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Nikiema JN, Liang J, Liang MQ, Dos Anjos D, Motulsky A. Improving the interoperability of drugs terminologies: Infusing local standardization with an international perspective. J Biomed Inform 2024; 151:104614. [PMID: 38395099 DOI: 10.1016/j.jbi.2024.104614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/10/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
OBJECTIVES The objective of this study is to describe how OCRx (Canadian Drug Ontology) has been built to address the dual need for local drug information integration in Canada and alignment with international standards requirements. METHODS This paper delves into (i) the implementation efforts to meet the Identification of Medicinal Product (IDMP) requirements in OCRx, alongside the ontology update strategy, (ii) the structure of the ontology itself, (iii) the alignment approach with several reference Knowledge Organization Systems, including SNOMED CT, RxNorm, and the list of "Code Identifiant de Spécialité" (CIS-Code), and (iv) the look-up services developed to facilitate its access and utilization. RESULTS Each OCRx release contains two distinct versions: the full and the up-to-date version. The full version encompasses all drugs with a DIN code sanctioned by Health Canada, while the up-to-date version is limited to drugs currently marketed in Canada. In the last release of OCRx, the full version comprises 162,400 classes; meanwhile, the up-to-date version consists of 36,909 classes. In terms of mappings with OCRx, substances in RxNorm and SNOMED CT fall below 40%, registering at 37% and 22% respectively. Meanwhile, mappings for CIS-Code achieve coverage of 61%. The strength mappings are notably low for RxNorm at 40% and for CIS-code at 28%. This affects the mapping of clinical drugs, which are predominantly alignable through post-coordinated expressions: 56% for RxNorm, 80% for SNOMED CT, and 35% for CIS-Code. The main support service of OCRx is a look-up service known as PaperRx that displays OCRx's entities based on description logic queries (DL-queries) performed through the classified structure of OCRx. The look-up services also contain a SPARQL endpoint, an OCRx OWL file downloader, and a RESTful API. DISCUSSION The OCRx ontology demonstrates a significant effort towards integrating Canadian drug information with international standards. However, there are areas for improvement. In the future, our focus will be on refining the structure of OCRx for better classification capability and improvement of dosage conversion. Additionally, we aim to harness OCRx in constructing an ontology-based annotator, setting our sights on its deployment in real-world data integration scenarios.
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Affiliation(s)
- Jean Noël Nikiema
- Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Canada; Centre de recherche en santé publique, Université de Montréal et CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Canada; Laboratoire Transformation Numérique en Santé (LabTNS), Canada.
| | - James Liang
- Centre de recherche en santé publique, Université de Montréal et CIUSSS du Centre-Sud-de-l'Île-de-Montréal, Canada; Laboratoire Transformation Numérique en Santé (LabTNS), Canada
| | - Man Qing Liang
- Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Canada; Laboratoire Transformation Numérique en Santé (LabTNS), Canada; Research Center, Centre hospitalier de l'Université de Montréal (CRCHUM), Canada
| | - Davllyn Dos Anjos
- Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Canada; Laboratoire Transformation Numérique en Santé (LabTNS), Canada; Research Center, Centre hospitalier de l'Université de Montréal (CRCHUM), Canada
| | - Aude Motulsky
- Department of Management, Evaluation and Health Policy, School of Public Health, Université de Montréal, Canada; Laboratoire Transformation Numérique en Santé (LabTNS), Canada; Research Center, Centre hospitalier de l'Université de Montréal (CRCHUM), Canada
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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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/29/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023]
Abstract
