
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Healthcare System, Salt Lake City, Utah, USA; Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E Matheny
- VA Informatics and Computing Infrastructure, Tennessee Valley Healthcare System, VA Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Seng Chan You
- Department of Preventive Medicine and Public Health, Yonsei University College of Medicine, Seoul, South Korea
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Jennifer Ce Lane
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Edward Burn
- Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Marc A Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Kristin Kostka
- IQVIA, Cambridge, MA, USA
- OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA
| | - Patrick B Ryan
- Janssen Research and Development, Titusville, NJ, USA
- Columbia University Irving Medical Center, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Pharmaco- and Device Epidemiology, Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
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16
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Li X, Ostropolets A, Makadia R, Shaoibi A, Rao G, Sena AG, Martinez-Hernandez E, Delmestri A, Verhamme K, Rijnbeek PR, Duarte-Salles T, Suchard M, Ryan P, Hripcsak G, Prieto-Alhambra D. Characterizing the incidence of adverse events of special interest for COVID-19 vaccines across eight countries: a multinational network cohort study. medRxiv 2021:2021.03.25.21254315. [PMID: 33791732 PMCID: PMC8010764 DOI: 10.1101/2021.03.25.21254315] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [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: 12/30/2022]
Abstract
BACKGROUND As large-scale immunization programs against COVID-19 proceed around the world, safety signals will emerge that need rapid evaluation. We report population-based, age- and sex-specific background incidence rates of potential adverse events of special interest (AESI) in eight countries using thirteen databases. METHODS This multi-national network cohort study included eight electronic medical record and five administrative claims databases from Australia, France, Germany, Japan, Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model. People observed for at least 365 days before 1 January 2017, 2018, or 2019 were included. We based study outcomes on lists published by regulators: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain-Barre syndrome, hemorrhagic and non-hemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, and transverse myelitis. We calculated incidence rates stratified by age, sex, and database. We pooled rates across databases using random effects meta-analyses. We classified meta-analytic estimates into Council of International Organizations of Medical Sciences categories: very common, common, uncommon, rare, or very rare. FINDINGS We analysed 126,661,070 people. Rates varied greatly between databases and by age and sex. Some AESI (e.g., myocardial infarction, Guillain-Barre syndrome) increased with age, while others (e.g., anaphylaxis, appendicitis) were more common in young people. As a result, AESI were classified differently according to age. For example, myocardial infarction was very rare in children, rare in women aged 35-54 years, uncommon in men and women aged 55-84 years, and common in those aged ≥85 years. INTERPRETATION We report robust baseline rates of prioritised AESI across 13 databases. Age, sex, and variation between databases should be considered if background AESI rates are compared to event rates observed with COVID-19 vaccines.
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Affiliation(s)
- Xintong Li
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Rupa Makadia
- Janssen Research and Development, Titusville, NJ, USA
| | - Azza Shaoibi
- Janssen Research and Development, Titusville, NJ, USA
| | - Gowtham Rao
- Janssen Research and Development, Titusville, NJ, USA
| | - Anthony G. Sena
- Janssen Research and Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Antonella Delmestri
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom
| | - Katia Verhamme
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Talita Duarte-Salles
- Fundacio Institut Universitari per a la recerca a l’Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Marc Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, UCLA, Los Angeles, CA, USA
| | - Patrick Ryan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
- Janssen Research and Development, Titusville, NJ, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
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17
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Nishimura A, Xie J, Kostka K, Duarte-Salles T, Bertolín SF, Aragón M, Blacketer C, Shoaibi A, DuVall SL, Lynch K, Matheny ME, Falconer T, Morales DR, Conover MM, You SC, Pratt N, Weaver J, Sena AG, Schuemie MJ, Reps J, Reich C, Rijnbeek PR, Ryan PB, Hripcsak G, Prieto-Alhambra D, Suchard MA. Alpha-1 blockers and susceptibility to COVID-19 in benign prostate hyperplasia patients : an international cohort study. medRxiv 2021:2021.03.18.21253778. [PMID: 33791740 PMCID: PMC8010772 DOI: 10.1101/2021.03.18.21253778] [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: 12/03/2022]
Abstract
Alpha-1 blockers, often used to treat benign prostate hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storms release. We conducted a prevalent-user active-comparator cohort study to assess association between alpha-1 blocker use and risks of three COVID-19 outcomes: diagnosis, hospitalization, and hospitalization requiring intensive services. Our study included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH therapy during the period between November 2019 and January 2020, found in electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We found no differential risk for any of COVID-19 outcome, pointing to the need for further research on potential COVID-19 therapies.
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Affiliation(s)
| | - Junqing Xie
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, UK
| | - Kristin Kostka
- Real World Solutions, IQVIA, Cambridge, MA, USA
- The OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernández Bertolín
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - María Aragón
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Clair Blacketer
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Azza Shoaibi
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kristine Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, UK
- Department of Public Health, University of Southern Denmark, Denmark
| | - Mitchell M Conover
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Seng Chan You
- Department of Preventive Medicine and Public Health, Yonsei University College of Medicine, Seoul, Korea
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | - James Weaver
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Anthony G Sena
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Martijn J Schuemie
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jenna Reps
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | | | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Patrick B Ryan
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, UK
| | - Marc A Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA
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18
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Burn E, Sena AG, Prats-Uribe A, Spotnitz M, DuVall S, Lynch KE, Matheny ME, Nyberg F, Ahmed WUR, Alser O, Alghoul H, Alshammari T, Zhang L, Casajust P, Areia C, Shah K, Reich C, Blacketer C, Andryc A, Fortin S, Natarajan K, Gong M, Golozar A, Morales D, Rijnbeek P, Subbian V, Roel E, Recalde M, Lane JCE, Vizcaya D, Posada JD, Shah NH, Jonnagaddala J, Lai LYH, Avilés-Jurado FX, Hripcsak G, Suchard MA, Ranzani OT, Ryan P, Prieto-Alhambra D, Kostka K, Duarte-Salles T. Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 842,928 patients hospitalized with COVID-19 in the United States. medRxiv 2021. [PMID: 33269356 PMCID: PMC7709172 DOI: 10.1101/2020.11.25.20229088] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Objective To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO). Design A network cohort study. Setting Seven databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Hospital CDM, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP. Patients Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19. Interventions Dialysis, tracheostomy, and ECMO. Measurements and Main Results 842,928 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 77,853 from IQVIA Hospital CDM, 533,997 from IQVIA Open Claims, 36,717 from Optum EHR, 4,336 from OPTUM SES, 156,187 from Premier, and 10,951 from VA-OMOP). Across the six databases, 35,192 (4.17% [95% CI: 4.13% to 4.22%]) patients received dialysis, 6,950 (0.82% [0.81% to 0.84%]) had a tracheostomy, and 1,568 (0.19% [95% CI: 0.18% to 0.20%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was more common among patients who were younger, male, and with fewer comorbidities. Tracheostomy was broadly used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was generally used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease. Conclusion Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial.
