OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval.

INTRODUCTION: Efficient searching and coding in databases that use terminological resources requires that they support efficient data retrieval. The Medical Dictionary for Regulatory Activities (MedDRA) is a reference terminology for several countries and organizations to code adverse drug reactions (ADRs) for pharmacovigilance. Ontologies that are available in the medical domain provide several advantages such as reasoning to improve data retrieval. The field of pharmacovigilance does not yet benefit from a fully operational ontology to formally represent the MedDRA terms. Our objective was to build a semantic resource based on formal description logic to improve MedDRA term retrieval and aid the generation of on-demand custom groupings by appropriately and efficiently selecting terms: OntoADR. METHODS: The method consists of the following steps: (1) mapping between MedDRA terms and SNOMED-CT, (2) generation of semantic definitions using semi-automatic methods, (3) storage of the resource and (4) manual curation by pharmacovigilance experts. RESULTS: We built a semantic resource for ADRs enabling a new type of semantics-based term search. OntoADR adds new search capabilities relative to previous approaches, overcoming the usual limitations of computation using lightweight description logic, such as the intractability of unions or negation queries, bringing it closer to user needs. Our automated approach for defining MedDRA terms enabled the association of at least one defining relationship with 67% of preferred terms. The curation work performed on our sample showed an error level of 14% for this automated approach. We tested OntoADR in practice, which allowed us to build custom groupings for several medical topics of interest. DISCUSSION: The methods we describe in this article could be adapted and extended to other terminologies which do not benefit from a formal semantic representation, thus enabling better data retrieval performance. Our custom groupings of MedDRA terms were used while performing signal detection, which suggests that the graphical user interface we are currently implementing to process OntoADR could be usefully integrated into specialized pharmacovigilance software that rely on MedDRA.

[Automated grouping of terms associated to cardiac valve fibrosis in MedDRA]. / Groupement automatisé de termes liés aux valvulopathies médicamenteuses dans MedDRA.

AIM: To propose an alternative approach for building custom groupings of terms that complements the usual approach based on both hierarchical method (selection of reference groupings in medical dictionary for regulatory activities [MedDRA]) and/or textual method (string search), for case reports extraction from a pharmacovigilance database in response to a safety problem. Here we take cardiac valve fibrosis as an example. METHODS: The list of terms obtained by an automated approach, based on querying ontology of adverse drug reactions (OntoADR), a knowledge base defining MedDRA terms through relationships with systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts, was compared with the reference list consisting of 53 preferred terms obtained by hierarchical and textual method. Two queries were performed on OntoADR by using a dedicated software: OntoADR query tools. Both queries excluded congenital diseases, and included a procedure or an auscultation method performed on cardiac valve structures. Query 1 also considered MedDRA terms related to fibrosis, narrowing or calcification of heart valves, and query 2 MedDRA terms described according to one of these four SNOMED CT terms: "Insufficiency", "Valvular sclerosis", "Heart valve calcification" or "Heart valve stenosis". RESULTS: The reference grouping consisted of 53 MedDRA preferred terms. Our automated method achieved recall of 79% and precision of 100% for query 1 privileging morphological abnormalities, and recall of 100% and precision of 96% for query 2 privileging functional abnormalities. CONCLUSION: An alternative approach to MedDRA reference groupings for building custom groupings is feasible for cardiac valve fibrosis. OntoADR is still in development. Its application to other adverse reactions would require significant work for a knowledge engineer to define every MedDRA term, but such definitions could then be queried as many times as necessary by pharmacovigilance professionals.

Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review.

BACKGROUND: The underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients' experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance. OBJECTIVE: A scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance. METHODS: Daubt et al's recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2. RESULTS: Of the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified. CONCLUSIONS: This scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.

[Drug-related anaphylactic shocks: under-reporting and PMSI]. / Chocs anaphylactiques d'origine médicamenteuse: sous-notification et PMSI.

AIM: To evaluate the value of research in the case-mix database to identify cases of drug-related anaphylactic or anaphylactoid shock. METHODS: Hospital stays of patients discharged from the University Hospital of Saint-Étienne between July 1st 2009 and June 30th 2012. Five codes from the international classification of diseases were selected: T88.6, T88.2, J39.3, T80.5 and T78.2. RESULTS: Among 89 cases identified by the programme for medicalization of information system (programme de médicalisation des systèmes d'information, PMSI), 40 were selected (45%). Of these, 16 cases were spontaneously reported by physicians. The unspecific code "anaphylactic shock unspecified (T78.2)" was coded for 57.5% of cases. CONCLUSION: The study confirms the interest of the PMSI as a tool for health monitoring, in addition to spontaneous reporting. Nevertheless, coding with insufficient precision about the causal role of the drug, requires a return to the medical record and so an important time consuming process.