BACKGROUND Hypercoagulable state contributing to thrombotic complications worsens COVID-19 severity and outcomes, whereas anticoagulation improves outcomes by alleviating hypercoagulability. OBJECTIVES To examine whether hemophilia, an inherent hypocoagulable condition, offers protection against COVID-19 severity and reduces venous thromboembolism (VTE) risk in persons with hemophilia (PwH). METHODS A 1:3 propensity score-matched retrospective cohort study used national COVID-19 registry data (January 2020 through January 2022) to compare outcomes between 300 male PwH and 900 matched controls without hemophilia. RESULTS Analyses of PwH demonstrated that known risk factors (older age, heart failure, hypertension, cancer/malignancy, dementia, and renal and liver disease) contributed to severe COVID-19 and/or 30-day all-cause mortality. Non-central nervous system bleeding was an additional risk factor for poor outcomes in PwH. Odds of developing VTE with COVID-19 in PwH were associated with pre-COVID VTE diagnosis (odds ratio [OR], 51.9; 95% CI, 12.8-266; p < .001), anticoagulation therapy (OR, 12.7; 95% CI, 3.01-48.6; p < .001), and pulmonary disease (OR, 16.1; 95% CI, 10.4-25.4; p < .001). Thirty-day all-cause mortality (OR, 1.27; 95% CI, 0.75-2.11; p = .3) and VTE events (OR, 1.32; 95% CI, 0.64-2.73; p = .4) were not significantly different between the matched cohorts; however, hospitalizations (OR, 1.58; 95% CI, 1.20-2.10; p = .001) and non-central nervous system bleeding events (OR, 4.78; 95% CI, 2.98-7.48; p < .001) were increased in PwH. In multivariate analyses, hemophilia did not reduce adverse outcomes (OR, 1.32; 95% CI, 0.74-2.31; p = .2) or VTE (OR, 1.14; 95% CI, 0.44-2.67; p = .8) but increased bleeding risk (OR, 4.70; 95% CI, 2.98-7.48; p < .001). CONCLUSION After adjusting for patient characteristics/comorbidities, hemophilia increased bleeding risk with COVID-19 but did not protect against severe disease and VTE.
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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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Leviton A, Loddenkemper T. Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review. BMC Med Res Methodol 2023; 23:271. [PMID: 37974111 PMCID: PMC10652539 DOI: 10.1186/s12874-023-02102-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/08/2023] [Indexed: 11/19/2023] Open
Abstract
Real world evidence is now accepted by authorities charged with assessing the benefits and harms of new therapies. Clinical trials based on real world evidence are much less expensive than randomized clinical trials that do not rely on "real world evidence" such as contained in electronic health records (EHR). Consequently, we can expect an increase in the number of reports of these types of trials, which we identify here as 'EHR-sourced trials.' 'In this selected literature review, we discuss the various designs and the ethical issues they raise. EHR-sourced trials have the potential to improve/increase common data elements and other aspects of the EHR and related systems. Caution is advised, however, in drawing causal inferences about the relationships among EHR variables. Nevertheless, we anticipate that EHR-CTs will play a central role in answering research and regulatory questions.
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Affiliation(s)
- Alan Leviton
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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Wardi G, Owens R, Josef C, Malhotra A, Longhurst C, Nemati S. Bringing the Promise of Artificial Intelligence to Critical Care: What the Experience With Sepsis Analytics Can Teach Us. Crit Care Med 2023; 51:985-991. [PMID: 37098790 PMCID: PMC10335736 DOI: 10.1097/ccm.0000000000005894] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/27/2023]
Affiliation(s)
- Gabriel Wardi
- Department of Emergency Medicine, UC San Diego Health, University of California, San Diego, CA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, UC San Diego Health, University of California, San Diego, CA
| | - Robert Owens
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, UC San Diego Health, University of California, San Diego, CA
| | | | - Atul Malhotra
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, UC San Diego Health, University of California, San Diego, CA
| | - Christopher Longhurst
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA
| | - Shamim Nemati
- Department of Biomedical Informatics, UC San Diego Health, University of California, San Diego, CA
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Abbasizanjani H, Torabi F, Bedston S, Bolton T, Davies G, Denaxas S, Griffiths R, Herbert L, Hollings S, Keene S, Khunti K, Lowthian E, Lyons J, Mizani MA, Nolan J, Sudlow C, Walker V, Whiteley W, Wood A, Akbari A. Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration. BMC Med Inform Decis Mak 2023; 23:8. [PMID: 36647111 PMCID: PMC9842203 DOI: 10.1186/s12911-022-02093-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 12/21/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. METHODS Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. RESULTS Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. CONCLUSIONS We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.