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Affiliation(s)
- Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Centre for Statistics in Medicine, NDORMS, University of Oxford
| | - Anthony G Sena
- Janssen Research & Development, Titusville, NJ, USA.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Scott DuVall
- Department of Veterans Affairs, Salt Lake City, UT, US.,University of Utah School of Medicine, Salt Lake City, UT, US
| | - Kristine E Lynch
- Department of Veterans Affairs, Salt Lake City, UT, US.,Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine,, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK.,College of Medicine and Health, University of Exeter, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Thamir Alshammari
- Medication Safety Research Chair, King Saud University , Riyadh, Saudi Arabia
| | - Lin Zhang
- School of Population Medicine and Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences.,School of Population and Global Health, The University of Melbourne
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | | | | | - Alan Andryc
- Janssen Research & Development, Titusville, NJ, USA
| | | | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | | | - Asieh Golozar
- Regeneron Pharmaceuticals, NY US.,Johns Hopkins Bloomberg School of Public Health, Baltimore, MD US
| | - Daniel Morales
- Division of Population Health and Genomics, University of Dundee
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, Arizona, USA
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.,Universitat Autònoma de Barcelona, Spain
| | | | | | | | | | | | - Lana Yin Hui Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester
| | - Francesc Xavier Avilés-Jurado
- Otorhinolaryngology Head-Neck Surgery Department, Hospital Clínic, IDIBAPS Universitat de Barcelona, Villarroel 170, 08036, Barcelona, Spain.,Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR, Generalitat de Catalunya, 2017-SGR-01581, Barcelona, Spain
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Marc A Suchard
- Department of Biostatistic, UCLA Fielding School of Public Health, University of California, Los Angeles
| | - Otavio T Ranzani
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain.,Pulmonary Division, Heart Institute (InCor, Hospital das Clinicas, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Patrick Ryan
- Janssen Research & Development, Titusville, NJ, USA.,Columbia University, New York, NY, US
| | | | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
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19
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Morales DR, Conover MM, You SC, Pratt N, Kostka K, Duarte-Salles T, Fernández-Bertolín S, Aragón M, DuVall SL, Lynch K, Falconer T, van Bochove K, Sung C, Matheny ME, Lambert CG, Nyberg F, Alshammari TM, Williams AE, Park RW, Weaver J, Sena AG, Schuemie MJ, Rijnbeek PR, Williams RD, Lane JCE, Prats-Uribe A, Zhang L, Areia C, Krumholz HM, Prieto-Alhambra D, Ryan PB, Hripcsak G, Suchard MA. Renin-angiotensin system blockers and susceptibility to COVID-19: an international, open science, cohort analysis. Lancet Digit Health 2021; 3:e98-e114. [PMID: 33342753 PMCID: PMC7834915 DOI: 10.1016/s2589-7500(20)30289-2] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/29/2020] [Accepted: 11/13/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension. METHODS In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296. FINDINGS Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84-1·14) or combination use exposure (1·01, 0·90-1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68-1·21; with heterogeneity of >40%) or combination use (0·95, 0·83-1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79-0·99) and non-significant for monotherapy (0·85, 0·69-1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons. INTERPRETATION No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19. FUNDING Wellcome Trust, UK National Institute for Health Research, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research & Development, IQVIA, South Korean Ministry of Health and Welfare Republic, Australian National Health and Medical Research Council, and European Health Data and Evidence Network.
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Affiliation(s)
- Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Mitchell M Conover
- Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, SA, Australia
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Maria Aragón
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Scott L DuVall
- Department of Veterans Affairs, Salt Lake City, UT, USA; University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kristine Lynch
- Department of Veterans Affairs, Salt Lake City, UT, USA; University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | | | - Cynthia Sung
- Health Services and Systems Research, Duke-NUS Medical School, Singapore
| | - Michael E Matheny
- Geriatric Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN, USA; Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christophe G Lambert
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | | | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - James Weaver
- Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA
| | - Anthony G Sena
- Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Martijn J Schuemie
- Observational Health Data Analytics, Janssen Research & Development, Titusville, NJ, USA
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Ross D Williams
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jennifer C E Lane
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Lin Zhang
- School of Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China; Melbourne School of Public Health, The University of Melbourne, VIC, Australia
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Medicine, Yale University, New Haven, CT, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Patrick B Ryan
- Division of Population Health and Genomics, University of Dundee, Dundee, UK; Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Marc A Suchard
- Department of Biostatistics, Fielding School of Public Health, and Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA.
