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Thakur A, Molaei S, Nganjimi PC, Soltan A, Schwab P, Branson K, Clifton DA. Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare. NPJ Digit Med 2024; 7:283. [PMID: 39414980 PMCID: PMC11484763 DOI: 10.1038/s41746-024-01272-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 09/26/2024] [Indexed: 10/18/2024] Open
Abstract
Robust data privacy regulations hinder the exchange of healthcare data among institutions, crucial for global insights and developing generalised clinical models. Federated learning (FL) is ideal for training global models using datasets from different institutions without compromising privacy. However, disparities in electronic healthcare records (EHRs) lead to inconsistencies in ML-ready data views, making FL challenging without extensive preprocessing and information loss. These differences arise from variations in services, care standards, and record-keeping practices. This paper addresses data view heterogeneity by introducing a knowledge abstraction and filtering-based FL framework that allows FL over heterogeneous data views without manual alignment or information loss. The knowledge abstraction and filtering mechanism maps raw input representations to a unified, semantically rich shared space for effective global model training. Experiments on three healthcare datasets demonstrate the framework's effectiveness in overcoming data view heterogeneity and facilitating information sharing in a federated setup.
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Affiliation(s)
- Anshul Thakur
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Soheila Molaei
- Department of Engineering Science, University of Oxford, Oxford, UK
| | | | - Andrew Soltan
- Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | | | | | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, UK
- Oxford-Suzhou Centre for Advanced Research, Suzhou, China
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Oberprieler NG, Pladevall-Vila M, Johannes C, Layton JB, Golozar A, Lavallee M, Liu F, Kubin M, Vizcaya D. FOUNTAIN: a modular research platform for integrated real-world evidence generation. BMC Med Res Methodol 2024; 24:224. [PMID: 39354358 PMCID: PMC11445988 DOI: 10.1186/s12874-024-02344-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 09/17/2024] [Indexed: 10/03/2024] Open
Abstract
BACKGROUND Real-world evidence (RWE) plays a key role in regulatory and healthcare decision-making, but the potentially fragmentated nature of generated evidence may limit its utility for clinical decision-making. Heterogeneity and a lack of reproducibility in RWE resulting from inconsistent application of methodologies across data sources should be minimized through harmonization. METHODS This paper's aim is to describe and reflect upon a multidisciplinary research platform (FOUNTAIN; FinerenOne mUlti-database NeTwork for evidence generAtIoN) with coordinated studies using diverse RWE generation approaches and explore the platform's strengths and limitations. With guidance from an executive advisory committee of multidisciplinary experts and patient representatives, the goal of the FOUNTAIN platform is to harmonize RWE generation across a portfolio of research projects, including research partner collaborations and a common data model (CDM)-based program. FOUNTAIN's overarching objectives as a research platform are to establish long-term collaborations among pharmacoepidemiology research partners and experts and to integrate diverse approaches for RWE generation, including global protocol execution by research partners in local data sources and common protocol execution in multiple data sources through federated data networks, while ensuring harmonization of medical definitions, methodology, and reproducible artifacts across all studies. Specifically, the aim of the multiple studies run within the frame of FOUNTAIN is to provide insight into the real-world utilization, effectiveness, and safety of finerenone across its life-cycle. RESULTS Currently, the FOUNTAIN platform includes 9 research partner collaborations and 8 CDM-mapped data sources from 7 countries (United States, United Kingdom, China, Japan, The Netherlands, Spain, and Denmark). These databases and research partners were selected after a feasibility fit-for-purpose evaluation. Six multicountry, multidatabase, cohort studies are ongoing to describe patient populations, current standard of care, comorbidity profiles, healthcare resource use, and treatment effectiveness and safety in different patient populations with chronic kidney disease and type 2 diabetes. Strengths and potential limitations of FOUNTAIN are described in the context of valid RWE generation. CONCLUSION The establishment of the FOUNTAIN platform has allowed harmonized execution of multiple studies, promoting consistency both within individual studies that employ multiple data sources and across all studies run within the platform's framework. FOUNTAIN presents a proposal to efficiently improve the consistency and generalizability of RWE on finerenone.
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Affiliation(s)
| | - Manel Pladevall-Vila
- RTI Health Solutions, Barcelona, Spain
- The Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, MI, USA
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Desai RJ, Marsolo K, Smith J, Carrell D, Penfold R, Pillai HS, Lii J, Ngan K, Winter R, Adgent M, Ramaprasan A, Driscoll MR, Scarnecchia D, Kiernan D, Draper C, Lyons JG, Khurshid A, Maro JC, Zimmerman R, Brown J, Bright P, Hernández-Muñoz JJ, Matheny ME, Schneeweiss S. The FDA Sentinel Real World Evidence Data Enterprise (RWE-DE). Pharmacoepidemiol Drug Saf 2024; 33:e70028. [PMID: 39385712 DOI: 10.1002/pds.70028] [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: 09/06/2024] [Accepted: 09/16/2024] [Indexed: 10/12/2024]
Abstract
PURPOSE The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making. METHODS In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives. RESULTS We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed. CONCLUSIONS Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system.
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Affiliation(s)
- Rishi J Desai
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Keith Marsolo
- Department of Population Health Sciences, Duke University, Durham, North Carolina, USA
| | - Joshua Smith
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - David Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington State, USA
| | - Robert Penfold
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington State, USA
| | - Haritha S Pillai
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Joyce Lii
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Kerry Ngan
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Robert Winter
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Margaret Adgent
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Arvind Ramaprasan
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington State, USA
| | - Meighan Rogers Driscoll
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel Scarnecchia
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Daniel Kiernan
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Christine Draper
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Jennifer G Lyons
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Anjum Khurshid
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | | | | | - Patricia Bright
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, Maryland, USA
| | - José J Hernández-Muñoz
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, Maryland, USA
| | - Michael E Matheny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Geriatrics Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA, Nashville, Tennessee, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Locatelli A, Bellante N, Donatiello G, Fortinguerra F, Belleudi V, Poggi FR, Perna S, Trotta F. Antihypertensive therapy during pregnancy: the prescription pattern in Italy. Front Pharmacol 2024; 15:1370797. [PMID: 39281270 PMCID: PMC11393683 DOI: 10.3389/fphar.2024.1370797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 08/12/2024] [Indexed: 09/18/2024] Open
Abstract
Drug use during pregnancy should be evidence-based and favor the safest and most appropriate prescription. The Italian Medicines Agency (AIFA) coordinates a network focusing on monitoring medication use in pregnancy. Hypertensive disorders are common medical complication of pregnancy and antihypertensive therapy is prescribed to reduce the risk of adverse feto-maternal complications. The objective of this study is to highlight the prescription pattern of antihypertensive drugs before pregnancy, during pregnancy and in the postpartum period in Italy and to evaluate their use with a specific attention to the prescription pattern of drugs considered safe during pregnancy. A multi-database cross-sectional population study using a Common Data Model (CDM) was performed. We selected all women aged 15-49 years living in eight Italian regions who gave birth in hospital between 1 April 2016 and 31 March 2018. In a cohort of 449.012 women, corresponding to 59% of Italian deliveries occurred in the study period, the prevalence of prescription of antihypertensive drugs in the pre-conceptional period was 1.2%, in pregnancy 2.0% and in the postpartum period 2.9%. Beta-blockers were the most prescribed drugs before pregnancy (0.28%-0.30%). Calcium channel blockers were the most prescribed drugs during pregnancy, with a prevalence of 0.23%, 0.33%, 0.75% in each trimester. Alfa-2-adrenergic receptor agonists were the second most prescribed during pregnancy with a prevalence of 0.16%, 0.26% and 0.55% in each trimester. The prescription of drugs contraindicated during pregnancy was below 0.5%. Only a small percentage of women switched from a contraindicated drug to a drug compatible with pregnancy. The analysis showed little variability between the different Italian regions. In general, the prescription of antihypertensive drugs in the Italian Mom-Network is coherent with the drugs compatible with pregnancy.
