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Faherty EAG, Wilkins KJ, Jones S, Challa A, Qin Q, Chan LE, Olson-Chen C, Tarleton JL, Liebman MN, Mariona F, Hill EL, Patel RC. Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C). Vaccines (Basel) 2024; 12:289. [PMID: 38543923 PMCID: PMC10975285 DOI: 10.3390/vaccines12030289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/02/2024] [Accepted: 03/04/2024] [Indexed: 04/07/2024] Open
Abstract
COVID-19 vaccines have been shown to be effective in preventing severe illness, including among pregnant persons. The vaccines appear to be safe in pregnancy, supporting a continuously favorable overall risk/benefit profile, though supportive data for the U.S. over different periods of variant predominance are lacking. We sought to analyze the association of adverse pregnancy outcomes with COVID-19 vaccinations in the pre-Delta, Delta, and Omicron SARS-CoV-2 variants' dominant periods (constituting 50% or more of each pregnancy) for pregnant persons in a large, nationally sampled electronic health record repository in the U.S. Our overall analysis included 311,057 pregnant persons from December 2020 to October 2023 at a time when there were approximately 3.6 million births per year. We compared rates of preterm births and stillbirths among pregnant persons who were vaccinated before or during pregnancy to persons vaccinated after pregnancy or those who were not vaccinated. We performed a multivariable Poisson regression with generalized estimated equations to address data site heterogeneity for preterm births and unadjusted exact models for stillbirths, stratified by the dominant variant period. We found lower rates of preterm birth in the majority of modeled periods (adjusted incidence rate ratio [aIRR] range: 0.42 to 0.85; p-value range: <0.001 to 0.06) and lower rates of stillbirth (IRR range: 0.53 to 1.82; p-value range: <0.001 to 0.976) in most periods among those who were vaccinated before or during pregnancy compared to those who were vaccinated after pregnancy or not vaccinated. We largely found no adverse associations between COVID-19 vaccination and preterm birth or stillbirth; these findings reinforce the safety of COVID-19 vaccination during pregnancy and bolster confidence for pregnant persons, providers, and policymakers in the importance of COVID-19 vaccination for this group despite the end of the public health emergency.
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Affiliation(s)
- Emily A. G. Faherty
- Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA
| | - Kenneth J. Wilkins
- Biostatistics Program, Office of the Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA;
| | - Sara Jones
- Office of Data Science and Emerging Technologies, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD 20892, USA;
| | - Anup Challa
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN 37203, USA;
| | - Qiuyuan Qin
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY 14642, USA; (Q.Q.); (E.L.H.)
| | - Lauren E. Chan
- Department of Pediatrics, University of Chicago, Chicago, IL 60637, USA
| | - Courtney Olson-Chen
- Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY 14620, USA;
| | - Jessica L. Tarleton
- Department of Obstetrics and Gynecology, Medical University of South Carolina, Charleston, SC 29425, USA;
| | | | - Federico Mariona
- Beaumont Hospital, Dearborn, MI 48124, USA;
- School of Medicine, Wayne State University, Detroit, MI 48201, USA
| | - Elaine L. Hill
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY 14642, USA; (Q.Q.); (E.L.H.)
