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De Pretis F, van Gils M, Forsberg MM. A smart hospital-driven approach to precision pharmacovigilance. Trends Pharmacol Sci 2022; 43:473-481. [PMID: 35490032 DOI: 10.1016/j.tips.2022.03.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/25/2022] [Accepted: 03/22/2022] [Indexed: 01/03/2023]
Abstract
Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.
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Affiliation(s)
- Francesco De Pretis
- VTT Technical Research Centre of Finland Ltd, 70210 Kuopio, Finland; Department of Communication and Economics, University of Modena and Reggio Emilia, 42121 Reggio Emilia, Italy.
| | - Mark van Gils
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
| | - Markus M Forsberg
- VTT Technical Research Centre of Finland Ltd, 70210 Kuopio, Finland; School of Pharmacy, University of Eastern Finland, 70211 Kuopio, Finland
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2
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De las Cuevas C, Sanz EJ, Rohde C, de Leon J. Association between myocarditis and antipsychotics other than clozapine: a systematic literature review and a pharmacovigilance study using VigiBase. Expert Rev Clin Pharmacol 2022; 15:65-78. [DOI: 10.1080/17512433.2022.2032659] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Carlos De las Cuevas
- Department of Internal Medicine, Dermatology and Psychiatry, School of Medicine, University of La Laguna, Canary Islands, Spain
- Instituto Universitario de Neurociencia (IUNE), Universidad de La Laguna, San Cristóbal de La Laguna, Spain
| | - Emilio J. Sanz
- Department of Physical Medicine and Pharmacology, School of Medicine, Universidad de La Laguna, Canary Islands, Spain
- Hospital Universitario de Canarias, Tenerife, Spain
| | - Christopher Rohde
- Department of Affective Disorders, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jose de Leon
- Mental Health Research Center at Eastern State Hospital, Lexington, KY, US
- Biomedical Research Centre in Mental Health Net (CIBERSAM), Santiago Apostol Hospital, University of the Basque Country, Vitoria, Spain
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3
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Bate A, Hobbiger SF. Artificial Intelligence, Real-World Automation and the Safety of Medicines. Drug Saf 2020; 44:125-132. [PMID: 33026641 DOI: 10.1007/s40264-020-01001-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/08/2020] [Indexed: 12/16/2022]
Abstract
Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.
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Affiliation(s)
- Andrew Bate
- Clinical Safety and Pharmacovigilance, GSK, 980 Great West Road, Brentford, Middlesex, TW8 9GS, UK.
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
| | - Steve F Hobbiger
- Clinical Safety and Pharmacovigilance, GSK, 980 Great West Road, Brentford, Middlesex, TW8 9GS, UK
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4
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De Pretis F, Landes J, Osimani B. E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance. Front Pharmacol 2019; 10:1317. [PMID: 31920632 PMCID: PMC6929659 DOI: 10.3389/fphar.2019.01317] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Accepted: 10/15/2019] [Indexed: 01/05/2023] Open
Abstract
Background: Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm. Methods: In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. In this article, we expand the Bayesian framework and add “evidential modulators,” which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, “E-Synthesis”, is then applied to a case study. Results: Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework. Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is philosophically and statistically grounded. Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses.
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Affiliation(s)
- Francesco De Pretis
- Dipartimento di Scienze biomediche e Sanità pubblica, Università Politecnica delle Marche, Ancona, Italy.,Dipartimento di Comunicazione ed Economia, Università degli Studi di Modena e Reggio Emilia, Reggio Emilia, Italy
| | - Jürgen Landes
- Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universtät München, München, Germany
| | - Barbara Osimani
- Dipartimento di Scienze biomediche e Sanità pubblica, Università Politecnica delle Marche, Ancona, Italy.,Munich Center for Mathematical Philosophy, Ludwig-Maximilians-Universtät München, München, Germany
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5
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Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Front Pharmacol 2019; 10:415. [PMID: 31156424 PMCID: PMC6533857 DOI: 10.3389/fphar.2019.00415] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 04/02/2019] [Indexed: 12/12/2022] Open
Abstract
Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.