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Affiliation(s)
- Anna Locatelli
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | - Nicolò Bellante
- School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy
| | | | | | - Valeria Belleudi
- Departement of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Lazio, Italy
| | - Francesca R Poggi
- Departement of Epidemiology, Lazio Regional Health Service, ASL Roma 1, Rome, Lazio, Italy
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Gressler LE, Marinac-Dabic D, Resnic FS, Williams S, Yang K, Weichold F, Avila-Tang E, Mack C, Coplan P, Panagiotou OA, Pappas G. A Comprehensive Framework for Evaluating the Value Created by Real-World Evidence for Diverse Stakeholders: The Case for Coordinated Registry Networks. Ther Innov Regul Sci 2024:10.1007/s43441-024-00680-z. [PMID: 39060838 DOI: 10.1007/s43441-024-00680-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 07/18/2024] [Indexed: 07/28/2024]
Abstract
OBJECTIVES This manuscript presents a comprehensive framework for the assessment of the value of real-world evidence (RWE) in healthcare decision-making. While RWE has been proposed to overcome some limitations of traditional, one-off studies, no systematic framework exists to measure if RWE actually lowers the burden. This framework aims to fill that gap by providing conceptual approaches for evaluating the time and cost efficiencies of RWE, thus guiding strategic investments in RWE infrastructure. METHODS The framework consists of four components: (114th Congress. 21st Century Cures Act.; 2015. https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf .) identification of stakeholders using and producing RWE, (National Health Council. Glossary of Patient Engagement Terms. Published 2019. Accessed May 18. 2021. https://nationalhealthcouncil.org/glossary-of-patient-engagement-terms/ .) understanding value propositions on how RWE can benefit stakeholders, (Center for Drug Evaluation and Research. CDER Patient-Focused Drug Development. U.S. Food & Drug Administration.) defining key performance indicators (KPIs), and (U.S. Department of Health and Human Services - Food and Drug Administration: Center for Devices and Radiological Health and Center for Biologics Evaluation and Research. Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices - Guidance for Industry and Food and Drug Administration Staff. 2017. http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInformation/Guida .) establishing metrics and case studies to assess value. KPIs are categorized as 'better, faster, or cheaper" as an indicator of value: better focusing on high-quality actionable evidence; 'faster,' denoting time-saving in evidence generation, and 'cheaper,' emphasizing cost-efficiency decision compared to methodologies that do not involve data routinely collected in clinical practice. Metrics and relevant case studies are tailored based on stakeholder value propositions and selected KPIs that can be used to assess what value has been created by using RWE compared to traditional evidence-generation approaches and comparing different RWE sources. RESULTS Operationalized through metrics and case studies drawn from the literature, the value of RWE is documented as improving treatment effect heterogeneity evaluation, expanding medical product labels, and expediting post-market compliance. RWE is also shown to reduce the cost and time required to produce evidence compared to traditional one-off approaches. An original example of a metric that measures the time saved by RWE methods to detect a signal of a product failure was presented based on analysis of the National Cardiovascular Disease Registry. CONCLUSIONS The framework presented in this manuscript offers a comprehensive approach for evaluating the value of RWE, applicable to all stakeholders engaged in leveraging RWE for healthcare decision-making. Through the proposed metrics and illustrated case studies, valuable insights are provided into the heightened efficiency, cost-effectiveness, and improved decision-making within clinical and regulatory domains facilitated by RWE. While this framework is primarily focused on medical devices, it could potentially inform the determination of RWE value in other medical products. By discerning the variations in cost, time, and data utility among various evidence-generation methods, stakeholders are empowered to invest strategically in RWE infrastructure and shape future research endeavors.
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Rai A, Maro JC, Dutcher S, Bright P, Toh S. Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment. Pharmacoepidemiol Drug Saf 2024; 33:e5820. [PMID: 38783407 DOI: 10.1002/pds.5820] [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: 02/15/2024] [Revised: 05/06/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources. METHODS We present a stepwise schematic for Sentinel's data harmonization, data quality check, query design and implementation, and reporting practices, and describe approaches to enhancing the transparency, reproducibility, and replicability of studies at each step. CONCLUSIONS Each Sentinel data partner converts its source data into the Sentinel Common Data Model. The transformed data undergoes rigorous quality checks before it can be used for Sentinel queries. The Sentinel Common Data Model framework, data transformation codes for several data sources, and data quality assurance packages are publicly available. Designed to run against the Sentinel Common Data Model, Sentinel's querying system comprises a suite of pre-tested, parametrizable computer programs that allow users to perform sophisticated descriptive and inferential analysis without having to exchange individual-level data across sites. Detailed documentation of capabilities of the programs as well as the codes and information required to execute them are publicly available on the Sentinel website. Sentinel also provides public trainings and online resources to facilitate use of its data model and querying system. Its study specifications conform to established reporting frameworks aimed at facilitating reproducibility and replicability of real-world data studies. Reports from Sentinel queries and associated design and analytic specifications are available for download on the Sentinel website. Sentinel is an example of how real-world data can be used to generate regulatory-grade evidence at scale using a transparent, reproducible, and replicable process.
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Affiliation(s)
- Ashish Rai
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
| | - Sarah Dutcher
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Patricia Bright
- U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA
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Qouhafa M, Benaji B, Lebbar S, Soulaymani A, Nsiri B, El Yousfi Alaoui MH, Abdelrhani M, Azougagh M. Metric identification and mapping of reimbursable implant codes in Morocco to the global nomenclature (GMDN) and European (EUDAMED) of medical devices. ANNALES PHARMACEUTIQUES FRANÇAISES 2024:S0003-4509(24)00088-9. [PMID: 38821481 DOI: 10.1016/j.pharma.2024.05.005] [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: 03/25/2024] [Accepted: 05/27/2024] [Indexed: 06/02/2024]
Abstract
BACKGROUND The list of medical devices (MD) eligible for reimbursement under the Compulsory Health Insurance in Morocco is set by Ministerial Order comprising 869 items between life-support equipment, external prostheses, and implants. The objective of the present study is to analyze the nomenclature of implantable medical devices (IMD) appearing on this list and compare them with the global nomenclature of MD (GMDN) and the European nomenclature of MD (EMDN). METHODS The study deals with (i) the mapping of the codes of the IMD list with 170 DM per cardinality and (ii) a metric identification by Sørensen-Dice coefficient of terminological similarity of the IMD with respect to the two databases. RESULTS The 170 IMD codes are mapped onto 493 terms in the GMDN and 344 terms in the EMDN. The 37.7% of implants are mapped to more than or equal to 2 terms of GMDN while 36.5% are mapped to more than or equal to 2 terms to the EMDN. The comparison of cardinality distributions has revealed no significant difference (P=0.430) between the two databases. The implants examined are divided into 11 categories whose strong similarity is given to active cardiovascular implants in the EMDN database with simDice=0.534. CONCLUSION Healthcare authorities need to align with nomenclature standards to improve interoperability and rely on a more efficient and rational regulatory process.
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Affiliation(s)
- Meryem Qouhafa
- Laboratoire biologie et santé, faculté des sciences, université Ibn Tofail, Kénitra, Morocco; Groupe de recherche d'ingénierie biomédicale et pharmaceutique, département d'ingénierie des technologies de la santé-(ENSAM Rabat), Mohammed V University Rabat, Rabat, Morocco; Higher Institute of Nursing and Technical Health Professions Rabat - Ministry of Health and Social Welfare, Rabat, Morocco.
| | - Brahim Benaji
- Groupe de recherche d'ingénierie biomédicale et pharmaceutique, département d'ingénierie des technologies de la santé-(ENSAM Rabat), Mohammed V University Rabat, Rabat, Morocco
| | - Souad Lebbar
- Laboratoire biologie et santé, faculté des sciences, université Ibn Tofail, Kénitra, Morocco
| | - Abdelmajid Soulaymani
- Laboratoire biologie et santé, faculté des sciences, université Ibn Tofail, Kénitra, Morocco
| | - Benayad Nsiri
- Groupe de recherche d'ingénierie biomédicale et pharmaceutique, département d'ingénierie des technologies de la santé-(ENSAM Rabat), Mohammed V University Rabat, Rabat, Morocco
| | - My Hachem El Yousfi Alaoui
- Groupe de recherche d'ingénierie biomédicale et pharmaceutique, département d'ingénierie des technologies de la santé-(ENSAM Rabat), Mohammed V University Rabat, Rabat, Morocco
| | - Mokhtari Abdelrhani
- Laboratoire biologie et santé, faculté des sciences, université Ibn Tofail, Kénitra, Morocco
| | - Mohammed Azougagh
- Groupe de recherche d'ingénierie biomédicale et pharmaceutique, département d'ingénierie des technologies de la santé-(ENSAM Rabat), Mohammed V University Rabat, Rabat, Morocco
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Sim JA, Huang X, Horan MR, Baker JN, Huang IC. Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:467-475. [PMID: 38383308 PMCID: PMC11001514 DOI: 10.1080/14737167.2024.2322664] [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: 09/02/2023] [Accepted: 02/20/2024] [Indexed: 02/23/2024]
Abstract
INTRODUCTION Patient-reported outcomes (PROs; symptoms, functional status, quality-of-life) expressed in the 'free-text' or 'unstructured' format within clinical notes from electronic health records (EHRs) offer valuable insights beyond biological and clinical data for medical decision-making. However, a comprehensive assessment of utilizing natural language processing (NLP) coupled with machine learning (ML) methods to analyze unstructured PROs and their clinical implementation for individuals affected by cancer remains lacking. AREAS COVERED This study aimed to systematically review published studies that used NLP techniques to extract and analyze PROs in clinical narratives from EHRs for cancer populations. We examined the types of NLP (with and without ML) techniques and platforms for data processing, analysis, and clinical applications. EXPERT OPINION Utilizing NLP methods offers a valuable approach for processing and analyzing unstructured PROs among cancer patients and survivors. These techniques encompass a broad range of applications, such as extracting or recognizing PROs, categorizing, characterizing, or grouping PROs, predicting or stratifying risk for unfavorable clinical results, and evaluating connections between PROs and adverse clinical outcomes. The employment of NLP techniques is advantageous in converting substantial volumes of unstructured PRO data within EHRs into practical clinical utilities for individuals with cancer.