- Department of Obstetrics and Gynecology, University of Rochester Medical Center, Rochester, NY 14620, USA;
| | - Rena C. Patel
- Departments of Medicine and Global Health, University of Washington, Seattle, WA 98195, USA;
- Department of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA
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Sharathkumar A, Wendt L, Ortman C, Srinivasan R, Chute CG, Chrischilles E, Takemoto CM. COVID-19 outcomes in persons with hemophilia: results from a US-based national COVID-19 surveillance registry. J Thromb Haemost 2024; 22:61-75. [PMID: 37182697 PMCID: PMC10181864 DOI: 10.1016/j.jtha.2023.04.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/29/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023]
Abstract
BACKGROUND Hypercoagulable state contributing to thrombotic complications worsens COVID-19 severity and outcomes, whereas anticoagulation improves outcomes by alleviating hypercoagulability. OBJECTIVES To examine whether hemophilia, an inherent hypocoagulable condition, offers protection against COVID-19 severity and reduces venous thromboembolism (VTE) risk in persons with hemophilia (PwH). METHODS A 1:3 propensity score-matched retrospective cohort study used national COVID-19 registry data (January 2020 through January 2022) to compare outcomes between 300 male PwH and 900 matched controls without hemophilia. RESULTS Analyses of PwH demonstrated that known risk factors (older age, heart failure, hypertension, cancer/malignancy, dementia, and renal and liver disease) contributed to severe COVID-19 and/or 30-day all-cause mortality. Non-central nervous system bleeding was an additional risk factor for poor outcomes in PwH. Odds of developing VTE with COVID-19 in PwH were associated with pre-COVID VTE diagnosis (odds ratio [OR], 51.9; 95% CI, 12.8-266; p < .001), anticoagulation therapy (OR, 12.7; 95% CI, 3.01-48.6; p < .001), and pulmonary disease (OR, 16.1; 95% CI, 10.4-25.4; p < .001). Thirty-day all-cause mortality (OR, 1.27; 95% CI, 0.75-2.11; p = .3) and VTE events (OR, 1.32; 95% CI, 0.64-2.73; p = .4) were not significantly different between the matched cohorts; however, hospitalizations (OR, 1.58; 95% CI, 1.20-2.10; p = .001) and non-central nervous system bleeding events (OR, 4.78; 95% CI, 2.98-7.48; p < .001) were increased in PwH. In multivariate analyses, hemophilia did not reduce adverse outcomes (OR, 1.32; 95% CI, 0.74-2.31; p = .2) or VTE (OR, 1.14; 95% CI, 0.44-2.67; p = .8) but increased bleeding risk (OR, 4.70; 95% CI, 2.98-7.48; p < .001). CONCLUSION After adjusting for patient characteristics/comorbidities, hemophilia increased bleeding risk with COVID-19 but did not protect against severe disease and VTE.
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Affiliation(s)
- Anjali Sharathkumar
- Stead Family Department of Pediatrics, Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA.
| | - Linder Wendt
- Institute for Clinical and Translational Science, University of Iowa, Iowa City, Iowa, USA
| | - Chris Ortman
- Department of Bioinformatics, University of Iowa, Iowa City, Iowa, USA; Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ragha Srinivasan
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | | | - Elizabeth Chrischilles
- Department of Bioinformatics, University of Iowa, Iowa City, Iowa, USA; Department of Epidemiology, School of Public Health, University of Iowa, Iowa, USA
| | - Clifford M Takemoto
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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3
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Mayo CS, Feng MU, Brock KK, Kudner R, Balter P, Buchsbaum JC, Caissie A, Covington E, Daugherty EC, Dekker AL, Fuller CD, Hallstrom AL, Hong DS, Hong JC, Kamran SC, Katsoulakis E, Kildea J, Krauze AV, Kruse JJ, McNutt T, Mierzwa M, Moreno A, Palta JR, Popple R, Purdie TG, Richardson S, Sharp GC, Satomi S, Tarbox LR, Venkatesan AM, Witztum A, Woods KE, Yao Y, Farahani K, Aneja S, Gabriel PE, Hadjiiski L, Ruan D, Siewerdsen JH, Bratt S, Casagni M, Chen S, Christodouleas JC, DiDonato A, Hayman J, Kapoor R, Kravitz S, Sebastian S, Von Siebenthal M, Bosch W, Hurkmans C, Yom SS, Xiao Y. Operational Ontology for Oncology (O3): A Professional Society-Based, Multistakeholder, Consensus-Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data From Patients Treated for Cancer. Int J Radiat Oncol Biol Phys 2023; 117:533-550. [PMID: 37244628 PMCID: PMC10741247 DOI: 10.1016/j.ijrobp.2023.05.033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 05/29/2023]
Abstract
PURPOSE The ongoing lack of data standardization severely undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), radiation oncology information systems, treatment planning systems, and other cancer care and outcomes databases. We sought to create a standardized ontology for clinical data, social determinants of health, and other radiation oncology concepts and interrelationships. METHODS AND MATERIALS The American Association of Physicists in Medicine's Big Data Science Committee was initiated in July 2019 to explore common ground from the stakeholders' collective experience of issues that typically compromise the formation of large inter- and intra-institutional databases from EHRs. The Big Data Science Committee adopted an iterative, cyclical approach to engaging stakeholders beyond its membership to optimize the integration of diverse perspectives from the community. RESULTS We developed the Operational Ontology for Oncology (O3), which identified 42 key elements, 359 attributes, 144 value sets, and 155 relationships ranked in relative importance of clinical significance, likelihood of availability in EHRs, and the ability to modify routine clinical processes to permit aggregation. Recommendations are provided for best use and development of the O3 to 4 constituencies: device manufacturers, centers of clinical care, researchers, and professional societies. CONCLUSIONS O3 is designed to extend and interoperate with existing global infrastructure and data science standards. The implementation of these recommendations will lower the barriers for aggregation of information that could be used to create large, representative, findable, accessible, interoperable, and reusable data sets to support the scientific objectives of grant programs. The construction of comprehensive "real-world" data sets and application of advanced analytical techniques, including artificial intelligence, holds the potential to revolutionize patient management and improve outcomes by leveraging increased access to information derived from larger, more representative data sets.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Dan Ruan
- University of California, Los Angeles
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Sue S Yom
- University of California, San Francisco
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Gu B, Sidhu S, Weinreb RN, Christopher M, Zangwill LM, Baxter SL. Review of Visualization Approaches in Deep Learning Models of Glaucoma. Asia Pac J Ophthalmol (Phila) 2023; 12:392-401. [PMID: 37523431 DOI: 10.1097/apo.0000000000000619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/11/2023] [Indexed: 08/02/2023] Open
Abstract
Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.
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Affiliation(s)
- Byoungyoung Gu
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
| | - Sophia Sidhu
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
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5
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Williams E, Kienast M, Medawar E, Reinelt J, Merola A, Klopfenstein SAI, Flint AR, Heeren P, Poncette AS, Balzer F, Beimes J, von Bünau P, Chromik J, Arnrich B, Scherf N, Niehaus S. A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study. JMIR Med Inform 2023; 11:e43847. [PMID: 36943344 PMCID: PMC10131740 DOI: 10.2196/43847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/24/2023] [Accepted: 01/25/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Increasing digitalization in the medical domain gives rise to large amounts of health care data, which has the potential to expand clinical knowledge and transform patient care if leveraged through artificial intelligence (AI). Yet, big data and AI oftentimes cannot unlock their full potential at scale, owing to nonstandardized data formats, lack of technical and semantic data interoperability, and limited cooperation between stakeholders in the health care system. Despite the existence of standardized data formats for the medical domain, such as Fast Healthcare Interoperability Resources (FHIR), their prevalence and usability for AI remain limited. OBJECTIVE In this paper, we developed a data harmonization pipeline (DHP) for clinical data sets relying on the common FHIR data standard. METHODS We validated the performance and usability of our FHIR-DHP with data from the Medical Information Mart for Intensive Care IV database. RESULTS We present the FHIR-DHP workflow in respect of the transformation of "raw" hospital records into a harmonized, AI-friendly data representation. The pipeline consists of the following 5 key preprocessing steps: querying of data from hospital database, FHIR mapping, syntactic validation, transfer of harmonized data into the patient-model database, and export of data in an AI-friendly format for further medical applications. A detailed example of FHIR-DHP execution was presented for clinical diagnoses records. CONCLUSIONS Our approach enables the scalable and needs-driven data modeling of large and heterogenous clinical data sets. The FHIR-DHP is a pivotal step toward increasing cooperation, interoperability, and quality of patient care in the clinical routine and for medical research.
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Affiliation(s)
| | | | | | | | | | | | - Anne Rike Flint
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Patrick Heeren
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Felix Balzer
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | | | - Jonas Chromik
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Bert Arnrich
- Digital Health - Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | - Nico Scherf
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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6
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Chan LE, Casiraghi E, Laraway B, Coleman B, Blau H, Zaman A, Harris NL, Wilkins K, Antony B, Gargano M, Valentini G, Sahner D, Haendel M, Robinson PN, Bramante C, Reese J. Metformin is associated with reduced COVID-19 severity in patients with prediabetes. Diabetes Res Clin Pract 2022; 194:110157. [PMID: 36400170 PMCID: PMC9663381 DOI: 10.1016/j.diabres.2022.110157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/17/2022]
Abstract
AIMS Studies suggest that metformin is associated with reduced COVID-19 severity in individuals with diabetes compared to other antihyperglycemics. We assessed if metformin is associated with reduced incidence of severe COVID-19 for patients with prediabetes or polycystic ovary syndrome (PCOS), common diseases that increase the risk of severe COVID-19. METHODS This observational, retrospective study utilized EHR data from 52 hospitals for COVID-19 patients with PCOS or prediabetes treated with metformin or levothyroxine/ondansetron (controls). After balancing via inverse probability score weighting, associations with COVID-19 severity were assessed by logistic regression. RESULTS In the prediabetes cohort, when compared to levothyroxine, metformin was associated with a significantly lower incidence of COVID-19 with "mild-ED" or worse (OR [95% CI]: 0.636, [0.455-0.888]) and "moderate" or worse severity (0.493 [0.339-0.718]). Compared to ondansetron, metformin was associated with lower incidence of "mild-ED" or worse severity (0.039 [0.026-0.057]), "moderate" or worse (0.045 [0.03-0.069]), "severe" or worse (0.183 [0.077-0.431]), and "mortality/hospice" (0.223 [0.071-0.694]). For PCOS, metformin showed no significant differences in severity compared to levothyroxine, but was associated with a significantly lower incidence of "mild-ED" or worse (0.101 [0.061-0.166]), and "moderate" or worse (0.094 [0.049-0.18]) COVID-19 outcome compared to ondansetron. CONCLUSIONS Metformin use is associated with less severe COVID-19 in patients with prediabetes or PCOS.