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20
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Tan EH, Sena AG, Prats-Uribe A, You SC, Ahmed WUR, Kostka K, Reich C, Duvall SL, Lynch KE, Matheny ME, Duarte-Salles T, Bertolin SF, Hripcsak G, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Blacketer C, Alshammari TM, Alghoul H, Alser O, Lane JC, Dawoud DM, Shah K, Yang Y, Zhang L, Areia C, Golozar A, Relcade M, Casajust P, Jonnagaddala J, Subbian V, Vizcaya D, Lai LYH, Nyberg F, Morales DR, Posada JD, Shah NH, Gong M, Vivekanantham A, Abend A, Minty EP, Suchard M, Rijnbeek P, Ryan PB, Prieto-Alhambra D. Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis. medRxiv 2020:2020.11.24.20236802. [PMID: 33269355 PMCID: PMC7709171 DOI: 10.1101/2020.11.24.20236802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [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: 12/24/2022]
Abstract
OBJECTIVE Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza. DESIGN Multinational network cohort study. SETTING Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea). PARTICIPANTS All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included. MAIN OUTCOME MEASURES 30-day complications during hospitalisation and death. RESULTS We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%). CONCLUSIONS Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases. WHAT IS ALREADY KNOWN ABOUT THIS TOPIC Patients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions. WHAT THIS STUDY ADDS Most people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.
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Affiliation(s)
- Eng Hooi Tan
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | - Anthony G. Sena
- Janssen Research and Development, Titusville, NJ USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
- College of Medicine and Health, University of Exeter, St Luke’s Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | | | | | - Scott L. Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kristine E. Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Michael E. Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernandez Bertolin
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, US
- New York-Presbyterian Hospital, New York, NY, US
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, US
- New York-Presbyterian Hospital, New York, NY, US
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Clair Blacketer
- Janssen Research and Development, Titusville, NJ USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, 02114, Massachusetts, USA
| | - Jennifer C.E. Lane
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
| | | | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | | | - Lin Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Science & Peking Union Medical College, Beijing 100730, China
- Melbourne School of Population and Global Health, The University of Melbourne, Victoria 3015, Australia
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, OX3 9DU, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, NY US
- Departament of Epidemiology, Johns Hopkins School of Public, Baltimore MD
| | - Martina Relcade
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autonoma de Barcelona, Spain
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | | | - Vignesh Subbian
- College of Engineering, The University of Arizona Tucson, Arizona, USA
| | | | - Lana YH Lai
- School of Medical Sciences, University of Manchester, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Jose D. Posada
- Stanford Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University
| | - Nigam H. Shah
- Stanford Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University
| | - Mengchun Gong
- Health Management Institute, Southern Medical University
| | - Arani Vivekanantham
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Botnar Research Centre, Windmill Road, Oxford, OX3 7LD, UK
| | - Aaron Abend
- Autoimmune Registry Inc., 125 West Lane, Guilford, CT 06437
| | - Evan P Minty
- O’Brien School for Public Health, Faculty of Medicine, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada
| | - Marc Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA USA
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Patrick B Ryan
- Janssen Research and Development, Titusville, NJ USA
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, OX3 7LD, UK
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Abstract
OBJECTIVE To conduct a retrospective analysis of sequential cross-sectional data of opioid prescribing practices in patients with no prior history of opioid use. METHODS Individuals filling an oral opioid prescription who had 1 year of prior observation were identified from four different administrative claims databases for the period between January 1, 2002, and December 31, 2018: IBM MarketScan® Commercial Database (CCAE), Multi-State Medicaid Database (MDCD), Medicare Supplemental Database (MDCR), and Optum© De-Identified Clinformatics® Data Mart Database. Outcomes included incidence of new opioid use and characteristics of patients' first opioid prescription, including dispensed morphine milligram equivalent (MME) per day, total MME dispensed, total MME ≥300, and days' supply of prescription for ≤3 or ≥30 days. RESULTS There were 40,600,696 new opioid users identified. The incidence of new opioid use in the past 17 years ranged from 6% to 11% within the two commercially insured databases. Incidence decreased over time in MDCD and was consistently higher in MDCR. Total MME dispensed decreased in MDCD and increased in CCAE, with no major changes in the other databases. The proportion of patients receiving ≥30-day prescriptions decreased and the proportion of patients receiving ≤3-day prescriptions increased in MDCD, while ≥30-day prescriptions in the Optum database dramatically increased (low of 3.0% in 2003 to peak of 16.9% in 2017). CONCLUSIONS Opioid prescribing practices varied across different populations of insured individuals during the past 17 years. The most substantial changes in opioid prescriptions over time have occurred in MDCD, with reductions in use across multiple metrics.