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Affiliation(s)
- Hoda Abbasizanjani
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK.
| | - Fatemeh Torabi
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Stuart Bedston
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Thomas Bolton
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Gareth Davies
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Spiros Denaxas
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Rowena Griffiths
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Laura Herbert
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | | | - Spencer Keene
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Kamlesh Khunti
- Diabetes Research Centre, University of Leicester, Leicester, UK
| | - Emily Lowthian
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Jane Lyons
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
| | - Mehrdad A Mizani
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - John Nolan
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Cathie Sudlow
- British Heart Foundation Data Science Centre, Health Data Research UK, London, UK
| | - Venexia Walker
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - William Whiteley
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Angela Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Ashley Akbari
- Population Data Science, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea, UK
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Hadley E, Yoo YJ, Patel S, Zhou A, Laraway B, Wong R, Preiss A, Chew R, Davis H, Chute CG, Pfaff ER, Loomba J, Haendel M, Hill E, Moffitt R. SARS-CoV-2 Reinfection is Preceded by Unique Biomarkers and Related to Initial Infection Timing and Severity: an N3C RECOVER EHR-Based Cohort Study. medRxiv 2023:2023.01.03.22284042. [PMID: 36656776 PMCID: PMC9844020 DOI: 10.1101/2023.01.03.22284042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Although the COVID-19 pandemic has persisted for over 2 years, reinfections with SARS-CoV-2 are not well understood. We use the electronic health record (EHR)-based study cohort from the National COVID Cohort Collaborative (N3C) as part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative to characterize reinfection, understand development of Long COVID after reinfection, and compare severity of reinfection with initial infection. We validate previous findings of reinfection incidence (5.9%), the occurrence of most reinfections during the Omicron epoch, and evidence of multiple reinfections. We present novel findings that Long COVID diagnoses occur closer to the index date for infection or reinfection in the Omicron BA epoch. We report lower albumin levels leading up to reinfection and a statistically significant association of severity between first infection and reinfection (chi-squared value: 9446.2, p-value: 0) with a medium effect size (Cramer's V: 0.18, DoF = 4).
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Affiliation(s)
| | | | | | - Andrea Zhou
- University of Virginia, Charlottesville, VA, US
| | | | | | | | - Rob Chew
- RTI International, Durham, NC, US
| | - Hannah Davis
- RECOVER Patient Led Research Collaborative (PLRC), US
| | | | | | | | - Melissa Haendel
- University of Colorado Anschutz Medical Campus, Denver, CO, US
| | - Elaine Hill
- University of Rochester Medical Center, Rochester, NY, US
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Papez V, Moinat M, Voss EA, Bazakou S, Van Winzum A, Peviani A, Payralbe S, Lara EG, Kallfelz M, Asselbergs FW, Prieto-Alhambra D, Dobson RJB, Denaxas S. Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond. J Am Med Inform Assoc 2022; 30:103-111. [PMID: 36227072 PMCID: PMC9619789 DOI: 10.1093/jamia/ocac203] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/03/2022] [Accepted: 10/12/2022] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVE The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDMs) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500 000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. MATERIALS AND METHODS We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHRs) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. RESULTS We identified 502 505 participants (3086 with COVID-19) and transformed 690 fields (1 373 239 555 rows) to the OMOP CDM using 8 different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported noncancer illnesses 946 053 (83.91% of all source entries), cancers 37 802 (70.81%), medications 1 218 935 (88.25%), and prescriptions 864 788 (86.96%). In EHR, we transformed 13 028 182 (99.95%) hospital diagnoses, 6 465 399 (89.2%) procedures, 337 896 333 primary care diagnoses (CTV3, SNOMED-CT), 139 966 587 (98.74%) prescriptions (dm+d) and 77 127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. DISCUSSION AND CONCLUSION Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.