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Affiliation(s)
- Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Bryan Laraway
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Adnin Zaman
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nomi L Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kenneth Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Blessy Antony
- Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Carolyn Bramante
- Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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7
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Baxter SL, Nwanyanwu K, Legault G, Lee AY. Data Sources for Evaluating Health Disparities in Ophthalmology: Where We Are and Where We Need to Go. Ophthalmology 2022; 129:e146-e149. [PMID: 36058733 PMCID: PMC9509471 DOI: 10.1016/j.ophtha.2022.06.035] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/17/2022] [Accepted: 06/06/2022] [Indexed: 10/14/2022] Open
Abstract
Data provide an opportunity to discover disparities and inequities that may otherwise be unrecognized. Within the American Academy of Ophthalmology (AAO) Task Force on Disparities in Eye Care, the Leveraging Data Sub-task Force was charged with identifying data sources to study health disparities in eye care and to leverage data to advance health equity. We evaluated large data sources to determine their strengths, deficiencies, and relative accessibility in relation to the likelihood of identifying eye care disparities. We highlight the current challenges with these data sources and review key recommendations for improving future sources for studying health disparities in eye care.
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Affiliation(s)
- Sally L Baxter
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California; Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Kristen Nwanyanwu
- Department of Ophthalmology and Visual Science, Yale University, New Haven, Connecticut
| | - Gary Legault
- Department of Ophthalmology, Brooke Army Medical Center, Fort Sam Houston, Texas
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington.
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8
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Chan LE, Casiraghi E, Laraway B, Coleman B, Blau H, Zaman A, Harris N, Wilkins K, Gargano M, Valentini G, Sahner D, Haendel M, Robinson PN, Bramante C, Reese J. Metformin is Associated with Reduced COVID-19 Severity in Patients with Prediabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.08.29.22279355. [PMID: 36093353 PMCID: PMC9460973 DOI: 10.1101/2022.08.29.22279355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Background With the continuing COVID-19 pandemic, identifying medications that improve COVID-19 outcomes is crucial. Studies suggest that use of metformin, an oral antihyperglycemic, is associated with reduced COVID-19 severity in individuals with diabetes compared to other antihyperglycemic medications. Some patients without diabetes, including those with polycystic ovary syndrome (PCOS) and prediabetes, are prescribed metformin for off-label use, which provides an opportunity to further investigate the effect of metformin on COVID-19. Participants In this observational, retrospective analysis, we leveraged the harmonized electronic health record data from 53 hospitals to construct cohorts of COVID-19 positive, metformin users without diabetes and propensity-weighted control users of levothyroxine (a medication for hypothyroidism that is not known to affect COVID-19 outcome) who had either PCOS (n = 282) or prediabetes (n = 3136). The primary outcome of interest was COVID-19 severity, which was classified as: mild, mild ED (emergency department), moderate, severe, or mortality/hospice. Results In the prediabetes cohort, metformin use was associated with a lower rate of COVID-19 with severity of mild ED or worse (OR: 0.630, 95% CI 0.450 - 0.882, p < 0.05) and a lower rate of COVID-19 with severity of moderate or worse (OR: 0.490, 95% CI 0.336 - 0.715, p < 0.001). In patients with PCOS, we found no significant association between metformin use and COVID-19 severity, although the number of patients was relatively small. Conclusions Metformin was associated with less severe COVID-19 in patients with prediabetes, as seen in previous studies of patients with diabetes. This is an important finding, since prediabetes affects between 19 and 38% of the US population, and COVID-19 is an ongoing public health emergency. Further observational and prospective studies will clarify the relationship between metformin and COVID-19 severity in patients with prediabetes, and whether metformin usage may reduce COVID-19 severity.