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Affiliation(s)
- David M Kern
- Janssen Research & Development, Titusville, New Jersey, USA
| | | | - Anthony G Sena
- Janssen Research & Development, Titusville, New Jersey, USA
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22
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Duarte-Salles T, Vizcaya D, Pistillo A, Casajust P, Sena AG, Lai LYH, Prats-Uribe A, Ahmed WUR, Alshammari TM, Alghoul H, Alser O, Burn E, You SC, Areia C, Blacketer C, DuVall S, Falconer T, Fernandez-Bertolin S, Fortin S, Golozar A, Gong M, Tan EH, Huser V, Iveli P, Morales DR, Nyberg F, Posada JD, Recalde M, Roel E, Schilling LM, Shah NH, Shah K, Suchard MA, Zhang L, Zhang Y, Williams AE, Reich CG, Hripcsak G, Rijnbeek P, Ryan P, Kostka K, Prieto-Alhambra D. Baseline characteristics, management, and outcomes of 55,270 children and adolescents diagnosed with COVID-19 and 1,952,693 with influenza in France, Germany, Spain, South Korea and the United States: an international network cohort study. medRxiv 2020:2020.10.29.20222083. [PMID: 33140074 PMCID: PMC7605587 DOI: 10.1101/2020.10.29.20222083] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [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: 12/25/2022]
Abstract
Objectives To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children/adolescents diagnosed or hospitalized with COVID-19. Secondly, to describe health outcomes amongst children/adolescents diagnosed with previous seasonal influenza. Design International network cohort. Setting Real-world data from European primary care records (France/Germany/Spain), South Korean claims and US claims and hospital databases. Participants Diagnosed and/or hospitalized children/adolescents with COVID-19 at age <18 between January and June 2020; diagnosed with influenza in 2017-2018. Main outcome measures Baseline demographics and comorbidities, symptoms, 30-day in-hospital treatments and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome (ARDS), multi-system inflammatory syndrome (MIS-C), and death. Results A total of 55,270 children/adolescents diagnosed and 3,693 hospitalized with COVID-19 and 1,952,693 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were all more common among those hospitalized vs diagnosed with COVID-19. The most common COVID-19 symptom was fever. Dyspnea, bronchiolitis, anosmia and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital treatments for COVID-19 included repurposed medications (<10%), and adjunctive therapies: systemic corticosteroids (6.8% to 37.6%), famotidine (9.0% to 28.1%), and antithrombotics such as aspirin (2.0% to 21.4%), heparin (2.2% to 18.1%), and enoxaparin (2.8% to 14.8%). Hospitalization was observed in 0.3% to 1.3% of the COVID-19 diagnosed cohort, with undetectable (N<5 per database) 30-day fatality. Thirty-day outcomes including pneumonia, ARDS, and MIS-C were more frequent in COVID-19 than influenza. Conclusions Despite negligible fatality, complications including pneumonia, ARDS and MIS-C were more frequent in children/adolescents with COVID-19 than with influenza. Dyspnea, anosmia and gastrointestinal symptoms could help differential diagnosis. A wide range of medications were used for the inpatient management of pediatric COVID-19.
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Affiliation(s)
- Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Anthony G. Sena
- Janssen Research & Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Albert Prats-Uribe
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
| | - Waheed-Ul-Rahman Ahmed
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
- College of Medicine and Health, University of Exeter, St Luke’s Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, USA
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Clair Blacketer
- Janssen Research & Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Scott DuVall
- Department of Veterans Affairs, Salt Lake City, UT, US
- University of Utah School of Medicine, Salt Lake City, UT, US
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Sergio Fernandez-Bertolin
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Asieh Golozar
- Regeneron Pharmaceutical, NY USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD USA
| | | | - Eng Hooi Tan
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
| | - Vojtech Huser
- Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Daniel R. Morales
- Division of Population Health and Genomics, University of Dundee, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Lisa M. Schilling
- Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, US
| | | | - Karishma Shah
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
| | - Marc A. Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, USA
| | - Lin Zhang
- School of Population Medicine and Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences, China
- Melbourne School of Population and Global Health, The University of Melbourne, Australia
| | - Ying Zhang
- DHC Technologies Co. Ltd. Beijing, China
| | | | | | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Patrick Ryan
- Janssen Research & Development, Titusville, NJ, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, US
| | | | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
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23
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Golozar A, Lai LYH, Sena AG, Vizcaya D, Schilling LM, Huser V, Nyberg F, Duvall SL, Morales DR, Alshammari TM, Abedtash H, Ahmed WUR, Alser O, Alghoul H, Zhang Y, Gong M, Guan Y, Areia C, Jonnagaddala J, Shah K, Lane JC, Prats-Uribe A, Posada JD, Shah NH, Subbian V, Zhang L, Abrahão MTF, Rijnbeek PR, You SC, Casajust P, Roel E, Recalde M, Fernández-Bertolín S, Andryc A, Thomas JA, Wilcox AB, Fortin S, Blacketer C, DeFalco F, Natarajan K, Falconer T, Spotnitz M, Ostropolets A, Hripcsak G, Suchard M, Lynch KE, Matheny ME, Williams A, Reich C, Duarte-Salles T, Kostka K, Ryan PB, Prieto-Alhambra D. Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States. medRxiv 2020:2020.10.25.20218875. [PMID: 33140068 PMCID: PMC7605581 DOI: 10.1101/2020.10.25.20218875] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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
Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems' response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.