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Affiliation(s)
| | | | - Erica A Voss
- Department of Epidemiology, Janssen Research & Development LLC, Raritan, New Jersey, USA
| | | | | | | | | | | | | | - Folkert W Asselbergs
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
| | - Daniel Prieto-Alhambra
- Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - Richard J B Dobson
- Institute of Health Informatics, University College London, London, UK
- Health Data Research UK, London, UK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
| | - Spiros Denaxas
- Corresponding Author: Spiros Denaxas, PhD, Institute of Health Informatics, University College London, London NW12DA, UK;
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Liu K, Witteveen-Lane M, Glicksberg BS, Kulkarni O, Shankar R, Chekalin E, Paithankar S, Yang J, Chesla D, Chen B. BGLM: big data-guided LOINC mapping with multi-language support. JAMIA Open 2022; 5:ooac099. [PMID: 36448022 PMCID: PMC9696745 DOI: 10.1093/jamiaopen/ooac099] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 06/08/2022] [Accepted: 11/13/2022] [Indexed: 09/27/2023] Open
Abstract
MOTIVATION Mapping internal, locally used lab test codes to standardized logical observation identifiers names and codes (LOINC) terminology has become an essential step in harmonizing electronic health record (EHR) data across different institutions. However, most existing LOINC code mappers are based on text-mining technology and do not provide robust multi-language support. MATERIALS AND METHODS We introduce a simple, yet effective tool called big data-guided LOINC code mapper (BGLM), which leverages the large amount of patient data stored in EHR systems to perform LOINC coding mapping. Distinguishing from existing methods, BGLM conducts mapping based on distributional similarity. RESULTS We validated the performance of BGLM with real-world datasets and showed that high mapping precision could be achieved under proper false discovery rate control. In addition, we showed that the mapping results of BGLM could be used to boost the performance of Regenstrief LOINC Mapping Assistant (RELMA), one of the most widely used LOINC code mappers. CONCLUSIONS BGLM paves a new way for LOINC code mapping and therefore could be applied to EHR systems without the restriction of languages. BGLM is freely available at https://github.com/Bin-Chen-Lab/BGLM.
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Affiliation(s)
- Ke Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | | | - Benjamin S Glicksberg
- Icahn School of Medicine at Mount Sinai, The Hasso Plattner Institute for Digital Health at Mount Sinai, New York City, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, New York, USA
| | - Omkar Kulkarni
- Office of Research, Spectrum Health, Grand Rapids, Michigan, USA
| | - Rama Shankar
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Evgeny Chekalin
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Shreya Paithankar
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Jeanne Yang
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan, USA
| | - Dave Chesla
- Office of Research, Spectrum Health, Grand Rapids, Michigan, USA
- Department of Obstetrics, Gynecology and Reproductive Biology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
| | - Bin Chen
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
- Department of Pharmacology and Toxicology, College of Human Medicine, Michigan State University, Grand Rapids, Michigan, USA
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Jiang S, Loomba J, Sharma S, Brown D. Vital Measurements of Hospitalized COVID-19 Patients as a Predictor of Long COVID: An EHR-based Cohort Study from the RECOVER Program in N3C. ArXiv 2022:arXiv:2211.08485v1. [PMID: 36415203 PMCID: PMC9681042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of Long COVID could play an important role in understanding the characteristic of this new disease. Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could reflect body's most basic functions and are measured regularly during hospitalization, so among patients diagnosed COVID positive and hospitalized, we analyze the vital measurements of first 7 days since the hospitalization start date to study the pattern of the vital measurements and predict Long COVID with the information from vital measurements.