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Affiliation(s)
- Lauren E. Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Bryan Laraway
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Adnin Zaman
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nomi Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kenneth Wilkins
- Biostatistics Program, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Michael Gargano
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Carolyn Bramante
- Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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9
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Brandt PS, Pacheco JA, Adekkanattu P, Sholle ET, Abedian S, Stone DJ, Knaack DM, Xu J, Xu Z, Peng Y, Benda NC, Wang F, Luo Y, Jiang G, Pathak J, Rasmussen LV. Design and validation of a FHIR-based EHR-driven phenotyping toolbox. J Am Med Inform Assoc 2022; 29:1449-1460. [PMID: 35799370 PMCID: PMC9382394 DOI: 10.1093/jamia/ocac063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 04/04/2022] [Accepted: 06/17/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms. MATERIALS AND METHODS We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation. We validated the performance of the tool by executing a thrombotic event phenotype definition at 3 sites, Mayo Clinic (MC), Northwestern Medicine (NM), and Weill Cornell Medicine (WCM), and used manual review to determine precision and recall. RESULTS An initial version of the PhEMA Workbench has been released, which supports phenotype authoring, execution, and publishing to a shared phenotype definition repository. The resulting thrombotic event phenotype definition consisted of 11 CQL statements, and 24 value sets containing a total of 834 codes. Technical validation showed satisfactory performance (both NM and MC had 100% precision and recall and WCM had a precision of 95% and a recall of 84%). CONCLUSIONS We demonstrate that the PhEMA Workbench can facilitate EHR-driven phenotype definition, execution, and phenotype sharing in heterogeneous clinical research data environments. A phenotype definition that integrates with existing standards-compliant systems, and the use of a formal representation facilitates automation and can decrease potential for human error.
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Affiliation(s)
- Pascal S Brandt
- Corresponding Author: Pascal S. Brandt, Department of Biomedical Informatics & Medical Education, University of Washington, Box 358047, Seattle, WA 98195, USA;
| | - Jennifer A Pacheco
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Prakash Adekkanattu
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Evan T Sholle
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Sajjad Abedian
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Daniel J Stone
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - David M Knaack
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Jie Xu
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Zhenxing Xu
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Yifan Peng
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Natalie C Benda
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Fei Wang
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Guoqian Jiang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Jyotishman Pathak
- Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, New York, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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10
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Reese JT, Coleman B, Chan L, Blau H, Callahan TJ, Cappelletti L, Fontana T, Bradwell KR, Harris NL, Casiraghi E, Valentini G, Karlebach G, Deer R, McMurry JA, Haendel MA, Chute CG, Pfaff E, Moffitt R, Spratt H, Singh JA, Mungall CJ, Williams AE, Robinson PN. NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study. Virol J 2022; 19:84. [PMID: 35570298 PMCID: PMC9107579 DOI: 10.1186/s12985-022-01813-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/04/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of 19,746 COVID-19 inpatients was constructed by matching cases (treated with NSAIDs at the time of admission) and 19,746 controls (not treated) from 857,061 patients with COVID-19 available for analysis. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.
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Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Lauren Chan
- Translational and Integrative Sciences Center, Oregon State University, Corvallis, OR, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Tiffany J Callahan
- Computational Bioscience, University of Colorado Anschutz Medical Campus, Boulder, CO, USA
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
| | | | - Nomi L Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Rome, Italy
| | - Giorgio Valentini
- AnacletoLab, Dipartimento Di Informatica, Università Degli Studi Di Milano, Milan, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Rome, Italy
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Rachel Deer
- University of Texas Medical Branch, Galveston, TX, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Emily Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Heidi Spratt
- University of Texas Medical Branch, Galveston, TX, USA
| | - Jasvinder A Singh
- University of Alabama at Birmingham, Birmingham, AL, USA
- Medicine Service, VA Medical Center, Birmingham, AL, USA
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew E Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts University School of Medicine, Boston, USA
- OHDSI Center at the Roux Institute, Northeastern University, Boston, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA.