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Affiliation(s)
- Asieh Golozar
- Regeneron Pharmaceutical, NY USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD USA
| | - Lana YH Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester, UK
| | - Anthony G. Sena
- Janssen R&D, Titusville NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Lisa M. Schilling
- Data Science to Patient Value Program, University of Colorado Anschutz Medical Campus
| | - Vojtech Huser
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Scott L. Duvall
- VINCI, VA Salt Lake City Health Care System, Salt Lake City, VA, & Division of Epidemiology, University of Utah, Salt Lake City, UT
| | - Daniel R. Morales
- Division of Population Health and Genomics, University of Dundee, UK
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Hamed Abedtash
- Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- College of Medicine and Health, University of Exeter, St Luke’s Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Ying Zhang
- DHC Technologies Co. Ltd, Beijing, China
| | | | - Yin Guan
- DHC Technologies Co. Ltd, Beijing, China
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Jennifer C.E. Lane
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Albert Prats-Uribe
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Jose D. Posada
- Stanford University School of Medicine, Stanford, California, USA
| | - Nigam H. Shah
- Stanford University School of Medicine, Stanford, California, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, Arizona, USA
| | - Lin Zhang
- School of Population Medicine and Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | | | - Peter R. Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | - Elena Roel
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Universitat Autònoma de Barcelona, Spain
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Jason A. Thomas
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Adam B. Wilcox
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
- UW Medicine, Seattle, WA, USA
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen Research and Development, Raritan, NJ, USA
| | - Clair Blacketer
- Janssen R&D, Titusville NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
- New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
- New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | - Marc Suchard
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, USA
| | - Kristine E. Lynch
- VINCI, VA Salt Lake City Health Care System, Salt Lake City, VA, & Division of Epidemiology, University of Utah, Salt Lake City, UT
| | - Michael E. Matheny
- VINCI, Tennessee Valley Healthcare System VA, Nashville, TN & Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | - Andrew Williams
- Tufts Institute for Clinical Research and Health Policy Studies, US
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Patrick B. Ryan
- Janssen R&D, Titusville NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
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Burn E, You SC, Sena AG, Kostka K, Abedtash H, Abrahão MTF, Alberga A, Alghoul H, Alser O, Alshammari TM, Aragon M, Areia C, Banda JM, Cho J, Culhane AC, Davydov A, DeFalco FJ, Duarte-Salles T, DuVall S, Falconer T, Fernandez-Bertolin S, Gao W, Golozar A, Hardin J, Hripcsak G, Huser V, Jeon H, Jing Y, Jung CY, Kaas-Hansen BS, Kaduk D, Kent S, Kim Y, Kolovos S, Lane JCE, Lee H, Lynch KE, Makadia R, Matheny ME, Mehta PP, Morales DR, Natarajan K, Nyberg F, Ostropolets A, Park RW, Park J, Posada JD, Prats-Uribe A, Rao G, Reich C, Rho Y, Rijnbeek P, Schilling LM, Schuemie M, Shah NH, Shoaibi A, Song S, Spotnitz M, Suchard MA, Swerdel JN, Vizcaya D, Volpe S, Wen H, Williams AE, Yimer BB, Zhang L, Zhuk O, Prieto-Alhambra D, Ryan P. Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study. Nat Commun 2020; 11:5009. [PMID: 33024121 PMCID: PMC7538555 DOI: 10.1038/s41467-020-18849-z] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [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: 07/01/2020] [Accepted: 09/10/2020] [Indexed: 01/08/2023] Open
Abstract
Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19.
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Affiliation(s)
- Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Anthony G Sena
- Janssen Research and Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | | | | | - Amanda Alberga
- Observational Health Data Sciences and Informatics Network, Alberta, Canada
| | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Maria Aragon
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Jaehyeong Cho
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Aedin C Culhane
- Data Science, Dana-Farber Cancer Institute. Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Alexander Davydov
- Odysseus Data Services, Inc., Cambridge, MA, USA
- Department for Microbiology, Virology and Immunology, Belarusian State Medical University, Minsk, Belarus
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Scott DuVall
- Department of Veterans Affairs, Salt Lake City, UT, USA
- University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Sergio Fernandez-Bertolin
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Weihua Gao
- Health Economics and Outcomes Research, AbbVie, North Chicago, IL, USA
| | - Asieh Golozar
- Pharmacoepidemiology, Regeneron, NY, USA
- Department of Epidemiology, Johns Hopkins School of Public, Baltimore, MD, USA
| | - Jill Hardin
- Janssen Research and Development, Titusville, NJ, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Vojtech Huser
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Hokyun Jeon
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Yonghua Jing
- Health Economics and Outcomes Research, AbbVie, North Chicago, IL, USA
| | - Chi Young Jung
- Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, Korea
| | - Benjamin Skov Kaas-Hansen
- Clinical Pharmacology Unit, Zealand University Hospital, Køge, Denmark
- NNF Centre for Protein Research, University of Copenhagen, København, Denmark
| | - Denys Kaduk
- Odysseus Data Services, Inc., Cambridge, MA, USA
- Department of Pediatrics № 2, V. N. Karazin Kharkiv National University, Kharkiv, Ukraine
| | - Seamus Kent
- Science Policy and Research, National Institute for Health and Care Excellence, London, UK
| | - Yeesuk Kim
- Department of Orthopaedic Surgery, College of Medicine, Hanyang University, Seoul, Korea
| | - Spyros Kolovos
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK
| | - Jennifer C E Lane
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK
| | - Hyejin Lee
- Bigdata Department, Health Insurance Review & Assessment Service, Wonju, Korea
| | - Kristine E Lynch
- Department of Veterans Affairs, Salt Lake City, UT, USA
- University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Rupa Makadia
- Janssen Research and Development, Titusville, NJ, USA
| | - Michael E Matheny
- GRECC, Tennessee Valley Healthcare System VA, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paras P Mehta
- College of Medicine-Tucson, University of Arizona, Tucson, AZ, USA
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, Dundee, UK
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
- New York-Presbyterian Hospital, New York, NY, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Jose D Posada
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK
| | - Gowtham Rao
- Janssen Research and Development, Titusville, NJ, USA
| | | | - Yeunsook Rho
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Lisa M Schilling
- Data Science to Patient Value Program, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Martijn Schuemie
- Janssen Research and Development, Titusville, NJ, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | - Nigam H Shah
- Department of Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Azza Shoaibi
- Janssen Research and Development, Titusville, NJ, USA
| | - Seokyoung Song
- Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Gyeongsan, Korea
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Marc A Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | | | | | - Salvatore Volpe
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Haini Wen
- Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA
| | - Belay B Yimer
- Centre for Epidemiology Versus Arthritis, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - Lin Zhang
- School of Public Health, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Oleg Zhuk
- Odysseus Data Services, Inc., Cambridge, MA, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, Oxford, UK.