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Affiliation(s)
- Jin-ah Sim
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, USA
- Department of AI Convergence, Hallym University, Chuncheon, Republic of Korea
| | - Xiaolei Huang
- Department of Computer Science, University of Memphis, Memphis, Tennessee, United States
| | - Madeline R. Horan
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Justin N. Baker
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - I-Chan Huang
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, USA
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Locatelli A, Ornaghi S, Terzaghi A, Belleudi V, Fortinguerra F, Poggi FR, Perna S, Trotta F. Antidiabetic Therapy during Pregnancy: The Prescription Pattern in Italy. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:7139. [PMID: 38063570 PMCID: PMC10706431 DOI: 10.3390/ijerph20237139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Revised: 11/26/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023]
Abstract
Pregestational and gestational diabetes mellitus are relevant complications of pregnancy, and antidiabetic drugs are prescribed to obtain glycemic control and improve perinatal outcomes. The objective of this study was to describe the prescription pattern of antidiabetics before, during and after pregnancy in Italy and to evaluate its concordance with the Italian guideline on treatment of diabetes mellitus. A multi-database cross-sectional population study using a Common Data Model was performed. In a cohort of about 450,000 women, the prescribing profile of antidiabetics seemed to be in line with the Italian guideline, which currently does not recommend the use of oral antidiabetics and non-insulin injection, even if practice is still heterogeneous (up to 3.8% in the third trimester used oral antidiabetics). A substantial variability in the prescription pattern was observed among the Italian regions considered: the highest increase was registered in Tuscany (4.2%) while the lowest was in Lombardy (1.5%). Women with multiple births had a higher proportion of antidiabetic prescriptions than women with singleton births both in the preconception period and during pregnancy (1.3% vs. 0.7%; 3.4% vs. 2.6%) and used metformin more frequently. The consumption of antidiabetics in foreign women was higher than Italians (second trimester: 1.8% vs. 0.9%, third trimester: 3.6% vs. 1.8%).
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Affiliation(s)
- Anna Locatelli
- School of Medicine and Surgery, University of Milano-Bicocca, Via Pergolesi 33, 20900 Monza, Italy; (A.L.); (A.T.)
| | - Sara Ornaghi
- School of Medicine and Surgery, University of Milano-Bicocca, Via Pergolesi 33, 20900 Monza, Italy; (A.L.); (A.T.)
| | - Alessandra Terzaghi
- School of Medicine and Surgery, University of Milano-Bicocca, Via Pergolesi 33, 20900 Monza, Italy; (A.L.); (A.T.)
| | - Valeria Belleudi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, 00147 Rome, Italy; (V.B.); (F.R.P.)
| | | | - Francesca Romana Poggi
- Department of Epidemiology, Lazio Regional Health Service, ASL Roma 1, 00147 Rome, Italy; (V.B.); (F.R.P.)
| | - Serena Perna
- Italian Medicines Agency (AIFA), 00187 Rome, Italy; (F.F.); (S.P.); (F.T.)
| | - Francesco Trotta
- Italian Medicines Agency (AIFA), 00187 Rome, Italy; (F.F.); (S.P.); (F.T.)
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Webster-Clark M, Toh S, Arnold J, McTigue KM, Carton T, Platt R. External validity in distributed data networks. Pharmacoepidemiol Drug Saf 2023; 32:1360-1367. [PMID: 37463756 DOI: 10.1002/pds.5666] [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: 12/05/2022] [Revised: 05/20/2023] [Accepted: 07/04/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE While much has been written about how distributed networks address internal validity, external validity is rarely discussed. We aimed to define key terms related to external validity, discuss how they relate to distributed networks, and identify how three networks (the US Food and Drug Administration's Sentinel System, the Canadian Network for Observational Drug Effect Studies [CNODES], and the National Patient Centered Clinical Research Network [PCORnet]) deal with external validity. METHODS We define external validity, target populations, target validity, generalizability, and transportability and describe how each relates to distributed networks. We then describe Sentinel, CNODES, and PCORnet and how each approaches these concepts, including a sample case study. RESULTS Each network approaches external validity differently. As its target population is US citizens and it includes only US data, Sentinel primarily worries about lack of external validity by not including some segments of the population. The fact that CNODES includes Canadian, United States, and United Kingdom data forces them to seriously consider whether the United States and United Kingdom data will be transportable to Canadian citizens when they meta-analyze database-specific estimates. PCORnet, with its focus on study-specific cohorts and pragmatic trials, conducts more case-by-case explorations of external validity for each new analytic data set it generates. CONCLUSIONS There is no one-size-fits-all approach to external validity within distributed networks. With these networks and comparisons between their findings becoming a key part of pharmacoepidemiology, there is a need to adapt tools for improving external validity to the distributed network setting.
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Affiliation(s)
- Michael Webster-Clark
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Gillings Schools of Global Public Health, UNC Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Jonathan Arnold
- Department of Medicine, University of Pittsburg, Pittsburgh, Pennsylvania, USA
| | - Kathleen M McTigue
- Department of Medicine, University of Pittsburg, Pittsburgh, Pennsylvania, USA
| | - Thomas Carton
- Division of Health Services Research, Louisiana Public Health Institute, New Orleans, Louisiana, USA
| | - Robert Platt
- Department of Epidemiology and Biostatistics, McGill University, Montreal, Quebec, Canada
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11
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Sim JA, Huang X, Horan MR, Stewart CM, Robison LL, Hudson MM, Baker JN, Huang IC. Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review. Artif Intell Med 2023; 146:102701. [PMID: 38042599 PMCID: PMC10693655 DOI: 10.1016/j.artmed.2023.102701] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 09/30/2023] [Accepted: 10/29/2023] [Indexed: 12/04/2023]
Abstract
OBJECTIVE Natural language processing (NLP) combined with machine learning (ML) techniques are increasingly used to process unstructured/free-text patient-reported outcome (PRO) data available in electronic health records (EHRs). This systematic review summarizes the literature reporting NLP/ML systems/toolkits for analyzing PROs in clinical narratives of EHRs and discusses the future directions for the application of this modality in clinical care. METHODS We searched PubMed, Scopus, and Web of Science for studies written in English between 1/1/2000 and 12/31/2020. Seventy-nine studies meeting the eligibility criteria were included. We abstracted and summarized information related to the study purpose, patient population, type/source/amount of unstructured PRO data, linguistic features, and NLP systems/toolkits for processing unstructured PROs in EHRs. RESULTS Most of the studies used NLP/ML techniques to extract PROs from clinical narratives (n = 74) and mapped the extracted PROs into specific PRO domains for phenotyping or clustering purposes (n = 26). Some studies used NLP/ML to process PROs for predicting disease progression or onset of adverse events (n = 22) or developing/validating NLP/ML pipelines for analyzing unstructured PROs (n = 19). Studies used different linguistic features, including lexical, syntactic, semantic, and contextual features, to process unstructured PROs. Among the 25 NLP systems/toolkits we identified, 15 used rule-based NLP, 6 used hybrid NLP, and 4 used non-neural ML algorithms embedded in NLP. CONCLUSIONS This study supports the potential utility of different NLP/ML techniques in processing unstructured PROs available in EHRs for clinical care. Though using annotation rules for NLP/ML to analyze unstructured PROs is dominant, deploying novel neural ML-based methods is warranted.
