Risk of Drug-Drug Interactions in Out-Hospital Drug Dispensings in France: Results From the DRUG-Drug Interaction Prevalence Study

Introduction: Drug interactions could account for 1% of hospitalizations in the general population and 2-5% of hospital admissions in the elderly. However, few data are available on the drugs concerned and the potential severity of the interactions encountered. We thus first aimed to estimate the prevalence of dispensings including drugs Contraindicated or Discommended because of Interactions (CDI codispensings) and to identify the most frequently involved drug pairs. Second, we aimed to investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved. Methods: We carried out a study using a random sample of all drugs dispensings registered in a database of the French Health Insurance System between 2010 and 2015. The distribution of the drugs involved was described considering active principles, detailing the 20 most frequent ones for both contraindicated or discommended codispensings (DCs). To investigate whether the frequency of CDI codispensings appeared higher or lower than the expected for the drugs involved, we developed a specific indicator, the Drug-drug interaction prevalence study-score (DIPS-score), that compares for each drug pair the observed frequency of codispensing to its expected probability. The latter is determined considering the frequencies of dispensings of the individual drugs constituting a pair of interest. Results: We analyzed 6,908,910 dispensings: 13,196 (0.2%) involved contraindicated codispensings (CCs), and 95,410 (1.4%) DCs. For CCS, the most frequently involved drug pair was "bisoprolol+flecainide" = 5,036); four out of five of the most represented pairs involved cardiovascular drugs. For DCS, the most frequently involved drug pair was "ramipril+spironolactone" = 4,741); all of the five most represented pairs involved cardiovascular drugs. The drug pair involved in the CC with the highest score value was "citalopram+hydroxyzine" (DIPS-score: 3.7; 2.9-4.6); that with the lowest score was "clarithromycin+simvastatin" (DIPS-score: 0.2; 0.2-0.3). DIPS-score median value was 0.4 for CCs and 0.6 for DCs. Conclusion: This high prevalence of CDI codispensings enforces the need for further risk-prevention actions regarding drug-drug interactions (DDIs), especially for arrhythmogenic or anti-arrhythmic drugs. In this perspective, the DIPS-score we develop could ease identifying the interactions that are poorly considered by clinicians/pharmacists and targeting interventions

Stud Health Technol Inform

Clinical information in electronic health records (EHRs) is mostly unstructured. With the ever-increasing amount of information in patients' EHRs, manual extraction of clinical information for data reuse can be tedious and time-consuming without dedicated tools. In this paper, we present SmartCRF, a prototype to visualize, search and ease the extraction and structuration of information from EHRs stored in an i2b2 data warehouse

Yearb Med Inform

OBJECTIVES: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. METHODS: The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 15 candidate best papers. These papers were then peer-reviewed by external reviewers to give the editorial team an enlightened selection of the best papers. RESULTS: Among the 805 references retrieved from PubMed and Web of Science, three were finally selected as best papers. All three papers are about surveillance using digital tools. One study is about the surveillance of flu, another about emerging animal infectious diseases and the last one is about foodborne illness. The sources of information are Google news, Twitter, and Yelp restaurant reviews. Machine learning approaches are most often used to detect signals. CONCLUSIONS: Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge

Stud Health Technol Inform

Secondary use of health data is made difficult in part because of large semantic heterogeneity. Many efforts are being made to align local terminologies with international standards. With increasing concerns about data privacy, we focused here on the use of machine learning methods to align biological data elements using aggregated features that could be shared as open data. A 3-step methodology (features engineering, blocking strategy and supervised learning) was proposed. The first results, although modest, are encouraging for the future development of this approach

Revue d'Épidémiologie et de Santé Publique

Introduction L’analyse des données de vie réelle permet d’étudier l’utilisation des médicaments après leur mise sur le marché. Le projet Domino a pour objectif de détecter automatiquement les mésusages médicamenteux mentionnés sur les médias sociaux, tels que la prise de médicaments pour une mauvaise indication ou en présence d’une contre-indication. Méthodes La première étape a consisté à extraire automatiquement les fils de discussion sur les forums en santé au format HTML puis de les transformer au format SIOC, un standard issu du web sémantique pour représenter les discussions en ligne. Une terminologie ouverte du médicament, Romedi, a été développée et utilisée pour filtrer les messages mentionnant au moins un médicament. Une étape de reconnaissance d’entités nommées basée sur le métathésaurus de l’UMLS a permis d’extraire automatiquement les symptômes et les maladies. Ces derniers ont ensuite été comparés aux indications présentes dans les résumés des caractéristiques du produit (RCP). Un signal de mésusage a été généré lorsqu’un symptôme ou une maladie hors indication étaient fréquemment associés à un médicament. Une interface a été développée pour visualiser les signaux générés par l’algorithme afin de les valider ou de les invalider par un expert du domaine. Résultats Parmi 5 millions de messages extraits des réseaux sociaux, environ 189 000 contenaient la mention d’au moins un médicament. Les 1000 premiers signaux générés par l’algorithme ont été revus par un pharmacien. Au total, 26 signaux de mésusage ont été validés manuellement. Le principal mésusage détecté concernait la doxylamine, traitement contre les insomnies occasionnelles utilisé comme antiémétique. Les nombreux faux positifs reflètent la difficulté de comparer le langage médical utilisé dans les RCP et le langage patient, imprécis, utilisé sur les forums. Discussion/conclusion L’analyse des messages publiés sur les réseaux sociaux est capable d’identifier des mésusages médicamenteux. La principale difficulté pour la détection du mésusage réside dans l’identification du motif de la prise d’un médicament dans le langage patient. D’autres approches devront être développées pour améliorer ces résultats

Evaluating the Relevance of Virtual Drugs

International audienceInformation about drugs is numerous and varied, and many drugs can share the same information. Grouping drugs that have common characteristics can be useful to avoid redundancy and facilitate interoperability. Our work focused on the evaluation of the relevance of classes allowing this type of grouping: the "Virtual Drug". Thus, in this paper, we describe the process of creating this class from the data of the French Public Drug Database, which is then evaluated against the codes of the Anatomical Therapeutic Chemical classification associated with the drugs. Our evaluation showed that 99.55% of the "Virtual Drug" classes have a good intra-class consistency

Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning

INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information. MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons. RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively. CONCLUSION: In this study, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning

NPJ Digit Med

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions