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Jaberi-Douraki M, Meyer E, Riviere J, Gedara NIM, Kawakami J, Wyckoff GJ, Xu X. Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity. Sci Rep 2021; 11:13349. [PMID: 34172790 PMCID: PMC8233397 DOI: 10.1038/s41598-021-92734-7] [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] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 06/15/2021] [Indexed: 12/15/2022] Open
Abstract
Hypertension is a recognized comorbidity for COVID-19. The association of antihypertensive medications with outcomes in patients with hypertension is not fully described. However, angiotensin-converting enzyme 2 (ACE2), responsible for host entry of the novel coronavirus (SARS-CoV-2) leading to COVID-19, is postulated to be upregulated in patients taking angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). Here, we evaluated the occurrence of pulmonary adverse drug events (ADEs) in patients with hypertension receiving ACEIs/ARBs to determine if disparities exist between individual drugs within the respective classes using data from the FDA Spontaneous Reporting Systems. For this purpose, we proposed the proportional reporting ratio to provide a statistical summary for the commonality of an ADE for a specific drug as compared to the entire database for drugs in the same or other classes. In addition, a statistical procedure, multiple logistic regression analysis, was employed to correct hidden confounders when causative covariates are underreported or untrusted to correct analyses of drug-ADE combinations. To date, analyses have been focused on drug classes rather than individual drugs which may have different ADE profiles depending on the underlying diseases present. A retrospective analysis of thirteen pulmonary ADEs showed significant differences associated with quinapril and trandolapril, compared to other ACEIs and ARBs. Specifically, quinapril and trandolapril were found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs as well as ARBs (P < 0.0001) for group comparison (i.e., ACEIs vs. ARBs vs. quinapril vs. trandolapril) and (P ≤ 0.0007) for pairwise comparison (i.e., ACEIs vs. quinapril, ACEIs vs. trandolapril, ARBs vs. quinapril, or ARBs vs. trandolapril). This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.
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Affiliation(s)
- Majid Jaberi-Douraki
- 1DATA Consortium, Manhattan, USA.
- Kansas State University Olathe, Olathe, KS, 66061-1304, USA.
- Department of Mathematics, Kansas State University, Manhattan, USA.
| | - Emma Meyer
- 1DATA Consortium, Manhattan, USA
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, USA
| | - Jim Riviere
- 1DATA Consortium, Manhattan, USA
- Kansas State University, Manhattan, USA
- North Carolina State University, Raleigh, USA
| | - Nuwan Indika Millagaha Gedara
- 1DATA Consortium, Manhattan, USA
- Kansas State University Olathe, Olathe, KS, 66061-1304, USA
- Department of Business Economics, University of Colombo, Colombo, Sri Lanka
| | - Jessica Kawakami
- 1DATA Consortium, Manhattan, USA
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, USA
- Molecular Biology and Biochemistry, School of Biological and Chemical Sciences, University of Missouri-Kansas City, Kansas City, USA
| | - Gerald J Wyckoff
- 1DATA Consortium, Manhattan, USA
- Division of Pharmacology and Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, USA
- Molecular Biology and Biochemistry, School of Biological and Chemical Sciences, University of Missouri-Kansas City, Kansas City, USA
| | - Xuan Xu
- 1DATA Consortium, Manhattan, USA
- Kansas State University Olathe, Olathe, KS, 66061-1304, USA
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Rando HM, Bennett TD, Byrd JB, Bramante C, Callahan TJ, Chute CG, Davis HE, Deer R, Gagnier J, Koraishy FM, Liu F, McMurry JA, Moffitt RA, Pfaff ER, Reese JT, Relevo R, Robinson PN, Saltz JH, Solomonides A, Sule A, Topaloglu U, Haendel MA. Challenges in defining Long COVID: Striking differences across literature, Electronic Health Records, and patient-reported information. medRxiv 2021:2021.03.20.21253896. [PMID: 33791733 PMCID: PMC8010765 DOI: 10.1101/2021.03.20.21253896] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [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: 12/11/2022]
Abstract
Since late 2019, the novel coronavirus SARS-CoV-2 has introduced a wide array of health challenges globally. In addition to a complex acute presentation that can affect multiple organ systems, increasing evidence points to long-term sequelae being common and impactful. The worldwide scientific community is forging ahead to characterize a wide range of outcomes associated with SARS-CoV-2 infection; however the underlying assumptions in these studies have varied so widely that the resulting data are difficult to compareFormal definitions are needed in order to design robust and consistent studies of Long COVID that consistently capture variation in long-term outcomes. Even the condition itself goes by three terms, most widely "Long COVID", but also "COVID-19 syndrome (PACS)" or, "post-acute sequelae of SARS-CoV-2 infection (PASC)". In the present study, we investigate the definitions used in the literature published to date and compare them against data available from electronic health records and patient-reported information collected via surveys. Long COVID holds the potential to produce a second public health crisis on the heels of the pandemic itself. Proactive efforts to identify the characteristics of this heterogeneous condition are imperative for a rigorous scientific effort to investigate and mitigate this threat.