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Affiliation(s)
- Jin-Ah Sim
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; School of AI Convergence, Hallym University, Chuncheon, Republic of Korea
| | - Xiaolei Huang
- Department of Computer Science, University of Memphis, Memphis, TN, United States
| | - Madeline R Horan
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Christopher M Stewart
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States
| | - Leslie L Robison
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Melissa M Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States; Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - Justin N Baker
- Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - I-Chan Huang
- Department of Epidemiology and Cancer Control, St. Jude Children's Research Hospital, Memphis, TN, United States.
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12
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Dhingra LS, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. Am J Cardiol 2023; 203:136-148. [PMID: 37499593 PMCID: PMC10865722 DOI: 10.1016/j.amjcard.2023.06.104] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 05/24/2023] [Accepted: 06/29/2023] [Indexed: 07/29/2023]
Abstract
The electronic health record (EHR) represents a rich source of patient information, increasingly being leveraged for cardiovascular research. Although its primary use remains the seamless delivery of health care, the various longitudinally aggregated structured and unstructured data elements for each patient within the EHR can define the computational phenotypes of disease and care signatures and their association with outcomes. Although structured data elements, such as demographic characteristics, laboratory measurements, problem lists, and medications, are easily extracted, unstructured data are underused. The latter include free text in clinical narratives, documentation of procedures, and reports of imaging and pathology. Rapid scaling up of data storage and rapid innovation in natural language processing and computer vision can power insights from unstructured data streams. However, despite an array of opportunities for research using the EHR, specific expertise is necessary to adequately address confidentiality, accuracy, completeness, and heterogeneity challenges in EHR-based research. These often require methodological innovation and best practices to design and conduct successful research studies. Our review discusses these challenges and their proposed solutions. In addition, we highlight the ongoing innovations in federated learning in the EHR through a greater focus on common data models and discuss ongoing work that defines such an approach to large-scale, multicenter, federated studies. Such parallel improvements in technology and research methods enable innovative care and optimization of patient outcomes.
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Affiliation(s)
| | - Miles Shen
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Internal Medicine
| | - Anjali Mangla
- Section of Cardiovascular Medicine, Department of Internal Medicine; Department of Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, Connecticut; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut.
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13
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Song I, Kim M, Choi H, Kim JH, Lim KH, Yoon HS, Rah YC, Park E, Im GJ, Song JJ, Chae SW, Choi J. Hydrophilic and lipophilic statin use and risk of hearing loss in hyperlipidemia using a Common Data Model: multicenter cohort study. Sci Rep 2023; 13:12373. [PMID: 37524760 PMCID: PMC10390480 DOI: 10.1038/s41598-023-39316-x] [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: 03/02/2023] [Accepted: 07/23/2023] [Indexed: 08/02/2023] Open
Abstract
Hearing impairment, the third largest health burden worldwide, currently lacks definitive treatments or preventive drugs. This study compared the effects of hydrophilic and lipophilic statin on hearing loss using a common database model. This retrospective multicenter study was conducted in three hospitals in South Korea (Anam, Guro, Ansan). We enrolled patients with hyperlipidemia with an initial hearing loss diagnosis. Data were collected during January 1, 2022-December 31, 2021 using the Observational Health Data Science and Informatics open-source software and Common Data Model database. The primary outcome was the occurrence of first-time hearing loss following a hyperlipidemia diagnosis, as documented in the Common Data Model cohort database. The measures of interest were hearing loss risk between hydrophilic and lipophilic statin use. Variables were compared using propensity score matching, Cox proportional regression, and meta-analysis. Among 37,322 patients with hyperlipidemia, 13,751 (7669 men and 6082 women) and 23,631 (11,390 men and 12,241 women) were treated with hydrophilic and lipophilic statins, respectively. After propensity score matching, according to the Kaplan-Meier curve, hearing loss risk did not significantly differ among the hospitals. The hazard ratio (HR) of the male patients from Anam (0.29, [95% confidence interval (CI), 0.05-1.51]), Guro (HR, 0.56, [95% CI 0.18-1.71]), and Ansan (hazard ratio, 0.29, [95% CI 0.05-1.51]) hospitals were analyzed using Cox proportional regression. Overall effect size (HR, 0.40, [95% CI 0.18-0.91]) was estimated using meta-analysis, which indicated that hearing loss risk among hydrophilic statin users was less than that among lipophilic statin users and was statistically significant. Men in the hydrophilic statin group had a lower risk of hearing impairment than those in the lipophilic statin group.
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Affiliation(s)
- Insik Song
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea
| | - Minjin Kim
- Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea
- Biomedical Research Center, Korea University Ansan Hospital, Ansan, Republic of Korea
| | - Hangseok Choi
- Department of Biostatistics, Korea University College of Medicine, Seoul, Republic of Korea
- Biomedical Research Center, Korea University Ansan Hospital, Ansan, Republic of Korea
| | - Jeong Hwan Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea
| | - Kang Hyeon Lim
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea
| | - Hee Soo Yoon
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea
| | - Yoon Chan Rah
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea
| | - Euyhyun Park
- Department of Otorhinolaryngology-Head and Neck Surgery, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Gi Jung Im
- Department of Otorhinolaryngology-Head and Neck Surgery, Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jae-Jun Song
- Department of Otorhinolaryngology-Head and Neck Surgery, Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sung-Won Chae
- Department of Otorhinolaryngology-Head and Neck Surgery, Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - June Choi
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan, Republic of Korea.
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
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14
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Kim M, Park DH, Choi H, Song I, Lim KH, Yoon HS, Rah YC, Choi J. A Multicenter Cohort Study on the Association between Metformin Use and Hearing Loss in Patients with Type 2 Diabetes Mellitus Using a Common Data Model. J Clin Med 2023; 12:jcm12093145. [PMID: 37176586 PMCID: PMC10179543 DOI: 10.3390/jcm12093145] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
We attempted to explore the association between metformin use and hearing loss in in a large-scale study. This retrospective multicenter cohort study assessed the data of patients with type 2 diabetes mellitus (DM) aged over 40 years using the Observational Health Data Science and Informatics open-source software and the Common Data Model database from 1 January 2002 to 31 December 2019. Each participant was selected using the ICD-10-CM diagnosis code E11 for type 2 DM with sensorineural hearing loss. The participants were divided into metformin and non-metformin users. The outcome measure was the first occurrence of hearing loss after the diagnosis of DM as measured by the CDM cohort study. A total of 80,596 patients, including 46,152 metformin users and 34,444 non-metformin users from three hospitals were assessed. After calibration, we compared the risk of hearing loss using Kaplan-Meier curves, and found significant differences between the groups. The calibrated hazard ratio in the three hospitals (0.79 [95% confidence interval, 0.57-1.12]) was summarized. These findings suggest that the probability of hearing loss-free survival in the metformin user group is higher than that in the non-metformin user group.