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Affiliation(s)
- Sihang Jiang
- Department of Engineering of Systems and Environment, University of Virginia
| | - Johanna Loomba
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia
| | | | - Donald Brown
- Department of Engineering of Systems and Environment, University of Virginia
- School of Data Science, University of Virginia
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10
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Khodaverdi M, Price BS, Porterfield JZ, Bunnell HT, Vest MT, Anzalone AJ, Harper J, Kimble WD, Moradi H, Hendricks B, Santangelo SL, Hodder SL. An ordinal severity scale for COVID-19 retrospective studies using Electronic Health Record data. JAMIA Open 2022; 5:ooac066. [PMID: 35911666 PMCID: PMC9278199 DOI: 10.1093/jamiaopen/ooac066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 12/02/2022] Open
Abstract
Objectives Although the World Health Organization (WHO) Clinical Progression Scale for COVID-19 is useful in prospective clinical trials, it cannot be effectively used with retrospective Electronic Health Record (EHR) datasets. Modifying the existing WHO Clinical Progression Scale, we developed an ordinal severity scale (OS) and assessed its usefulness in the analyses of COVID-19 patient outcomes using retrospective EHR data. Materials and Methods An OS was developed to assign COVID-19 disease severity using the Observational Medical Outcomes Partnership common data model within the National COVID Cohort Collaborative (N3C) data enclave. We then evaluated usefulness of the developed OS using heterogenous EHR data from January 2020 to October 2021 submitted to N3C by 63 healthcare organizations across the United States. Principal component analysis (PCA) was employed to characterize changes in disease severity among patients during the 28-day period following COVID-19 diagnosis. Results The data set used in this analysis consists of 2 880 456 patients. PCA of the day-to-day variation in OS levels over the totality of the 28-day period revealed contrasting patterns of variation in disease severity within the first and second 14 days and illustrated the importance of evaluation over the full 28-day period. Discussion An OS with well-defined, robust features, based on discrete EHR data elements, is useful for assessments of COVID-19 patient outcomes, providing insights on the progression of COVID-19 disease severity over time. Conclusions The OS provides a framework that can facilitate better understanding of the course of acute COVID-19, informing clinical decision-making and resource allocation.
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Affiliation(s)
- Maryam Khodaverdi
- West Virginia Clinical and Translational Sciences Institute, Morgantown, West Virginia, USA
| | - Bradley S Price
- West Virginia Clinical and Translational Sciences Institute, Morgantown, West Virginia, USA
- Department of Management Information Systems, West Virginia University, Morgantown, West Virginia, USA
| | | | - H Timothy Bunnell
- Biomedical Research Informatics Center, Nemours Children's Health, Wilmington, Delaware, USA
| | - Michael T Vest
- Section of Pulmonary and Critical Care Medicine, Christiana Care Health System, Newark, Delaware, USA
- Department of Medicine, Sidney Kimmel College of Medicine, Philadelphia, Pennsylvania, USA
| | - Alfred Jerrod Anzalone
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | | | - Wes D Kimble
- West Virginia Clinical and Translational Sciences Institute, Morgantown, West Virginia, USA
| | - Hamidreza Moradi
- Department of Data Science, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Brian Hendricks
- Department of Epidemiology, West Virginia University, Morgantown, West Virginia, USA
| | - Susan L Santangelo
- Center for Psychiatric Research, Maine Medical Center Research Institute, and Maine Medical Center, Portland, Maine, USA
- Department of Psychiatry, Tufts University School of Medicine, Boston, Massachusetts, USA
| | - Sally L Hodder
- West Virginia Clinical and Translational Sciences Institute, Morgantown, West Virginia, USA
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11