| | - Patrick Ryan
- Janssen Research and Development, Titusville, NJ, USA
- Columbia University, New York, NY, USA
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25
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Lane JCE, Weaver J, Kostka K, Duarte-Salles T, Abrahao MTF, Alghoul H, Alser O, Alshammari TM, Biedermann P, Banda JM, Burn E, Casajust P, Conover MM, Culhane AC, Davydov A, DuVall SL, Dymshyts D, Fernandez-Bertolin S, Fišter K, Hardin J, Hester L, Hripcsak G, Kaas-Hansen BS, Kent S, Khosla S, Kolovos S, Lambert CG, van der Lei J, Lynch KE, Makadia R, Margulis AV, Matheny ME, Mehta P, Morales DR, Morgan-Stewart H, Mosseveld M, Newby D, Nyberg F, Ostropolets A, Park RW, Prats-Uribe A, Rao GA, Reich C, Reps J, Rijnbeek P, Sathappan SMK, Schuemie M, Seager S, Sena AG, Shoaibi A, Spotnitz M, Suchard MA, Torre CO, Vizcaya D, Wen H, de Wilde M, Xie J, You SC, Zhang L, Zhuk O, Ryan P, Prieto-Alhambra D. Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study. Lancet Rheumatol 2020; 2:e698-e711. [PMID: 32864627 PMCID: PMC7442425 DOI: 10.1016/s2665-9913(20)30276-9] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis. Methods In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the I 2 value was less than 0·4. Findings The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]). Interpretation Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment. Funding National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.
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Affiliation(s)
- Jennifer C E Lane
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - James Weaver
- Janssen Research and Development, Titusville, NJ, USA
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | | | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Edward Burn
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK.,Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Paula Casajust
- Real-World Evidence, Trial Form Support, Barcelona, Spain
| | | | - Aedin C Culhane
- Department of Data Sciences, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA, USA
| | - Alexander Davydov
- Medical Ontology Solutions, Odysseus Data Services, Cambridge MA, USA
| | - Scott L DuVall
- Western Institute for Biomedical Research, Department of Veterans Affairs, Salt Lake City, UT, USA.,Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Dmitry Dymshyts
- Medical Ontology Solutions, Odysseus Data Services, Cambridge MA, USA
| | - Sergio Fernandez-Bertolin
- Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Kristina Fišter
- School of Medicine, Andrija Štampar School of Public Health, University of Zagreb, Zagreb, Croatia
| | - Jill Hardin
- Janssen Research and Development, Titusville, NJ, USA
| | - Laura Hester
- Janssen Research and Development, Titusville, NJ, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.,New York-Presbyterian Hospital, New York, NY, USA
| | - Benjamin Skov Kaas-Hansen
- Clinical Pharmacology Unit, Zealand University Hospital, Roskilde, Denmark.,NNF Centre for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Seamus Kent
- National Institute for Health and Care Excellence, London, UK
| | - Sajan Khosla
- Real World Science and Digital, AstraZeneca, Cambridge, UK
| | - Spyros Kolovos
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Christophe G Lambert
- Department of Internal Medicine, Center for Global Health and Division of Translational Informatics, Albuquerque, NM, USA
| | - Johan van der Lei
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Kristine E Lynch
- Western Institute for Biomedical Research, Department of Veterans Affairs, Salt Lake City, UT, USA.,Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Rupa Makadia
- Janssen Research and Development, Titusville, NJ, USA
| | | | - Michael E Matheny
- Geriatrics Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paras Mehta
- College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, UK
| | | | - Mees Mosseveld
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon-si Gyeonggi-do, South Korea
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Gowtham A Rao
- Janssen Research and Development, Titusville, NJ, USA
| | | | - Jenna Reps
- Janssen Research and Development, Titusville, NJ, USA
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | | | | | | | - Anthony G Sena
- Janssen Research and Development, Titusville, NJ, USA.,Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Azza Shoaibi
- Janssen Research and Development, Titusville, NJ, USA
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Marc A Suchard
- Department of Biomathematics and Department of Human Genetics, David Geffen School of Medicine at UCLA, and Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA
| | | | | | - Haini Wen
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Marcel de Wilde
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Junqing Xie
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon-si Gyeonggi-do, South Korea
| | - Lin Zhang
- School of Population Medicine and Public Health, Peking Union Medical College/Chinese Academy of Medical Sciences, Beijing, China.,Melbourne School of Population and Global Health, University of Melbourne, VIC, Australia
| | - Oleg Zhuk
- Medical Ontology Solutions, Odysseus Data Services, Cambridge MA, USA
| | - Patrick Ryan
- Janssen Research and Development, Titusville, NJ, USA.,Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK.,Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
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26
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Morales DR, Conover MM, You SC, Pratt N, Kostka K, Duarte-Salles T, Fernández-Bertolín S, Aragón M, DuVall SL, Lynch K, Falconer T, van Bochove K, Sung C, Matheny ME, Lambert CG, Nyberg F, Alshammari TM, Williams AE, Park RW, Weaver J, Sena AG, Schuemie MJ, Rijnbeek PR, Williams RD, Lane JCE, Prats-Uribe A, Zhang L, Areia C, Krumholz HM, Prieto-Alhambra D, Ryan PB, Hripcsak G, Suchard MA. Renin-angiotensin system blockers and susceptibility to COVID-19: a multinational open science cohort study. medRxiv 2020:2020.06.11.20125849. [PMID: 32587982 PMCID: PMC7310640 DOI: 10.1101/2020.06.11.20125849] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [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: 12/28/2022]
Abstract
INTRODUCTION Angiotensin converting enzyme inhibitors (ACEs) and angiotensin receptor blockers (ARBs) could influence infection risk of coronavirus disease (COVID-19). Observational studies to date lack pre-specification, transparency, rigorous ascertainment adjustment and international generalizability, with contradictory results. METHODS Using electronic health records from Spain (SIDIAP) and the United States (Columbia University Irving Medical Center and Department of Veterans Affairs), we conducted a systematic cohort study with prevalent ACE, ARB, calcium channel blocker (CCB) and thiazide diuretic (THZ) use to determine relative risk of COVID-19 diagnosis and related hospitalization outcomes. The study addressed confounding through large-scale propensity score adjustment and negative control experiments. RESULTS Following over 1.1 million antihypertensive users identified between November 2019 and January 2020, we observed no significant difference in relative COVID-19 diagnosis risk comparing ACE/ARB vs CCB/THZ monotherapy (hazard ratio: 0.98; 95% CI 0.84 - 1.14), nor any difference for mono/combination use (1.01; 0.90 - 1.15). ACE alone and ARB alone similarly showed no relative risk difference when compared to CCB/THZ monotherapy or mono/combination use. Directly comparing ACE vs. ARB demonstrated a moderately lower risk with ACE, non-significant for monotherapy (0.85; 0.69 - 1.05) and marginally significant for mono/combination users (0.88; 0.79 - 0.99). We observed, however, no significant difference between drug- classes for COVID-19 hospitalization or pneumonia risk across all comparisons. CONCLUSION There is no clinically significant increased risk of COVID-19 diagnosis or hospitalization with ACE or ARB use. Users should not discontinue or change their treatment to avoid COVID-19.