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Affiliation(s)
- Minjin Kim
- Department of Biostatistics, Korea University College of Medicine, Seoul 02842, Republic of Korea
- Medical Science Research Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
| | - Dong Heun Park
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | - Hangseok Choi
- Department of Biostatistics, Korea University College of Medicine, Seoul 02842, Republic of Korea
- Medical Science Research Center, Korea University Ansan Hospital, Ansan 15355, Republic of Korea
| | - Insik Song
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | - Kang Hyeon Lim
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | - Hee Soo Yoon
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | - Yoon Chan Rah
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan 15355, Republic of Korea
| | - June Choi
- Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Ansan 15355, Republic of Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul 02842, Republic of Korea
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15
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Hlavaty A, Roustit M, Montani D, Chaumais M, Guignabert C, Humbert M, Cracowski J, Khouri C. Identifying new drugs associated with pulmonary arterial hypertension: A WHO pharmacovigilance database disproportionality analysis. Br J Clin Pharmacol 2022; 88:5227-5237. [PMID: 35679331 PMCID: PMC9795981 DOI: 10.1111/bcp.15436] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 04/11/2022] [Accepted: 05/29/2022] [Indexed: 12/30/2022] Open
Abstract
Since the 1960s, several drugs have been linked to the onset or aggravation of pulmonary arterial hypertension (PAH): dasatinib, some amphetamine-like appetite suppressants (aminorex, fenfluramine, dexfenfluramine, benfluorex) and recreational drugs (methamphetamine). Moreover, in numerous cases, the implication of other drugs with PAH have been suggested, but the precise identification of iatrogenic aetiologies of PAH is challenging given the scarcity of this disease and the potential long latency period between drug intake and PAH onset. In this context, we used the World Health Organization's pharmacovigilance database, VigiBase, to generate new hypotheses about drug associated PAH. METHODS We used VigiBase, the largest pharmacovigilance database worldwide to generate disproportionality signals through the Bayesian neural network method. All disproportionality signals were further independently reviewed by experts in pulmonary arterial hypertension, pharmacovigilance and vascular pharmacology and their plausibility ranked according to World Health Organization causality categories. RESULTS We included 2184 idiopathic PAH cases, yielding a total of 93 disproportionality signals. Among them, 25 signals were considered very likely, 15 probable, 28 possible and 25 unlikely. Notably, we identified 4 new protein kinases inhibitors (lapatinib, lorlatinib, ponatinib and ruxolitinib), 1 angiogenesis inhibitor (bevacizumab), and several chemotherapeutics (etoposide, trastuzumab), antimetabolites (cytarabine, fludarabine, fluorouracil, gemcitabine) and immunosuppressants (leflunomide, thalidomide, ciclosporin). CONCLUSION Such signals represent plausible adverse drug reactions considering the knowledge of iatrogenic PAH, the drugs' biological and pharmacological activity and the characteristics of the reported case. Although confirmatory studies need to be performed, the signals identified may help clinicians envisage an iatrogenic aetiology when faced with a patient who develops PAH.
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Affiliation(s)
- Alex Hlavaty
- Pharmacovigilance UnitGrenoble Alpes University HospitalGrenobleFrance
| | - Matthieu Roustit
- Clinical Pharmacology Department INSERM CIC1406Grenoble Alpes University HospitalGrenobleFrance,HP2 Laboratory, Inserm U1300Grenoble Alpes University ‐ GrenobleFrance
| | - David Montani
- INSERM UMR_S 999 «Pulmonary Hypertension: Pathophysiology and Novel Therapies», Hôpital Marie LannelongueLe Plessis‐RobinsonFrance,Faculté de MédecineUniversité Paris‐SaclayLe Kremlin‐BicêtreFrance,Assistance Publique ‐ Hôpitaux de Paris (AP‐HP), Service de Pneumologie, Centre de référence Maladie Rares de l'Hypertension PulmonaireHôpital BicêtreLe Kremlin‐BicêtreFrance
| | - Marie‐Camille Chaumais
- INSERM UMR_S 999 «Pulmonary Hypertension: Pathophysiology and Novel Therapies», Hôpital Marie LannelongueLe Plessis‐RobinsonFrance,Faculté de PharmacieUniversité Paris‐SaclayChâtenay MalabryFrance,Assistance Publique ‐ Hôpitaux de Paris (AP‐HP), Service de PharmacieHôpital BicêtreLe Kremlin‐BicêtreFrance
| | - Christophe Guignabert
- INSERM UMR_S 999 «Pulmonary Hypertension: Pathophysiology and Novel Therapies», Hôpital Marie LannelongueLe Plessis‐RobinsonFrance,Faculté de MédecineUniversité Paris‐SaclayLe Kremlin‐BicêtreFrance
| | - Marc Humbert
- INSERM UMR_S 999 «Pulmonary Hypertension: Pathophysiology and Novel Therapies», Hôpital Marie LannelongueLe Plessis‐RobinsonFrance,Faculté de MédecineUniversité Paris‐SaclayLe Kremlin‐BicêtreFrance,Assistance Publique ‐ Hôpitaux de Paris (AP‐HP), Service de Pneumologie, Centre de référence Maladie Rares de l'Hypertension PulmonaireHôpital BicêtreLe Kremlin‐BicêtreFrance
| | - Jean‐Luc Cracowski
- Pharmacovigilance UnitGrenoble Alpes University HospitalGrenobleFrance,HP2 Laboratory, Inserm U1300Grenoble Alpes University ‐ GrenobleFrance
| | - Charles Khouri
- Pharmacovigilance UnitGrenoble Alpes University HospitalGrenobleFrance,Clinical Pharmacology Department INSERM CIC1406Grenoble Alpes University HospitalGrenobleFrance,HP2 Laboratory, Inserm U1300Grenoble Alpes University ‐ GrenobleFrance
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16
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Zhao X, Iqbal S, Valdes IL, Dresser M, Girish S. Integrating real-world data to accelerate and guide drug development: A clinical pharmacology perspective. Clin Transl Sci 2022; 15:2293-2302. [PMID: 35912537 PMCID: PMC9579393 DOI: 10.1111/cts.13379] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/01/2022] [Accepted: 07/15/2022] [Indexed: 01/25/2023] Open
Abstract
Pharmaceutical products in the current accelerated drug development landscape can benefit from tools beyond data generated from randomized control trials. We have seen an abundance of real-world data (RWD) and real-world evidence, driven by the digitalization of healthcare systems and an increased awareness that has inspired a heightened interest in their potential use. Literature review suggest leveraging RWD as a promising tool to answer key questions in the areas of clinical pharmacology and translational science. RWD may increase our understanding regarding the impact of intrinsic (e.g., liver, renal impairment, or genetic polymorphisms) and extrinsic (e.g., food consumption or concomitant medications) factors on the clearance of administered drugs. Changes in clearance may lead to clinically relevant changes in drug exposure that may require clinical management strategies, such as change in dose or dosing regimen. RWD can be leveraged to potentially bridge the gaps among research, development, and clinical care. This paper highlights promising areas of how RWD have been used to complement clinical pharmacology throughout various phases of drug development; case examples will include dose/regimen extrapolation, dose adjustments for special populations (organ impairment, pediatrics, etc.), and pharmacokinetic/pharmacodynamic models to assess impact of prognostic factors on outcomes. In addition, this paper will also juxtapose limitations and promises of utilizing RWD to answer key scientific questions in drug development and articulate challenges posed by quality issues, data availability, and integration from various sources as well as the increased need for multidimensional-omics data that can better guide the development of personalized and predictive medicine.
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Affiliation(s)
- Xiaochen Zhao
- Department of Clinical PharmacologyGilead Sciences, Inc.Foster CityCaliforniaUSA
| | - Shahed Iqbal
- Department of Clinical PharmacologyGilead Sciences, Inc.Foster CityCaliforniaUSA
| | - Ivelisse L. Valdes
- Department of Clinical PharmacologyGilead Sciences, Inc.Foster CityCaliforniaUSA
| | - Mark Dresser
- Department of Clinical PharmacologyGilead Sciences, Inc.Foster CityCaliforniaUSA
| | - Sandhya Girish
- Department of Clinical PharmacologyGilead Sciences, Inc.Foster CityCaliforniaUSA
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17
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Kumar S, Arnold M, James G, Padman R. Developing a common data model approach for DISCOVER CKD: A retrospective, global cohort of real-world patients with chronic kidney disease. PLoS One 2022; 17:e0274131. [PMID: 36173958 PMCID: PMC9521926 DOI: 10.1371/journal.pone.0274131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/22/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES To describe a flexible common data model (CDM) approach that can be efficiently tailored to study-specific needs to facilitate pooled patient-level analysis and aggregated/meta-analysis of routinely collected retrospective patient data from disparate data sources; and to detail the application of this CDM approach to the DISCOVER CKD retrospective cohort, a longitudinal database of routinely collected (secondary) patient data of individuals with chronic kidney disease (CKD). METHODS The flexible CDM approach incorporated three independent, exchangeable components that preceded data mapping and data model implementation: (1) standardized code lists (unifying medical events from different coding systems); (2) laboratory unit harmonization tables; and (3) base cohort definitions. Events between different coding vocabularies were not mapped code-to-code; for each data source, code lists of labels were curated at the entity/event level. A study team of epidemiologists, clinicians, informaticists, and data scientists were included within the validation of each component. RESULTS Applying the CDM to the DISCOVER CKD retrospective cohort, secondary data from 1,857,593 patients with CKD were harmonized from five data sources, across three countries, into a discrete database for rapid real-world evidence generation. CONCLUSIONS This flexible CDM approach facilitates evidence generation from real-world data within the DISCOVER CKD retrospective cohort, providing novel insights into the epidemiology of CKD that may expedite improvements in diagnosis, prognosis, early intervention, and disease management. The adaptable architecture of this CDM approach ensures scalable, fast, and efficient application within other therapy areas to facilitate the combined analysis of different types of secondary data from multiple, heterogeneous sources.