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Yoo YJ, Wilkins KJ, Alakwaa F, Liu F, Torre-Healy LA, Krichevsky S, Hong SS, Sakhuja A, Potu CK, Saltz JH, Saran R, Zhu RL, Setoguchi S, Kane-Gill SL, Mallipattu SK, He Y, Ellison DH, Byrd JB, Parikh CR, Moffitt RA, Koraishy FM. COVID-19-associated AKI in hospitalized US patients: incidence, temporal trends, geographical distribution, risk factors and mortality. medRxiv 2022:2022.09.02.22279398. [PMID: 36093355 PMCID: PMC9460976 DOI: 10.1101/2022.09.02.22279398] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Background Acute kidney injury (AKI) is associated with mortality in patients hospitalized with COVID-19, however, its incidence, geographic distribution, and temporal trends since the start of the pandemic are understudied. Methods Electronic health record data were obtained from 53 health systems in the United States (US) in the National COVID Cohort Collaborative (N3C). We selected hospitalized adults diagnosed with COVID-19 between March 6th, 2020, and January 6th, 2022. AKI was determined with serum creatinine (SCr) and diagnosis codes. Time were divided into 16-weeks (P1-6) periods and geographical regions into Northeast, Midwest, South, and West. Multivariable models were used to analyze the risk factors for AKI or mortality. Results Out of a total cohort of 306,061, 126,478 (41.0 %) patients had AKI. Among these, 17.9% lacked a diagnosis code but had AKI based on the change in SCr. Similar to patients coded for AKI, these patients had higher mortality compared to those without AKI. The incidence of AKI was highest in P1 (49.3%), reduced in P2 (40.6%), and relatively stable thereafter. Compared to the Midwest, the Northeast, South, and West had higher adjusted AKI incidence in P1, subsequently, the South and West regions continued to have the highest relative incidence. In multivariable models, AKI defined by either SCr or diagnostic code, and the severity of AKI was associated with mortality. Conclusions Uncoded cases of COVID-19-associated AKI are common and associated with mortality. The incidence and distribution of COVID-19-associated AKI have changed since the first wave of the pandemic in the US.
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Affiliation(s)
- Yun Jae Yoo
- Department of Biology, Stony Brook University, Stony Brook, NY
| | - Kenneth J. Wilkins
- Biostatistics Program, Office of the Director, National Institute of Diabetes & Digestive & Kidney Diseases; Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services, University of the Health Sciences, Bethesda, MD
| | - Fadhl Alakwaa
- Department of Internal Medicine, Nephrology Division, University of Michigan, Ann Arbor, MI
| | - Feifan Liu
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA
| | - Luke A. Torre-Healy
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Spencer Krichevsky
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Stephanie S. Hong
- Biomedical Informatics and Data Science Section, Department of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ankit Sakhuja
- Section of Cardiovascular Critical Care, Dept of Cardiovascular and Thoracic Surgery, West Virginia University, Morgantown, WV
| | - Chetan K. Potu
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY
| | - Joel H. Saltz
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Rajiv Saran
- Division of Nephrology, Department of Internal Medicine and Department of Epidemiology, University of Michigan, Ann Arbor, MI
| | - Richard L. Zhu
- Institution for Clinical and Translational Research, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Soko Setoguchi
- Department of Medicine and Epidemiology, Rutgers Robert Wood Johnson Medical School and School of Public Health, New Brunswick, NJ
| | - Sandra L. Kane-Gill
- Department of Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA
| | - Sandeep K. Mallipattu
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, and Northport VAMC, Northport, NY, USA
| | - Yongqun He
- Unit for Laboratory Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI
| | - David H. Ellison
- Oregon Clinical & Translational Research Institute, Oregon Health & Science University, Portland OR and VA Portland Health Care System, Portland, OR
| | - James Brian Byrd
- Division of Cardiovascular Medicine, Department of Medicine, University of Michigan, Ann Arbor, MI
| | | | - Richard A. Moffitt
- Department of Biomedical Informatics, Cancer Center, Department of Pathology, Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY
| | - Farrukh M. Koraishy
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook University, Stony Brook, and Northport VAMC, Northport, NY, USA
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