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Affiliation(s)
- Daniel R Morales
- Division of Population Health and Genomics, University of Dundee, UK
| | - Mitchell M Conover
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Nicole Pratt
- Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Maria Aragón
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Scott L DuVall
- Department of Veterans Affairs, Salt Lake City, UT, USA
- University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Kristine Lynch
- Department of Veterans Affairs, Salt Lake City, UT, USA
- University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University, New York, USA
| | | | - Cynthia Sung
- Translational Discovery, Bill & Melinda Gates Medical Research Institute, Seattle, WA, USA
| | - Michael E Matheny
- Geriatric Research Education, and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christophe G Lambert
- Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | | | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - James Weaver
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Anthony G Sena
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Martijn J Schuemie
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
| | - Peter R Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Ross D Williams
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Jennifer C E Lane
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, UK
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, UK
| | - Lin Zhang
- School of Public Health, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
- Melbourne School of Public Health, The University of Melbourne, Victoria, Australia
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Harlan M Krumholz
- Section of Cardiovascular Medicine, Department of Medicine, Yale University, New Haven, CT, USA
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford University, Oxford, UK
| | - Patrick B Ryan
- Observational Health Data Analytics, Janssen Research and Development, Titusville, NJ, USA
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, USA
| | - Marc A Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, USA
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Wilcox MA, Villasis-Keever A, Sena AG, Knoll C, Fife D. Evaluation of disability in patients exposed to fluoroquinolones. BMC Pharmacol Toxicol 2020; 21:40. [PMID: 32493505 PMCID: PMC7268406 DOI: 10.1186/s40360-020-00415-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 04/05/2019] [Accepted: 05/19/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Fluoroquinolones are used for conditions including sinusitis, bronchitis, and urinary tract infections. It has been suggested that exposure to fluoroquinolones for these conditions is associated with disability resulting from adverse events in 2 or more organ systems. The objectives were to: describe: 1) fluoroquinolone, azithromycin, and sulfamethoxazole / trimethoprim utilization for these infections; 2) the rate of disability associated with exposure to each of these antibiotic classes and adverse events in 2 or more system organ classes, and 3) compare outcome rates for each of the antibiotic classes. METHODS This study was conducted using administrative data to mitigate the limitations of spontaneous reports. The sampling frame was a U.S. population with both medical and disability insurance, including patients with the above uncomplicated infections who were prescribed the antibiotics of interest. The primary outcome was an incident short-term disability claim associated with adverse events in 2 different organ systems within 120 days of exposure. A matched analysis was used to compare the outcome for patients receiving each of the drug classes. RESULTS After propensity score matching, there were 119,653 individuals in each of the exposure groups. There were 264 fluoroquinolone associated disability events and 243 azithromycin/ sulfamethoxazole associated disability events (relative risk =1.09 (95% CI: 0.92-1.30; calibrated p = 0.84)). The results were not significantly different from the null hypothesis of no difference between groups. CONCLUSION Comparative assessments are difficult to conduct in spontaneous reports. This examination of disability associated with adverse events in different system organ classes showed no difference between fluoroquinolones and azithromycin or sulfamethoxazole in administrative data.
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Affiliation(s)
- Marsha A Wilcox
- Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA.