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Affiliation(s)
- Halie M. Rando
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Tellen D. Bennett
- Center for Health AI and Section of Informatics and Data Science, Department of Pediatrics, University of Colorado School of Medicine, University of Colorado, Aurora, CO, USA
| | | | | | - Tiffany J. Callahan
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Computational Bioscience, University of Colorado Anschutz Medical Campus, Boulder, CO, USA
| | - Christopher G. Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | | | - Rachel Deer
- The University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Joel Gagnier
- Computational Bioscience, University of Colorado Anschutz Medical Campus, Boulder, CO, USA
| | | | - Feifan Liu
- University of Massachusetts Medical School Worcester, Worcester, MA, USA
| | - Julie A. McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Richard A. Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Emily R. Pfaff
- Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Justin T. Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Rose Relevo
- Oregon Health & Science University, Portland, OR, USA
| | - Peter N. Robinson
- The Jackson Laboratory For Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | | | - Anupam Sule
- Saint Joseph Mercy Health System, Ypsilanti, MI, USA
| | - Umit Topaloglu
- School of Medicine, Wake Forest University, Winston Salem, NC, USA
| | - Melissa A. Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Rando HM, MacLean AL, Lee AJ, Lordan R, Ray S, Bansal V, Skelly AN, Sell E, Dziak JJ, Shinholster L, McGowan LD, Guebila MB, Wellhausen N, Knyazev S, Boca SM, Capone S, Qi Y, Park Y, Sun Y, Mai D, Boerckel JD, Brueffer C, Byrd JB, Kamil JP, Wang J, Velazquez R, Szeto GL, Barton JP, Goel RR, Mangul S, Lubiana T, Gitter A, Greene CS. Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure. ArXiv 2021:arXiv:2102.01521v4. [PMID: 33594340 PMCID: PMC7885912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 12/03/2021] [Indexed: 12/02/2022]
Abstract
The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease.
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Affiliation(s)
- Halie M Rando
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America; Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552); the National Human Genome Research Institute (R01 HG010067)
| | - Adam L MacLean
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California, United States of America
| | - Alexandra J Lee
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552)
| | - Ronan Lordan
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104-5158, USA
| | - Sandipan Ray
- Department of Biotechnology, Indian Institute of Technology Hyderabad, Kandi, Sangareddy 502285, Telangana, India
| | - Vikas Bansal
- Biomedical Data Science and Machine Learning Group, German Center for Neurodegenerative Diseases, Tübingen 72076, Germany
| | - Ashwin N Skelly
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Institute for Immunology, University of Pennsylvania Perelman School of Medicine, Philadelphia, United States of America · Funded by NIH Medical Scientist Training Program T32 GM07170
| | - Elizabeth Sell
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - John J Dziak
- Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University, University Park, PA, United States of America
| | - Lamonica Shinholster
- Mercer University, Macon, GA, United States of America · Funded by the Center for Global Genomics and Health Equity at the University of Pennsylvania
| | - Lucy D'Agostino McGowan
- Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, North Carolina, United States of America
| | - Marouen Ben Guebila
- Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Nils Wellhausen
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Sergey Knyazev
- Georgia State University, Atlanta, GA, United States of America
| | - Simina M Boca
- Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, District of Columbia, United States of America
| | - Stephen Capone
- St. George's University School of Medicine, St. George's, Grenada
| | - Yanjun Qi
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States of America
| | - YoSon Park
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America · Funded by NHGRI R01 HG10067
| | - Yuchen Sun
- Department of Computer Science, University of Virginia, Charlottesville, VA, United States of America
| | - David Mai
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Joel D Boerckel
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | - James Brian Byrd
- University of Michigan School of Medicine, Ann Arbor, Michigan, United States of America · Funded by NIH K23HL128909; FastGrants
| | - Jeremy P Kamil
- Department of Microbiology and Immunology, Louisiana State University Health Sciences Center Shreveport, Shreveport, Louisiana, USA
| | - Jinhui Wang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | | | - Gregory L Szeto
- Allen Institute for Immunology, Seattle, WA, United States of America
| | - John P Barton
- Department of Physics and Astronomy, University of California-Riverside, Riverside, California, United States of America
| | - Rishi Raj Goel
- Institute for Immunology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Serghei Mangul
- Department of Clinical Pharmacy, School of Pharmacy, University of Southern California, Los Angeles, CA, United States of America
| | - Tiago Lubiana
- Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America; Morgridge Institute for Research, Madison, Wisconsin, United States of America · Funded by John W. and Jeanne M. Rowe Center for Research in Virology
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America; Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, Philadelphia, Pennsylvania, United States of America; Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, Colorado, United States of America; Center for Health AI, University of Colorado School of Medicine, Aurora, Colorado, United States of America · Funded by the Gordon and Betty Moore Foundation (GBMF 4552); the National Human Genome Research Institute (R01 HG010067)
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Cromer SJ, Lakhani CM, Wexler DJ, Burnett-Bowie SAM, Udler M, Patel CJ. Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes. medRxiv 2020:2020.09.30.20201830. [PMID: 33024982 PMCID: PMC7536884 DOI: 10.1101/2020.09.30.20201830] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [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: 12/22/2022]
Abstract
Background The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. Methods and Findings All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2.Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. Conclusions This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.
