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.

Descriptions of Adverse Drug Reactions Are Less Informative in Forums Than in the French Pharmacovigilance Database but Provide More Unexpected Reactions.

Background: Social media have drawn attention for their potential use in Pharmacovigilance. Recent work showed that it is possible to extract information concerning adverse drug reactions (ADRs) from posts in social media. The main objective of the Vigi4MED project was to evaluate the relevance and quality of the information shared by patients on web forums about drug safety and its potential utility for pharmacovigilance. Methods: After selecting websites of interest, we manually evaluated the relevance of the content of posts for pharmacovigilance related to six drugs (agomelatine, baclofen, duloxetine, exenatide, strontium ranelate, and tetrazepam). We compared forums to the French Pharmacovigilance Database (FPVD) to (1) evaluate whether they contained relevant information to characterize a pharmacovigilance case report (patient's age and sex; treatment indication, dose and duration; time-to-onset (TTO) and outcome of the ADR, and drug dechallenge and rechallenge) and (2) perform impact analysis (nature, seriousness, unexpectedness, and outcome of the ADR). Results: The cases in the FPVD were significantly more informative than posts in forums for patient description (age, sex), treatment description (dose, duration, TTO), and outcome of the ADR, but the indication for the treatment was more often found in forums. Cases were more often serious in the FPVD than in forums (46% vs. 4%), but forums more often contained an unexpected ADR than the FPVD (24% vs. 17%). Moreover, 197 unexpected ADRs identified in forums were absent from the FPVD and the distribution of the MedDRA System Organ Classes (SOCs) was different between the two data sources. Discussion: This study is the first to evaluate if patients' posts may qualify as potential and informative case reports that should be stored in a pharmacovigilance database in the same way as case reports submitted by health professionals. The posts were less informative (except for the indication) and focused on less serious ADRs than the FPVD cases, but more unexpected ADRs were presented in forums than in the FPVD and their SOCs were different. Thus, web forums should be considered as a secondary, but complementary source for pharmacovigilance.

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.

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.

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.

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.

Evaluation of Internet Social Networks using Net scoring Tool: A Case Study in Adverse Drug Reaction Mining.

BACKGROUND AND OBJECTIVES: Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary tool to already existing ADRs signal detection processes. However, several studies have shown that the quality of medical information published online varies drastically whatever the health topic addressed. The aim of this study is to use an existing rating tool on a set of social network web sites in order to assess the capabilities of these tools to guide experts for selecting the most adapted social network web site to mine ADRs. METHODS: First, we reviewed and rated 132 Internet forums and social networks according to three major criteria: the number of visits, the notoriety of the forum and the number of messages posted in relation with health and drug therapy. Second, the pharmacist reviewed the topic-oriented message boards with a small number of drug names to ensure that they were not off topic. Six experts have been chosen to assess the selected internet forums using a French scoring tool: Net scoring. Three different scores and the agreement between experts according to each set of scores using weighted kappa pooled using mean have been computed. RESULTS: Three internet forums were chosen at the end of the selection step. Some criteria get high score (scores 3-4) no matter the website evaluated like accessibility (45-46) or design (34-36), at the opposite some criteria always have bad scores like quantitative (40-42) and ethical aspect (43-44), hyperlinks actualization (30-33). Kappa were positives but very small which corresponds to a weak agreement between experts. CONCLUSION: The personal opinion of the expert seems to have a major impact, undermining the relevance of the criterion. Our future work is to collect results given by this evaluation grid and proposes a new scoring tool for Internet social networks assessment.

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.