| | | | - Anthony G Sena
- Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA
| | - Christopher Knoll
- Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA
| | - Daniel Fife
- Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA
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Burn E, Weaver J, Morales D, Prats-Uribe A, Delmestri A, Strauss VY, He Y, Robinson DE, Pinedo-Villanueva R, Kolovos S, Duarte-Salles T, Sproviero W, Yu D, Van Speybroeck M, Williams R, John LH, Hughes N, Sena AG, Costello R, Birlie B, Culliford D, O'Leary C, Morgan H, Burkard T, Prieto-Alhambra D, Ryan P. Opioid use, postoperative complications, and implant survival after unicompartmental versus total knee replacement: a population-based network study. Lancet Rheumatol 2019; 1:e229-e236. [PMID: 38229379 DOI: 10.1016/s2665-9913(19)30075-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 10/01/2019] [Accepted: 10/04/2019] [Indexed: 02/08/2023]
Abstract
BACKGROUND There is uncertainty around whether to use unicompartmental knee replacement (UKR) or total knee replacement (TKR) for individuals with osteoarthritis confined to a single compartment of the knee. We aimed to emulate the design of the Total or Partial Knee Arthroplasty Trial (TOPKAT) using routinely collected data to assess whether the efficacy results reported in the trial translate into effectiveness in routine practice, and to assess comparative safety. METHODS We did a population-based network study using data from four US and one UK health-care database, part of the Observational Health Data Sciences and Informatics network. The inclusion criteria were the same as those for TOPKAT; briefly, we identified patients aged at least 40 years with osteoarthritis who had undergone UKR or TKR and who had available data for at least one year prior to surgery. Patients were excluded if they had evidence of previous knee arthroplasty, knee fracture, knee surgery (except diagnostic), rheumatoid arthritis, infammatory arthropathies, or septic arthritis. Opioid use from 91-365 days after surgery, as a proxy for persistent pain, was assessed for all participants in all databases. Postoperative complications (ie, venous thromboembolism, infection, readmission, and mortality) were assessed over the 60 days after surgery and implant survival (as measured by revision procedures) was assessed over the 5 years after surgery. Outcomes were assessed in all databases, except for readmission, which was assessed in three of the databases, and mortality, which was assessed in two of the databases. Propensity score matched Cox proportional hazards models were fitted for each outcome. Calibrated hazard ratios (cHRs) were generated for each database to account for observed differences in control outcomes, and cHRs were then combined using meta-analysis. FINDINGS 33 867 individuals who received UKR and 557 831 individuals who received TKR between Jan 1, 2005, and April 30, 2018, were eligible for matching. 32 379 with UKR and 250 377 with TKR were propensity score matched and informed the analyses. UKR was associated with a reduced risk of postoperative opioid use (cHR from meta-analysis 0·81, 95% CI 0·73-0·90) and a reduced risk of venous thromboembolism (0·62, 0·36-0·95), whereas no difference was seen for infection (0·85, 0·51-1·37) and readmission (0·79, 0·47-1·25). Evidence was insufficient to conclude whether there was a reduction in risk of mortality. UKR was also associated with an increased risk of revision (1·64, 1·40-1·94). INTERPRETATION UKR was associated with a reduced risk of postoperative opioid use compared with TKR, which might indicate a reduced risk of persistent pain after surgery. UKR was associated with a lower risk of venous thromboembolism but an increased risk of revision compared with TKR. These findings can help to inform shared decision making for individuals eligible for knee replacement surgery. FUNDING EU/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative (2) Joint Undertaking (EHDEN).
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Affiliation(s)
- Edward Burn
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - James Weaver
- Janssen Research and Development, Titusville, NJ, USA
| | | | - Albert Prats-Uribe
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Antonella Delmestri
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Victoria Y Strauss
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Ying He
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Danielle E Robinson
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Rafael Pinedo-Villanueva
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Spyros Kolovos
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Barcelona, Spain
| | | | | | | | | | | | | | | | - Ruth Costello
- Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK
| | - Belay Birlie
- Arthritis Research UK Centre for Epidemiology, University of Manchester, Manchester, UK
| | - David Culliford
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; NIHR CLAHRC Wessex, University of Southampton, Southampton, UK
| | | | | | - Theresa Burkard
- University of Basel, Basel, Switzerland; University Hospital Basel, Basel, Switzerland
| | - Daniel Prieto-Alhambra
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; GREMPAL Research Group, Idiap Jordi Gol and CIBERFes, Universitat Autonoma de Barcelona and Instituto de Salud Carlos III, Barcelona, Spain.
| | - Patrick Ryan
- Janssen Research and Development, Titusville, NJ, USA; Columbia University, New York, NY, USA
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Fife D, Cepeda MS, Baseman A, Richards H, Hu P, Starr HL, Sena AG. Medication changes after switching from CONCERTA® brand methylphenidate HCl to a generic long-acting formulation: A retrospective database study. PLoS One 2018; 13:e0193453. [PMID: 29489906 PMCID: PMC5831385 DOI: 10.1371/journal.pone.0193453] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 01/28/2018] [Indexed: 12/04/2022] Open
Abstract
Background Observational studies of switching from branded to generic formulations of the same drug substance often lack appropriate comparators for the subjects who switched. Three generic formulations were deemed equivalent to Concerta: an authorized generic (AG) identical except for external packaging, and two other generics (EG). Objective Compare the incidence of a combined endpoint (switching back to Concerta, changing the use of immediate release methylphenidate (MPH), stopping all long-acting methylphenidate, or starting a new medication) among people switched from Concerta to the AG versus the EG. Methods Cohort study from the Truven CCAE database of people aged 6 to 65 diagnosed with ADHD, treated with Concerta, and switched to the EG or to the AG formulation. Results In the EG arm 24.6% and in the AG arm 19.7% of subjects switched back to Concerta. The proportion of subjects meeting the combined endpoint was 39.5% in the EG arm, 32.9% in the AG arm, a crude risk ratio of 1.20 (95% CI 0.94, 1.54). After adjustment by propensity score stratification, the adjusted odds ratio (OR) was 1.23 (95% CI 0.90, 1.70). In an unplanned analysis using a different method of adjustment, the adjusted OR was 1.00 (95% CI 0.69, 1.44). Discussion This study did not detect a difference between the proportion of people who met the study endpoint in the two study arms, i.e. between those who switched to a generic formulation that was identical to Concerta except for external packaging and those who switched to the comparison generics. The high incidence of the combined endpoint in the AG arm demonstrates the need for an appropriate comparator in studies of this type. Trial registration ClinicalTrials.gov NCT02730572
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Affiliation(s)
- Daniel Fife
- Epidemiology, Janssen Research & Development, LLC, Titusville, NJ, United States of America
- * E-mail:
| | - M. Soledad Cepeda
- Epidemiology, Janssen Research & Development, LLC, Titusville, NJ, United States of America
| | - Alan Baseman
- Global Medical Safety, Janssen Research & Development, LLC, Horsham, PA, United States of America
| | - Henry Richards
- Established Products, Janssen Research & Development, LLC, Titusville, NJ, United States of America
| | - Peter Hu
- Clinical Biostatistics, Janssen Research & Development, LLC, Raritan, NJ, United States of America
| | - H. Lynn Starr
- Janssen Scientific Affairs, LLC, Titusville, NJ, United States of America
| | - Anthony G. Sena
- Epidemiology, Janssen Research & Development, LLC, Titusville, NJ, United States of America
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