[Not Available]. / Chocs anaphylactiques d'origine médicamenteuse : sous-notification et PMSI.

AIM: To evaluate the value of research in the case-mix database to identify cases of drug-related anaphylactic or anaphylactoid shock. METHODS: Hospital stays of patients discharged from the University Hospital of Saint-Étienne between July 1st 2009 and June 30th 2012. Five codes from the international classification of diseases were selected: T88.6, T88.2, J39.3, T80.5 and T78.2. RESULTS: Among 89 cases identified by the programme for medicalization of information system (programme de médicalisation des systèmes d'information, PMSI), 40 were selected (45%). Of these, 16 cases were spontaneously reported by physicians. The unspecific code "anaphylactic shock unspecified (T78.2)" was coded for 57.5% of cases. CONCLUSION: The study confirms the interest of the PMSI as a tool for health monitoring, in addition to spontaneous reporting. Nevertheless, coding with insufficient precision about the causal role of the drug, requires a return to the medical record and so an important time consuming process.

Semantic Queries Expedite MedDRA Terms Selection Thanks to a Dedicated User Interface: A Pilot Study on Five Medical Conditions.

Background: Searching into the MedDRA terminology is usually limited to a hierarchical search, and/or a string search. Our objective was to compare user performances when using a new kind of user interface enabling semantic queries versus classical methods, and evaluating term selection improvement in MedDRA. Methods: We implemented a forms-based web interface: OntoADR Query Tools (OQT). It relies on OntoADR, a formal resource describing MedDRA terms using SNOMED CT concepts and corresponding semantic relations, enabling terminological reasoning. We then compared time spent on five examples of medical conditions using OQT or the MedDRA web-based browser (MWB), and precision and recall of the term selection. Results: OntoADR Query Tools allows the user to search in MedDRA: One may enter search criteria by selecting one semantic property from a dropdown list and one or more SNOMED CT concepts related to the range of the chosen property. The user is assisted in building his query: he can add criteria and combine them. Then, the interface displays the set of MedDRA terms matching the query. Meanwhile, on average, the time spent on OQT (about 4 min 30 s) is significantly lower (-35%; p < 0.001) than time spent on MWB (about 7 min). The results of the System Usability Scale (SUS) gave a score of 62.19 for OQT (rated as good). We also demonstrated increased precision (+27%; p = 0.01) and recall (+34%; p = 0.02). Computed "performance" (correct terms found per minute) is more than three times better with OQT than with MWB. Discussion: This pilot study establishes the feasibility of our approach based on our initial assumption: performing MedDRA queries on the five selected medical conditions, using terminological reasoning, expedites term selection, and improves search capabilities for pharmacovigilance end users. Evaluation with a larger number of users and medical conditions are required in order to establish if OQT is appropriate for the needs of different user profiles, and to check if conclusions can be extended to other kinds of medical conditions. The application is currently limited by the non-exhaustive coverage of MedDRA by OntoADR, but nevertheless shows good performance which encourages continuing in the same direction.

Evaluating Twitter as a complementary data source for pharmacovigilance.

BACKGROUND: Social media are currently considered as a potential complementary source of knowledge for drug safety surveillance. Our primary objective was to estimate the frequency of adverse drug reactions (ADRs) experienced by Twitter users. Our secondary objective was to determine whether tweets constitute a valuable and informative source of data for pharmacovigilance purposes, despite limitations on character number per tweet. RESEARCH DESIGN AND METHODS: We selected a list of 33 drugs subject to careful monitoring due to safety concern in France and Europe, and extracted tweets using the streaming API from 30 September 2014 to 5 April 2015. Two pharmacovigilance centers classified these tweets manually as potential ADR case reports. RESULTS: Among 10,534 tweets, 848 (8.05%) implied or mentioned an ADR without meeting the four FDA criteria required for reporting an ADR, and 289 (2.74%) tweets were classified as 'case reports.' Among them 20 (7.27%) tweets mentioned an unexpected ADR and 33 (11.42%) tweets mentioned a serious ADR. CONCLUSIONS: With the use of dedicated tools, Twitter could become a complementary source of information for pharmacovigilance, despite a major limitation regarding causality assessment of ADRs in individual tweets, which may improve with the new limitation to 280 characters per tweet.