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Affiliation(s)
- Supriya Kumar
- Real World Evidence Data and Analytics, BioPharmaceuticals Medical, AstraZeneca, Gaithersburg, MD, United States of America
| | - Matthew Arnold
- Real World Evidence Data and Analytics, BioPharmaceuticals Medical, AstraZeneca, Cambridge, United Kingdom
| | - Glen James
- Formerly Cardiovascular, Renal, Metabolism & Epidemiology, BioPharmaceuticals Medical, AstraZeneca, Cambridge, United Kingdom
| | - Rema Padman
- Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, United States of America
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18
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Tak YW, You SC, Han JH, Kim SS, Kim GT, Lee Y. Perceived Risk of Re-Identification in OMOP-CDM Database: A Cross-Sectional Survey. J Korean Med Sci 2022; 37:e205. [PMID: 35790207 PMCID: PMC9259248 DOI: 10.3346/jkms.2022.37.e205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 05/30/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND The advancement of information technology has immensely increased the quality and volume of health data. This has led to an increase in observational study, as well as to the threat of privacy invasion. Recently, a distributed research network based on the common data model (CDM) has emerged, enabling collaborative international medical research without sharing patient-level data. Although the CDM database for each institution is built inside a firewall, the risk of re-identification requires management. Hence, this study aims to elucidate the perceptions CDM users have towards CDM and risk management for re-identification. METHODS The survey, targeted to answer specific in-depth questions on CDM, was conducted from October to November 2020. We targeted well-experienced researchers who actively use CDM. Basic statistics (total number and percent) were computed for all covariates. RESULTS There were 33 valid respondents. Of these, 43.8% suggested additional anonymization was unnecessary beyond, "minimum cell count" policy, which obscures a cell with a value lower than certain number (usually 5) in shared results to minimize the liability of re-identification due to rare conditions. During extract-transform-load processes, 81.8% of respondents assumed structured data is under control from the risk of re-identification. However, respondents noted that date of birth and death were highly re-identifiable information. The majority of respondents (n = 22, 66.7%) conceded the possibility of identifier-contained unstructured data in the NOTE table. CONCLUSION Overall, CDM users generally attributed high reliability for privacy protection to the intrinsic nature of CDM. There was little demand for additional de-identification methods. However, unstructured data in the CDM were suspected to have risks. The necessity for a coordinating consortium to define and manage the re-identification risk of CDM was urged.
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Affiliation(s)
- Yae Won Tak
- Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Seng Chan You
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - Jeong Hyun Han
- Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Soon-Seok Kim
- Department of Big Data Science, Halla University, Wonju, Korea
| | | | - Yura Lee
- Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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19
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Desai RJ, Matheny ME, Johnson K, Marsolo K, Curtis LH, Nelson JC, Heagerty PJ, Maro J, Brown J, Toh S, Nguyen M, Ball R, Pan GD, Wang SV, Gagne JJ, Schneeweiss S. Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework. NPJ Digit Med 2021; 4:170. [PMID: 34931012 PMCID: PMC8688411 DOI: 10.1038/s41746-021-00542-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 11/28/2021] [Indexed: 11/09/2022] Open
Abstract
The Sentinel System is a major component of the United States Food and Drug Administration's (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center's initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.
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Affiliation(s)
- Rishi J Desai
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Michael E Matheny
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kevin Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Keith Marsolo
- Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - Lesley H Curtis
- Department of Population Health Sciences, Duke University, Durham, NC, USA
| | - Jennifer C Nelson
- Biostatistics Unit, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | | | - Judith Maro
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
| | - Jeffery Brown
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
| | - Sengwee Toh
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA
| | - Michael Nguyen
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA
| | - Robert Ball
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA
| | - Gerald Dal Pan
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA
| | - Shirley V Wang
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Joshua J Gagne
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.,Johnson & Johnson, New Brunswick, NJ, USA
| | - Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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20
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Thurin NH, Pajouheshnia R, Roberto G, Dodd C, Hyeraci G, Bartolini C, Paoletti O, Nordeng H, Wallach-Kildemoes H, Ehrenstein V, Dudukina E, MacDonald T, De Paoli G, Loane M, Damase-Michel C, Beau AB, Droz-Perroteau C, Lassalle R, Bergman J, Swart K, Schink T, Cavero-Carbonell C, Barrachina-Bonet L, Gomez-Lumbreras A, Giner-Soriano M, Aragón M, Neville AJ, Puccini A, Pierini A, Ientile V, Trifirò G, Rissmann A, Leinonen MK, Martikainen V, Jordan S, Thayer D, Scanlon I, Georgiou ME, Cunnington M, Swertz M, Sturkenboom M, Gini R. From Inception to ConcePTION: Genesis of a Network to Support Better Monitoring and Communication of Medication Safety During Pregnancy and Breastfeeding. Clin Pharmacol Ther 2021; 111:321-331. [PMID: 34826340 PMCID: PMC9299060 DOI: 10.1002/cpt.2476] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/24/2021] [Indexed: 02/01/2023]
Abstract
In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project-Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation-with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and mother-child linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population.
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Affiliation(s)
- Nicolas H Thurin
- Bordeaux PharmacoEpi, INSERM CIC-P1401, Univ. Bordeaux, Bordeaux, France
| | - Romin Pajouheshnia
- Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | | | - Caitlin Dodd
- PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Giulia Hyeraci
- Agenzia regionale di sanità della Toscana, Florence, Italy
| | | | - Olga Paoletti
- Agenzia regionale di sanità della Toscana, Florence, Italy
| | - Hedvig Nordeng
- PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Helle Wallach-Kildemoes
- PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, and PharmaTox Strategic Initiative, Faculty of Mathematics and Natural Sciences, University of Oslo, Oslo, Norway
| | - Vera Ehrenstein
- Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark
| | - Elena Dudukina
- Department of Clinical Epidemiology, Aarhus University, Aarhus, Denmark
| | - Thomas MacDonald
- MEMO Research, School of Medicine, University of Dundee, Dundee, UK
| | - Giorgia De Paoli
- MEMO Research, School of Medicine, University of Dundee, Dundee, UK
| | - Maria Loane
- Institute of Nursing and Health Research, Ulster University, Newtownabbey, UK
| | | | - Anna-Belle Beau
- INSERM, CERPOP: SPHERE, CIC 1436, Université de Toulouse, Toulouse, France
| | | | - Régis Lassalle
- Bordeaux PharmacoEpi, INSERM CIC-P1401, Univ. Bordeaux, Bordeaux, France
| | - Jorieke Bergman
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Karin Swart
- PHARMO Institute for Drug Outcomes Research, Utrecht, The Netherlands
| | - Tania Schink
- Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
| | - Clara Cavero-Carbonell
- Fundació per al Foment de la Investigació Sanitaria i Biomédica de la Comunitat Valenciana (FISABIO), Valencia, Spain
| | - Laia Barrachina-Bonet
- Fundació per al Foment de la Investigació Sanitaria i Biomédica de la Comunitat Valenciana (FISABIO), Valencia, Spain
| | - Ainhoa Gomez-Lumbreras
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Maria Giner-Soriano
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - María Aragón
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Amanda J Neville
- IMER Registry (Emila Romagna Registry of Birth Defects), Center of Epidemiology for Clinical Research, University of Ferrara, Ferrara, Italy
| | - Aurora Puccini
- Drug Policy Service, Emilia Romagna Region Health Authority, Bologna, Italy
| | - Anna Pierini
- Epidemiology of Rare Diseases and Congenital Anomalies Unit, National Research Council-Institute of Clinical Physiology (CNR-IFC), Pisa, Italy
| | - Valentina Ientile
- Department of Biomedical and Dental Sciences and Morpho-functional Imaging, University of Messina, Messina, Italy
| | - Gianluca Trifirò
- Department of Diagnostics and Public Health, University of Verona, Verona, Italy
| | - Anke Rissmann
- Malformation Monitoring Centre Saxony-Anhalt, Medical Faculty, Otto-von-Guericke-University, Magdeburg, Germany
| | | | | | - Sue Jordan
- Faculty of Health and Life Science, Swansea University, Swansea, UK
| | - Daniel Thayer
- Faculty of Health and Life Science, Swansea University, Swansea, UK
| | - Ieuan Scanlon
- Faculty of Health and Life Science, Swansea University, Swansea, UK
| | | | | | - Morris Swertz
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Miriam Sturkenboom
- Department Datascience and Biostatistics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rosa Gini
- Agenzia regionale di sanità della Toscana, Florence, Italy
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21
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Schneeweiss S, Patorno E. Conducting Real-world Evidence Studies on the Clinical Outcomes of Diabetes Treatments. Endocr Rev 2021; 42:658-690. [PMID: 33710268 PMCID: PMC8476933 DOI: 10.1210/endrev/bnab007] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Indexed: 12/12/2022]
Abstract
Real-world evidence (RWE), the understanding of treatment effectiveness in clinical practice generated from longitudinal patient-level data from the routine operation of the healthcare system, is thought to complement evidence on the efficacy of medications from randomized controlled trials (RCTs). RWE studies follow a structured approach. (1) A design layer decides on the study design, which is driven by the study question and refined by a medically informed target population, patient-informed outcomes, and biologically informed effect windows. Imagining the randomized trial we would ideally perform before designing an RWE study in its likeness reduces bias; the new-user active comparator cohort design has proven useful in many RWE studies of diabetes treatments. (2) A measurement layer transforms the longitudinal patient-level data stream into variables that identify the study population, the pre-exposure patient characteristics, the treatment, and the treatment-emergent outcomes. Working with secondary data increases the measurement complexity compared to primary data collection that we find in most RCTs. (3) An analysis layer focuses on the causal treatment effect estimation. Propensity score analyses have gained in popularity to minimize confounding in healthcare database analyses. Well-understood investigator errors, like immortal time bias, adjustment for causal intermediates, or reverse causation, should be avoided. To increase reproducibility of RWE findings, studies require full implementation transparency. This article integrates state-of-the-art knowledge on how to conduct and review RWE studies on diabetes treatments to maximize study validity and ultimately increased confidence in RWE-based decision making.