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Affiliation(s)
- Sara J. Cromer
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114
- Harvard Medical School, Boston, MA 02115
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Chirag M. Lakhani
- Harvard Medical School, Boston, MA 02115
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142
| | - Deborah J Wexler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114
- Harvard Medical School, Boston, MA 02115
| | | | - Miriam Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114
- Harvard Medical School, Boston, MA 02115
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA 02142
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Oliver N, Barber X, Roomp K, Roomp K. Assessing the Impact of the COVID-19 Pandemic in Spain: Large-Scale, Online, Self-Reported Population Survey. J Med Internet Res 2020; 22:e21319. [PMID: 32870159 PMCID: PMC7485997 DOI: 10.2196/21319] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/31/2020] [Accepted: 09/01/2020] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Spain has been one of the countries most impacted by the COVID-19 pandemic. Since the first confirmed case was reported on January 31, 2020, there have been over 405,000 cases and 28,000 deaths in Spain. The economic and social impact is without precedent. Thus, it is important to quickly assess the situation and perception of the population. Large-scale online surveys have been shown to be an effective tool for this purpose. OBJECTIVE We aim to assess the situation and perception of the Spanish population in four key areas related to the COVID-19 pandemic: social contact behavior during confinement, personal economic impact, labor situation, and health status. METHODS We obtained a large sample using an online survey with 24 questions related to COVID-19 in the week of March 28-April 2, 2020, during the peak of the first wave of COVID-19 in Spain. The self-selection online survey method of nonprobability sampling was used to recruit 156,614 participants via social media posts that targeted the general adult population (age >18 years). Given such a large sample, the 95% CI was ±0.843 for all reported proportions. RESULTS Regarding social behavior during confinement, participants mainly left their homes to satisfy basic needs. We found several statistically significant differences in social behavior across genders and age groups. The population's willingness to comply with the confinement measures is evident. From the survey answers, we identified a significant adverse economic impact of the pandemic on those working in small businesses and a negative correlation between economic damage and willingness to stay in confinement. The survey revealed that close contacts play an important role in the transmission of the disease, and 28% of the participants lacked the necessary resources to properly isolate themselves. We also identified a significant lack of testing, with only 1% of the population tested and 6% of respondents unable to be tested despite their doctor's recommendation. We developed a generalized linear model to identify the variables that were correlated with a positive SARS-CoV-2 test result. Using this model, we estimated an average of 5% for SARS-CoV-2 prevalence in the Spanish population during the time of the study. A seroprevalence study carried out later by the Spanish Ministry of Health reported a similar level of disease prevalence (5%). CONCLUSIONS Large-scale online population surveys, distributed via social media and online messaging platforms, can be an effective, cheap, and fast tool to assess the impact and prevalence of an infectious disease in the context of a pandemic, particularly when there is a scarcity of official data and limited testing capacity.
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Affiliation(s)
- Nuria Oliver
- The Institute for Human(ity)-Centric Artificial Intelligence, ELLIS Unit Alicante Foundation, Alicante, Spain
| | - Xavier Barber
- Center of Operations Research, Universidad Miguel Hernández, Elche, Spain
| | - Kirsten Roomp
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
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