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11
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Reese JT, Coleman B, Chan L, Blau H, Callahan TJ, Cappelletti L, Fontana T, Bradwell KR, Harris NL, Casiraghi E, Valentini G, Karlebach G, Deer R, McMurry JA, Haendel MA, Chute CG, Pfaff E, Moffitt R, Spratt H, Singh J, Mungall CJ, Williams AE, Robinson PN. NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2021:2021.04.13.21255438. [PMID: 33907758 PMCID: PMC8077581 DOI: 10.1101/2021.04.13.21255438] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Non-steroidal anti-inflammatory drugs (NSAIDs) are commonly used to reduce pain, fever, and inflammation but have been associated with complications in community-acquired pneumonia. Observations shortly after the start of the COVID-19 pandemic in 2020 suggested that ibuprofen was associated with an increased risk of adverse events in COVID-19 patients, but subsequent observational studies failed to demonstrate increased risk and in one case showed reduced risk associated with NSAID use. METHODS A 38-center retrospective cohort study was performed that leveraged the harmonized, high-granularity electronic health record data of the National COVID Cohort Collaborative. A propensity-matched cohort of COVID-19 inpatients was constructed by matching cases (treated with NSAIDs) and controls (not treated) from 857,061 patients with COVID-19. The primary outcome of interest was COVID-19 severity in hospitalized patients, which was classified as: moderate, severe, or mortality/hospice. Secondary outcomes were acute kidney injury (AKI), extracorporeal membrane oxygenation (ECMO), invasive ventilation, and all-cause mortality at any time following COVID-19 diagnosis. RESULTS Logistic regression showed that NSAID use was not associated with increased COVID-19 severity (OR: 0.57 95% CI: 0.53-0.61). Analysis of secondary outcomes using logistic regression showed that NSAID use was not associated with increased risk of all-cause mortality (OR 0.51 95% CI: 0.47-0.56), invasive ventilation (OR: 0.59 95% CI: 0.55-0.64), AKI (OR: 0.67 95% CI: 0.63-0.72), or ECMO (OR: 0.51 95% CI: 0.36-0.7). In contrast, the odds ratios indicate reduced risk of these outcomes, but our quantitative bias analysis showed E-values of between 1.9 and 3.3 for these associations, indicating that comparatively weak or moderate confounder associations could explain away the observed associations. CONCLUSIONS Study interpretation is limited by the observational design. Recording of NSAID use may have been incomplete. Our study demonstrates that NSAID use is not associated with increased COVID-19 severity, all-cause mortality, invasive ventilation, AKI, or ECMO in COVID-19 inpatients. A conservative interpretation in light of the quantitative bias analysis is that there is no evidence that NSAID use is associated with risk of increased severity or the other measured outcomes. Our findings are the largest EHR-based analysis of the effect of NSAIDs on outcome in COVID-19 patients to date. Our results confirm and extend analogous findings in previous observational studies using a large cohort of patients drawn from 38 centers in a nationally representative multicenter database.
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Affiliation(s)
- Justin T Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Lauren Chan
- Translational and Integrative Sciences Center, Oregon State University, Corvallis, OR, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Tiffany J Callahan
- Computational Bioscience, University of Colorado Anschutz Medical Campus, Boulder, CO, USA
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Luca Cappelletti
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | - Tommaso Fontana
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
| | | | - Nomi L Harris
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Elena Casiraghi
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Roma, Italy
| | - Giorgio Valentini
- AnacletoLab, Dipartimento di Informatica, Università degli Studi di Milano, Italy
- CINI, National Laboratory in Artificial Intelligence and Intelligent Systems-AIIS, Roma, Italy
| | - Guy Karlebach
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Rachel Deer
- University of Texas Medical Branch, Galveston, TX, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Emily Pfaff
- North Carolina Translational and Clinical Sciences Institute (NC TraCS), University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Heidi Spratt
- University of Texas Medical Branch, Galveston, TX, USA
| | - Jasvinder Singh
- University of Alabama at Birmingham, Birmingham, AL, USA
- Medicine Service, VA Medical Center, Birmingham, AL, USA
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Andrew E Williams
- Tufts Medical Center Clinical and Translational Science Institute, Tufts Medical Center, Boston, MA, USA
- Tufts University School of Medicine, Institute for Clinical Research and Health Policy Studies
- Northeastern University, OHDSI Center at the Roux Institute
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
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12
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Rosenau L, Majeed RW, Ingenerf J, Kiel A, Kroll B, Köhler T, Prokosch HU, Gruendner J. Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for federated Feasibility Queries in the context of COVID-19: An automated approach (Preprint). JMIR Med Inform 2021; 10:e35789. [PMID: 35380548 PMCID: PMC9049646 DOI: 10.2196/35789] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 01/27/2022] [Accepted: 02/13/2022] [Indexed: 12/02/2022] Open
Abstract