MedDRA® automated term groupings using OntoADR: evaluation with upper gastrointestinal bleedings.

OBJECTIVE: To propose a method to build customized sets of MedDRA terms for the description of a medical condition. We illustrate this method with upper gastrointestinal bleedings (UGIB). RESEARCH DESIGN AND METHODS: We created a broad list of MedDRA terms related to UGIB and defined a gold standard with the help of experts. MedDRA terms were formally described in a semantic resource named OntoADR. We report the use of two semantic queries that automatically select candidate terms for UGIB. Query 1 is a combination of two SNOMED CT concepts describing both morphology 'Hemorrhage' and finding site 'Upper digestive tract structure'. Query 2 complements Query 1 by taking into account MedDRA terms associated to SNOMED CT concepts describing clinical manifestations 'Melena' or 'Hematemesis'. RESULTS: We compared terms in queries and our gold standard achieving a recall of 71.0% and a precision of 81.4% for query 1 (F1 score 0.76); and a recall of 96.7% and a precision of 77.0% for query 2 (F1 score 0.86). CONCLUSIONS: Our results demonstrate the feasibility of applying knowledge engineering techniques for building customized sets of MedDRA terms. Additional work is necessary to improve precision and recall, and confirm the interest of the proposed strategy.

Inconsistencies Between Antiparkinsonian Drugs and ICD-10 Codes in Inpatients: A TOLBIAC Project Case Study.

In France, data derived from hospital information systems are adequate to feed the prospective payment system. The consistency between drugs prescribed to patients and their indications could solve difficulties related to the identification of ICD-10 undercoded chronic diseases as the Parkinson Disease. Our goal was to highlight patients' stays mentioning administration of antiparkinsonian drugs and not coded for Parkinson's disease. Our approach was to parameterize tables of associations between ICD-10 codes and drug identifiers in the Web100T® application that collects medical information in our hospital and displays related inconsistencies for patients' stays. Based on acute care patients' stays of the second semester of 2015, we identified 246 patients corresponding to 253 stays, for which 33% of stays were not coded with the ICD-10 G20 code of the Parkinson's disease. The precision of our approach was 29%. Based on these data we predict roughly 84 patient stays without mention of Parkinson Disease. We plan to extend this study to other drugs and other kinds of data available in the health information system, such as biology or medical devices in order to improve the coding of chronic diseases in our hospital.

Improvement of Diagnosis Coding by Analysing EHR and Using Rule Engine: Application to the Chronic Kidney Disease.

Coding medical diagnosis in case mix databases is a time-consuming task as every information available in patient records has to be taken into account. We developed rules based on EHR data with the Drools rules engine in order to support diagnosis coding of chronic kidney disease (CKD) in our hospital. 520 patients had a GFR < 60 ml/min as estimated by the Cockroft-Gault formula and corresponded to 429 case mix database entries. We compared stays in which the patient was older than 12 and younger than 65 or 80 at the time of the stay. We concluded that our rules engine implementation may improve coding of CKD for 45.6% of patients with a GFR < 60 ml/min and younger than 65. When patients are older than 65 our rule engine may be less useful for suggesting missing codes of CKD because the estimation of GFR by the Cockroft-Gault formula becomes less reliable as patients get older.

Ci4SeR--curation interface for semantic resources--evaluation with adverse drug reactions.

Evaluation and validation have become a crucial problem for the development of semantic resources. We developed Ci4SeR, a Graphical User Interface to optimize the curation work (not taking into account structural aspects), suitable for any type of resource with lightweight description logic. We tested it on OntoADR, an ontology of adverse drug reactions. A single curator has reviewed 326 terms (1020 axioms) in an estimated time of 120 hours (2.71 concepts and 8.5 axioms reviewed per hour) and added 1874 new axioms (15.6 axioms per hour). Compared with previous manual endeavours, the interface allows increasing the speed-rate of reviewed concepts by 68% and axiom addition by 486%. A wider use of Ci4SeR would help semantic resources curation and improve completeness of knowledge modelling.