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Affiliation(s)
- Sebastian Schneeweiss
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MAUSA
| | - Elisabetta Patorno
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MAUSA
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22
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Bérard A. Pharmacoepidemiology Research-Real-World Evidence for Decision Making. Front Pharmacol 2021; 12:723427. [PMID: 34557096 PMCID: PMC8452957 DOI: 10.3389/fphar.2021.723427] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/16/2021] [Indexed: 12/27/2022] Open
Affiliation(s)
- Anick Bérard
- Faculty of Pharmacy, University of Montreal, Montreal, QC, Canada.,Faculty of Medicine, Université Claude Bernard Lyon 1, Lyon, France.,CHU Sainte-Justine, Montreal, QC, Canada
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23
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Belleudi V, Fortinguerra F, Poggi FR, Perna S, Bortolus R, Donati S, Clavenna A, Locatelli A, Davoli M, Addis A, Trotta F. The Italian Network for Monitoring Medication Use During Pregnancy (MoM-Net): Experience and Perspectives. Front Pharmacol 2021; 12:699062. [PMID: 34248644 PMCID: PMC8262612 DOI: 10.3389/fphar.2021.699062] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/04/2021] [Indexed: 11/25/2022] Open
Abstract
There is an acute need for research to acquire high-quality information on the use of medicines in pregnancy, both in terms of appropriateness and safety. For this purpose, the Italian Medicines Agency established a Network for Monitoring Medication use in pregnancy (MoM-Net) through the conduction of population-based studies using administrative data available at regional level. This paper aimed to describe the experiences and challenges within the network. MoM-Net currently involves eight regions and several experts from public and academic institutions. The first study conducted aimed to identify drug use before, during and after pregnancy investigating specific therapeutic categories, analysing regional variability and monitoring drug use in specific subpopulations (i.e. foreign women/multiple pregnancies). Aggregated demographic, clinical, and prescription data were analysed using a distributed network approach based on common data model. The study population included all women delivering during 2016–2018 in the participating regions (n = 449,012), and corresponding to 59% of deliveries in Italy. Seventy-three per cent of the cohort had at least one drug prescription during pregnancy, compared to 57% before and 59% after pregnancy. In general, a good adherence to guidelines for pregnant women was found although some drug categories at risk of inappropriateness, such as progestins and antibiotics, were prescribed. A strong variability in the use of drugs among regions and in specific subpopulations was observed. The MoM-Net represents a valuable surveillance system on the use of medicines in pregnancy, available to monitor drug categories at high risk of inappropriateness and to investigate health needs in specific regions or subpopulations.
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Affiliation(s)
- Valeria Belleudi
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | | | - Francesca R Poggi
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | | | - Renata Bortolus
- Directorate General for Preventive Health - Office 9, Ministry of Health, Rome, Italy
| | - Serena Donati
- National Centre for Disease Prevention and Health Promotion, Istituto Superiore di Sanità - Italian National Institute of Health, Rome, Italy
| | - Antonio Clavenna
- Laboratory for Mother and Child Health, Department of Public Health - Istituto di Ricerche Farmacologiche Mario Negri IRCSS, Milan, Italy
| | - Anna Locatelli
- Department of Obstetrics and Gynecology, University of Milano-Bicocca, Monza, Italy
| | - Marina Davoli
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
| | - Antonio Addis
- Department of Epidemiology, Lazio Regional Health Service, Rome, Italy
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24
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Brown JS, Maro JC, Nguyen M, Ball R. Using and improving distributed data networks to generate actionable evidence: the case of real-world outcomes in the Food and Drug Administration's Sentinel system. J Am Med Inform Assoc 2021; 27:793-797. [PMID: 32279080 DOI: 10.1093/jamia/ocaa028] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 02/24/2020] [Indexed: 11/13/2022] Open
Abstract
The US Food and Drug Administration (FDA) Sentinel System uses a distributed data network, a common data model, curated real-world data, and distributed analytic tools to generate evidence for FDA decision-making. Sentinel system needs include analytic flexibility, transparency, and reproducibility while protecting patient privacy. Based on over a decade of experience, a critical system limitation is the inability to identify enough medical conditions of interest in observational data to a satisfactory level of accuracy. Improving the system's ability to use computable phenotypes will require an "all of the above" approach that improves use of electronic health data while incorporating the growing array of complementary electronic health record data sources. FDA recently funded a Sentinel System Innovation Center and a Community Building and Outreach Center that will provide a platform for collaboration across disciplines to promote better use of real-world data for decision-making.
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Affiliation(s)
- Jeffrey S Brown
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Judith C Maro
- Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Nguyen
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, Maryland, USA
| | - Robert Ball
- Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, FDA, Silver Spring, Maryland, USA
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25
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Zayas-Cabán T, Chaney KJ, Rucker DW. National health information technology priorities for research: A policy and development agenda. J Am Med Inform Assoc 2021; 27:652-657. [PMID: 32090265 DOI: 10.1093/jamia/ocaa008] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 01/03/2020] [Accepted: 01/16/2020] [Indexed: 01/17/2023] Open
Abstract
The growth of digitized health data presents exciting opportunities to leverage the health information technology (IT) infrastructure for advancing biomedical and health services research. However, challenges impede use of those resources effectively and at scale to improve outcomes. The Office of the National Coordinator for Health Information Technology (ONC) led a collaborative effort to identify challenges, priorities, and actions to leverage health IT and electronic health data for research. Specifically, ONC led a review of relevant literature and programs, key informant interviews, and a stakeholder workshop to identify electronic health data and health IT infrastructure gaps. This effort resulted in the National Health IT Priorities for Research: A Policy and Development Agenda, which articulates an optimized health information ecosystem for scientific discovery. This article outlines 9 priorities and recommended actions to be implemented in collaboration with the research and informatics communities for realizing this vision.
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Affiliation(s)
- Teresa Zayas-Cabán
- Office of the National Coordinator for Health Information Technology, U.S. Department of Health and Human Services, Washington, DC, USA
| | - Kevin J Chaney
- Office of the National Coordinator for Health Information Technology, U.S. Department of Health and Human Services, Washington, DC, USA
| | - Donald W Rucker
- Office of the National Coordinator for Health Information Technology, U.S. Department of Health and Human Services, Washington, DC, USA
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26
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Kent S, Burn E, Dawoud D, Jonsson P, Østby JT, Hughes N, Rijnbeek P, Bouvy JC. Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment. PHARMACOECONOMICS 2021; 39:275-285. [PMID: 33336320 PMCID: PMC7746423 DOI: 10.1007/s40273-020-00981-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/05/2020] [Indexed: 05/28/2023]
Abstract
There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making.