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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.,Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France.,Public Health and Medical Information Unit, University Hospital of Saint-Etienne, Saint-Étienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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6
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Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2019. [DOI: 10.1007/s41060-019-00175-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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7
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Convertino I, Ferraro S, Blandizzi C, Tuccori M. The usefulness of listening social media for pharmacovigilance purposes: a systematic review. Expert Opin Drug Saf 2018; 17:1081-1093. [DOI: 10.1080/14740338.2018.1531847] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Irma Convertino
- Unit of Pharmacology and Pharmacovigilance, University of Pisa, Pisa, Italy
| | - Sara Ferraro
- Unit of Pharmacology and Pharmacovigilance, University of Pisa, Pisa, Italy
| | - Corrado Blandizzi
- Unit of Pharmacology and Pharmacovigilance, University of Pisa, Pisa, Italy
- Division of Pharmacology and Pharmacovigilance, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Marco Tuccori
- Unit of Pharmacology and Pharmacovigilance, University of Pisa, Pisa, Italy
- Division of Pharmacology and Pharmacovigilance, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
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8
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Knoph KN, Morgan RJ, Palmer BA, Schak KM, Owen AC, Leloux MR, Patel M, Leung JG. Clozapine-induced cardiomyopathy and myocarditis monitoring: A systematic review. Schizophr Res 2018; 199:17-30. [PMID: 29548760 DOI: 10.1016/j.schres.2018.03.006] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 02/27/2018] [Accepted: 03/04/2018] [Indexed: 02/02/2023]
Abstract
The use of clozapine requires monitoring the absolute neutrophil count because of the risk of agranulocytosis, but other potentially fatal adverse events associated with clozapine (specifically, myocarditis and cardiomyopathy) do not have mandatory procedures. We performed a systematic review of English-language articles to synthesize an evidence-based approach for myocarditis and cardiomyopathy monitoring. Articles published from January 1988 through February 2017 were identified through a search of Ovid MEDLINE, Ovid Embase, Ovid Cochrane Database of Systematic Reviews, Web of Science, Scopus, and Google Scholar. Selected articles were required to relate to myocarditis or cardiomyopathy in humans from exposure to clozapine. A total of 144 articles were included. Recommendations varied widely. Some authors recommended baseline laboratory monitoring, with or without follow-up testing, for C-reactive protein, creatine kinase MB, and troponin. Electrocardiography was commonly recommended, and echocardiography was less commonly recommended. The expense of monitoring was a consideration. A unanimous recommendation was to stop the use of clozapine and seek a cardiovascular consultation if myocarditis or cardiomyopathy is suspected. Although there is general agreement on which tests to perform for confirming myocarditis and cardiomyopathy, preemptive screening for these clozapine-induced conditions is controversial, and cost and barriers for the use of clozapine are concerns. For asymptomatic patients receiving clozapine, testing could include baseline electrocardiography, echocardiography as part of a cardiac consultation if patients have cardiac disease or risk factors, and monitoring of C-reactive protein and troponin as indicated.
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Affiliation(s)
- Kristen N Knoph
- Pharmacy Services, Mayo Clinic, Rochester, MN, United States
| | - Robert J Morgan
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Brian A Palmer
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Kathryn M Schak
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States
| | - Amanda C Owen
- Pharmacy Services, Mayo Clinic, Rochester, MN, United States
| | - Megan R Leloux
- Pharmacy Services, Mayo Clinic, Rochester, MN, United States
| | - Mayur Patel
- Pharmacy Services, Mayo Clinic, Rochester, MN, United States
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9
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Natsiavas P, Boyce RD, Jaulent MC, Koutkias V. OpenPVSignal: Advancing Information Search, Sharing and Reuse on Pharmacovigilance Signals via FAIR Principles and Semantic Web Technologies. Front Pharmacol 2018; 9:609. [PMID: 29997499 PMCID: PMC6028717 DOI: 10.3389/fphar.2018.00609] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 05/21/2018] [Indexed: 12/27/2022] Open
Abstract