Background The COVID-19 pandemic highlighted the importance of making research data from all German hospitals available to scientists to respond to current and future pandemics promptly. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible. The German Corona Consensus Dataset (GECCO) specifies how data for COVID-19 patients will be standardized in Fast Healthcare Interoperability Resources (FHIR) profiles across German hospitals. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible. A simplified visual representation is needed to reduce the technical burden, while allowing feasibility queries. Objective This study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. Furthermore, it describes how this ontology can be used in a user interface (UI) and how a mapping and a terminology tree created together with the ontology can translate user input into FHIR queries. Methods We used the FHIR profiles from the GECCO data set combined with a terminology server to generate an ontology and the required mapping files for the translation. We analyzed the profiles and identified search criteria for the visual representation. In this process, we reduced the complex profiles to code value pairs for improved usability. We enriched our ontology with the necessary information to display it in a UI. We also developed an intermediate query language to transform the queries from the UI to federated FHIR requests. Separation of concerns resulted in discrepancies between the criteria used in the intermediate query format and the target query language. Therefore, a mapping was created to reintroduce all information relevant for creating the query in its target language. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process. Results In the scope of this project, 82 (99%) of 83 elements defined in the GECCO profile were successfully implemented. We verified our solution based on an independently developed test patient. A discrepancy between the test data and the criteria was found in 6 cases due to different versions used to generate the test data and the UI profiles, the support for specific code systems, and the evaluation of postcoordinated Systematized Nomenclature of Medicine (SNOMED) codes. Our results highlight the need for governance mechanisms for version changes, concept mapping between values from different code systems encoding the same concept, and support for different unit dimensions. Conclusions We developed an automatic process to generate ontology and mapping files for FHIR-formatted data. Our tests found that this process works for most of our chosen FHIR profile criteria. The process established here works directly with FHIR profiles and a terminology server, making it extendable to other FHIR profiles and demonstrating that automatic ontology generation on FHIR profiles is feasible.
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Affiliation(s)
| | - Raphael W Majeed
- Institute for Medical Informatics, University Clinic Rheinisch-Westfälische Technische Hochschule Aachen, Aachen, Germany
| | | | - Alexander Kiel
- Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany
- Federated Information Systems, German Cancer Research Center, Heidelberg, Germany
| | - Björn Kroll
- IT Center for Clinical Research, Lübeck, Germany
| | - Thomas Köhler
- Federated Information Systems, German Cancer Research Center, Heidelberg, Germany
- Complex Data Processing in Medical Informatics, Medical Faculty Mannheim, Mannheim, Germany
| | - Hans-Ulrich Prokosch
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Julian Gruendner
- Chair of Medical Informatics, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
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13
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Baxter SL, Lee AY. Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice. Curr Opin Ophthalmol 2021; 32:431-438. [PMID: 34231531 PMCID: PMC8373825 DOI: 10.1097/icu.0000000000000781] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
PURPOSE OF REVIEW The purpose of this review is to provide an overview of healthcare standards and their relevance to multiple ophthalmic workflows, with a specific emphasis on describing gaps in standards development needed for improved integration of artificial intelligence technologies into ophthalmic practice. RECENT FINDINGS Healthcare standards are an essential component of data exchange and critical for clinical practice, research, and public health surveillance activities. Standards enable interoperability between clinical information systems, healthcare information exchange between institutions, and clinical decision support in a complex health information technology ecosystem. There are several gaps in standards in ophthalmology, including relatively low adoption of imaging standards, lack of use cases for integrating apps providing artificial intelligence -based decision support, lack of common data models to harmonize big data repositories, and no standards regarding interfaces and algorithmic outputs. SUMMARY These gaps in standards represent opportunities for future work to develop improved data flow between various elements of the digital health ecosystem. This will enable more widespread adoption and integration of artificial intelligence-based tools into clinical practice. Engagement and support from the ophthalmology community for standards development will be important for advancing this work.
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Affiliation(s)
- Sally L. Baxter
- Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, USA
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, USA
| | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
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