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Affiliation(s)
- Seamus Kent
- National Institute for Health and Care Excellence, London, United Kingdom
| | - Edward Burn
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, UK
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Dalia Dawoud
- National Institute for Health and Care Excellence, London, United Kingdom
| | - Pall Jonsson
- National Institute for Health and Care Excellence, London, United Kingdom
| | | | - Nigel Hughes
- Janssen Research and Development, Beerse, Belgium
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jacoline C Bouvy
- National Institute for Health and Care Excellence, London, United Kingdom.
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27
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Seesaghur A, Petruski-Ivleva N, Banks V, Wang JR, Mattox P, Hoeben E, Maskell J, Neasham D, Reynolds SL, Kafatos G. Real-world reproducibility study characterizing patients newly diagnosed with multiple myeloma using Clinical Practice Research Datalink, a UK-based electronic health records database. Pharmacoepidemiol Drug Saf 2020; 30:248-256. [PMID: 33174338 PMCID: PMC7984077 DOI: 10.1002/pds.5171] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 11/04/2020] [Indexed: 12/31/2022]
Abstract
Purpose We evaluated the reproducibility of a study characterizing newly‐diagnosed multiple myeloma (MM) patients within an electronic health records (EHR) database using different analytic tools. Methods We reproduced the findings of a descriptive cohort study using an iterative two‐phase approach. In Phase I, a common protocol and statistical analysis plan (SAP) were implemented by independent investigators using the Aetion Evidence Platform® (AEP), a rapid‐cycle analytics tool, and SAS statistical software as a gold standard for statistical analyses. Using the UK Clinical Practice Research Datalink (CPRD) dataset, the study included patients newly diagnosed with MM within primary care setting and assessed baseline demographics, conditions, drug exposure, and laboratory procedures. Phase II incorporated analysis revisions based on our initial comparison of the Phase I findings. Reproducibility of findings was evaluate by calculating the match rate and absolute difference in prevalence between the SAS and AEP study results. Results Phase I yielded slightly discrepant results, prompting amendments to SAP to add more clarity to operational decisions. After detailed specification of data and operational choices, exact concordance was achieved for the number of eligible patients (N = 2646), demographics, comorbidities (i.e., osteopenia, osteoporosis, cardiovascular disease [CVD], and hypertension), bone pain, skeletal‐related events, drug exposure, and laboratory investigations in the Phase II analyses. Conclusions In this reproducibility study, a rapid‐cycle analytics tool and traditional statistical software achieved near‐exact findings after detailed specification of data and operational choices. Transparency and communication of the study design, operational and analytical choices between independent investigators were critical to achieve this reproducibility.
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Affiliation(s)
| | | | - Victoria Banks
- Department Center for Observational Research, Amgen Ltd, Uxbridge, UK.,VLB Contractors Ltd, Kent, UK
| | | | - Pattra Mattox
- Department Science, Aetion, Inc, Boston, Massachusetts, USA
| | - Edwin Hoeben
- Department Center for Observational Research, Amgen Ltd, Uxbridge, UK
| | - Joe Maskell
- Department Center for Observational Research, Amgen Ltd, Uxbridge, UK
| | - David Neasham
- Department Center for Observational Research, Amgen Ltd, Uxbridge, UK
| | | | - George Kafatos
- Department Center for Observational Research, Amgen Ltd, Uxbridge, UK
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28
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29
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Gini R, Sturkenboom MCJ, Sultana J, Cave A, Landi A, Pacurariu A, Roberto G, Schink T, Candore G, Slattery J, Trifirò G. Different Strategies to Execute Multi-Database Studies for Medicines Surveillance in Real-World Setting: A Reflection on the European Model. Clin Pharmacol Ther 2020; 108:228-235. [PMID: 32243569 PMCID: PMC7484985 DOI: 10.1002/cpt.1833] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 03/13/2020] [Indexed: 12/18/2022]
Abstract
Although postmarketing studies conducted in population-based databases often contain information on patients in the order of millions, they can still be underpowered if outcomes or exposure of interest is rare, or the interest is in subgroup effects. Combining several databases might provide the statistical power needed. A multi-database study (MDS) uses at least two healthcare databases, which are not linked with each other at an individual person level, with analyses carried out in parallel across each database applying a common study protocol. Although many MDSs have been performed in Europe in the past 10 years, there is a lack of clarity on the peculiarities and implications of the existing strategies to conduct them. In this review, we identify four strategies to execute MDSs, classified according to specific choices in the execution: (A) local analyses, where data are extracted and analyzed locally, with programs developed by each site; (B) sharing of raw data, where raw data are locally extracted and transferred without analysis to a central partner, where all the data are pooled and analyzed; (C) use of a common data model with study-specific data, where study-specific data are locally extracted, loaded into a common data model, and processed locally with centrally developed programs; and (D) use of general common data model, where all local data are extracted and loaded into a common data model, prior to and independent of any study protocol, and protocols are incorporated in centrally developed programs that run locally. We illustrate differences between strategies and analyze potential implications.
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Affiliation(s)
- Rona Gini
- Agenzia regionale di sanità della ToscanaFlorenceItaly
| | | | | | - Alison Cave
- European Medicines AgencyAmsterdamThe Netherlands
| | - Annalisa Landi
- Fondazione per la Ricerca Farmacologica Gianni Benzi OnlusValenzanoItaly
- Teddy European Network of Excellence for Paediatric Clinical ResearchPaviaItaly
| | | | | | - Tania Schink
- Leibniz Institute for Prevention Research and EpidemiologyBremenGermany
| | | | - Jim Slattery
- European Medicines AgencyAmsterdamThe Netherlands
| | - Gianluca Trifirò
- Department of Biomedical and Dental Sciences and Morphofunctional ImagingUniversità di MessinaMessinaItaly
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30
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Candore G, Hedenmalm K, Slattery J, Cave A, Kurz X, Arlett P. Can We Rely on Results From IQVIA Medical Research Data UK Converted to the Observational Medical Outcome Partnership Common Data Model?: A Validation Study Based on Prescribing Codeine in Children. Clin Pharmacol Ther 2020; 107:915-925. [PMID: 31956997 PMCID: PMC7158210 DOI: 10.1002/cpt.1785] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 12/17/2019] [Indexed: 12/15/2022]
Abstract
Exploring and combining results from more than one real‐world data (RWD) source might be necessary in order to explore variability and demonstrate generalizability of the results or for regulatory requirements. However, the heterogeneous nature of RWD poses challenges when working with more than one source, some of which can be solved by analyzing databases converted into a common data model (CDM). The main objective of the study was to evaluate the implementation of the Observational Medical Outcome Partnership (OMOP) CDM on IQVIA Medical Research Data (IMRD)‐UK data. A drug utilization study describing the prescribing of codeine for pain in children was used as a case study to be replicated in IMRD‐UK and its corresponding OMOP CDM transformation. Differences between IMRD‐UK source and OMOP CDM were identified and investigated. In IMRD‐UK updated to May 2017, results were similar between source and transformed data with few discrepancies. These were the result of different conventions applied during the transformation regarding the date of birth for children younger than 15 years and the start of the observation period, and of a misclassification of two drug treatments. After the initial analysis and feedback provided, a rerun of the analysis in IMRD‐UK updated to September 2018 showed almost identical results for all the measures analyzed. For this study, the conversion to OMOP CDM was adequate. Although some decisions and mapping could be improved, these impacted on the absolute results but not on the study inferences. This validation study supports six recommendations for good practice in transforming to CDMs.
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Affiliation(s)
- Gianmario Candore
- Business Data Department, European Medicines Agency, Amsterdam, The Netherlands
| | - Karin Hedenmalm
- Business Data Department, European Medicines Agency, Amsterdam, The Netherlands
| | - Jim Slattery
- Pharmacovigilance and Epidemiology Department, European Medicines Agency, Amsterdam, The Netherlands
| | - Alison Cave
- Pharmacovigilance and Epidemiology Department, European Medicines Agency, Amsterdam, The Netherlands
| | - Xavier Kurz
- Pharmacovigilance and Epidemiology Department, European Medicines Agency, Amsterdam, The Netherlands
| | - Peter Arlett
- Pharmacovigilance and Epidemiology Department, European Medicines Agency, Amsterdam, The Netherlands
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