Signal detection and management is a key activity in pharmacovigilance (PV). When a new PV signal is identified, the respective information is publicly communicated in the form of periodic newsletters or reports by organizations that monitor and investigate PV-related information (such as the World Health Organization and national PV centers). However, this type of communication does not allow for systematic access, discovery and explicit data interlinking and, therefore, does not facilitate automated data sharing and reuse. In this paper, we present OpenPVSignal, a novel ontology aiming to support the semantic enrichment and rigorous communication of PV signal information in a systematic way, focusing on two key aspects: (a) publishing signal information according to the FAIR (Findable, Accessible, Interoperable, and Re-usable) data principles, and (b) exploiting automatic reasoning capabilities upon the interlinked PV signal report data. OpenPVSignal is developed as a reusable, extendable and machine-understandable model based on Semantic Web standards/recommendations. In particular, it can be used to model PV signal report data focusing on: (a) heterogeneous data interlinking, (b) semantic and syntactic interoperability, (c) provenance tracking and (d) knowledge expressiveness. OpenPVSignal is built upon widely-accepted semantic models, namely, the provenance ontology (PROV-O), the Micropublications semantic model, the Web Annotation Data Model (WADM), the Ontology of Adverse Events (OAE) and the Time ontology. To this end, we describe the design of OpenPVSignal and demonstrate its applicability as well as the reasoning capabilities enabled by its use. We also provide an evaluation of the model against the FAIR data principles. The applicability of OpenPVSignal is demonstrated by using PV signal information published in: (a) the World Health Organization's Pharmaceuticals Newsletter, (b) the Netherlands Pharmacovigilance Centre Lareb Web site and (c) the U.S. Food and Drug Administration (FDA) Drug Safety Communications, also available on the FDA Web site.
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Affiliation(s)
- Pantelis Natsiavas
- Centre for Research & Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece.,Lab of Computing, Medical Informatics & Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Richard D Boyce
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Marie-Christine Jaulent
- Institut National de la Santé et de la Recherche Médicale, U1142, LIMICS, Paris, France.,Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, Paris, France.,Université Paris 13, Sorbonne Paris Cité, UMR_S 1142, LIMICS, Villetaneuse, France
| | - Vassilis Koutkias
- Centre for Research & Technology Hellas, Institute of Applied Biosciences, Thessaloniki, Greece.,Lab of Computing, Medical Informatics & Biomedical Imaging Technologies, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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10
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Chen X, Faviez C, Schuck S, Lillo-Le-Louët A, Texier N, Dahamna B, Huot C, Foulquié P, Pereira S, Leroux V, Karapetiantz P, Guenegou-Arnoux A, Katsahian S, Bousquet C, Burgun A. Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate. Front Pharmacol 2018; 9:541. [PMID: 29881351 PMCID: PMC5978246 DOI: 10.3389/fphar.2018.00541] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Accepted: 05/04/2018] [Indexed: 12/29/2022] Open
Abstract
Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus. Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics. Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse. Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better understanding of patients' behaviors regarding drugs, including misuse.
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Affiliation(s)
- Xiaoyi Chen
- UMRS 1138, équipe 22, Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
| | | | | | - Agnès Lillo-Le-Louët
- Centre Régional de Pharmacovigilance, Hôpital Européen Georges-Pompidou, AP-HP, Paris, France
| | | | - Badisse Dahamna
- Service d'Informatique Biomédicale, Centre Hospitalier Universitaire de Rouen, Rouen, France.,Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes-TIBS EA 4108, Rouen, France
| | | | | | | | | | - Pierre Karapetiantz
- UMRS 1138, équipe 22, Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
| | - Armelle Guenegou-Arnoux
- UMRS 1138, équipe 22, Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France
| | - Sandrine Katsahian
- UMRS 1138, équipe 22, Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France.,Département d'Informatique Médicale, Hôpital Européen Georges Pompidou, Paris, France
| | - Cédric Bousquet
- Sorbonne Université, Inserm, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, Paris, France
| | - Anita Burgun
- UMRS 1138, équipe 22, Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, Université Paris Descartes, Paris, France.,Département d'Informatique Médicale, Hôpital Européen Georges Pompidou, Paris, France
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11
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Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data. J Biomed Semantics 2017; 8:11. [PMID: 28270198 PMCID: PMC5341176 DOI: 10.1186/s13326-017-0115-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Accepted: 01/13/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Integrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings. RESULTS LAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources. CONCLUSIONS The prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.
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