Visualizing omics and clinical data: Which challenges for dealing with their variety?

Life sciences are currently going through a great number of transformations raised by the in-going revolution in high-throughput technologies for the acquisition of data. The integration of their high dimensionality, ranging from omics to clinical data, is becoming one of the most challenging stages. It involves inter-disciplinary developments with the aim to move towards an enhanced understanding of human physiology for caring purposes. Biologists, bioinformaticians, physicians and other experts related to the healthcare domain have to accompany each step of the analysis process in order to investigate and expertise these various data. In this perspective, methods related to information visualization are gaining increasing attention within life sciences. The softwares based on these methods are now well recognized to facilitate expert users' success in carrying out their data analysis tasks. This article aims at reviewing the current methods and techniques dedicated to information visualisation and their current use in software development related to omics or/and clinical data.

Appropriateness of psychotropic drug prescriptions in the elderly: structuring tools based on data extracted from the hospital information system to understand physician practices.

BACKGROUND: The appropriateness of psychotropic prescriptions in the elderly is a major quality-of-care challenge at hospital. Quality indicators have been developed to prevent inappropriate psychotropic prescriptions. We aimed to select and automatically calculate such indicators, from the Bordeaux University Hospital information system, and to analyze the appropriateness of psychotropic prescription practices, in an observational study. METHODS: Experts selected indicators of the appropriateness of psychotropic prescriptions in hospitalized elderly patients, according to guidelines from the French High Authority for Health. The indicators were reformulated to focus on psychotropic administrations. The automated calculation of indicators was analyzed by comparing their measure to data collected from a clinical audit. In elderly patients hospitalized between 2014 and 2015, we then analyzed the evolution of the appropriateness of psychotropic prescription practices during hospital stay, using methods of visualization, and described practices by considering patients' characteristics. RESULTS: Two indicators were automated to detect overuse and misuse of psychotropic drugs. Indicators identified frequent inappropriate drug administrations, but practices tended to become more appropriate after quality-of-care improvement actions. In the majority of patients (85%), there was no inappropriate administration of psychotropic drugs during hospital stay; for the remaining 15% with at least one inappropriate administration, physicians tended to limit overuse or misuse during hospital stay. Inappropriate administrations were more frequent in patients suffering from psychiatric disorders, dependence and associated complications or morbidities. CONCLUSIONS: The automated indicators are structuring tools for the development of a drug prescription monitoring system. Inappropriate psychotropic administrations were limited by physicians during hospital stay; some inappropriate prescriptions might be explained by clinical characteristics of patients.

Timeline representation of clinical data: usability and added value for pharmacovigilance.

BACKGROUND: Pharmacovigilance consists in monitoring and preventing the occurrence of adverse drug reactions (ADR). This activity requires the collection and analysis of data from the patient record or any other sources to find clues of a causality link between the drug and the ADR. This can be time-consuming because often patient data are heterogeneous and scattered in several files. To facilitate this task, we developed a timeline prototype to gather and classify patient data according to their chronology. Here, we evaluated its usability and quantified its contribution to routine pharmacovigilance using real ADR cases. METHODS: The timeline prototype was assessed using the biomedical data warehouse eHOP (from entrepôt de données biomédicales de l'HOPital) of the Rennes University Hospital Centre. First, the prototype usability was tested by six experts of the Regional Pharmacovigilance Centre of Rennes. Their experience was assessed with the MORAE software and a System and Usability Scale (SUS) questionnaire. Then, to quantify the timeline contribution to pharmacovigilance routine practice, three of them were asked to investigate possible ADR cases with the "Usual method" (analysis of electronic health record data with the DxCare software) or the "Timeline method". The time to complete the task and the data quality in their reports (using the vigiGrade Completeness score) were recorded and compared between methods. RESULTS: All participants completed their tasks. The usability could be considered almost excellent with an average SUS score of 82.5/100. The time to complete the assessment was comparable between methods (P = 0.38) as well as the average vigiGrade Completeness of the data collected with the two methods (P = 0.49). CONCLUSIONS: The results showed a good general level of usability for the timeline prototype. Conversely, no difference in terms of the time spent on each ADR case and data quality was found compared with the usual method. However, this absence of difference between the timeline and the usual tools that have been in use for several years suggests a potential use in pharmacovigilance especially because the testers asked to continue using the timeline after the evaluation.

Requests for post-registration studies (PRS), patients follow-up in actual practice: Changes in the role of databases.

Early market access of health products is associated with a larger number of requests for information by the health authorities. Compared with these expectations, the growing expansion of health databases represents an opportunity for responding to questions raised by the authorities. The computerised nature of the health system provides numerous sources of data, and first and foremost medical/administrative databases such as the French National Inter-Scheme Health Insurance Information System (SNIIRAM) database. These databases, although developed for other purposes, have already been used for many years with regard to post-registration studies (PRS). The use thereof will continue to increase with the recent creation of the French National Health Data System (SNDS [2016 health system reform law]). At the same time, other databases are available in France, offering an illustration of "product use under actual practice conditions" by patients and health professionals (cohorts, specific registries, data warehouses, etc.). Based on a preliminary analysis of requests for PRS, approximately two-thirds appeared to have found at least a partial response in existing databases. Using these databases has a number of disadvantages, but also numerous advantages, which are listed. In order to facilitate access and optimise their use, it seemed important to draw up recommendations aiming to facilitate these developments and guarantee the conditions for their technical validity. The recommendations drawn up notably include the need for measures aiming to promote the visibility of research conducted on databases in the field of PRS. Moreover, it seemed worthwhile to promote the interoperability of health data warehouses, to make it possible to match information originating from field studies with information originating from databases, and to develop and share algorithms aiming to identify criteria of interest (proxies). Methodological documents, such as the French National Authority for Health (HAS) recommendations on "Les études post-inscription sur les technologies de santé (médicaments, dispositifs médicaux et actes). Principes et méthodes" [Post-registration studies on health technologies (medicinal products, medical devices and procedures). Principles and methods] should be updated to incorporate these developments.

Protocole of a controlled before-after evaluation of a national health information technology-based program to improve healthcare coordination and access to information.

BACKGROUND: Improvement of coordination of all health and social care actors in the patient pathways is an important issue in many countries. Health Information (HI) technology has been considered as a potentially effective answer to this issue. The French Health Ministry first funded the development of five TSN ("Territoire de Soins Numérique"/Digital health territories) projects, aiming at improving healthcare coordination and access to information for healthcare providers, patients and the population, and at improving healthcare professionals work organization. The French Health Ministry then launched a call for grant to fund one research project consisting in evaluating the TSN projects implementation and impact and in developing a model for HI technology evaluation. METHODS: EvaTSN is mainly based on a controlled before-after study design. Data collection covers three periods: before TSN program implementation, during early TSN program implementation and at late TSN program implementation, in the five TSN projects' territories and in five comparison territories. Three populations will be considered: "TSN-targeted people" (healthcare system users and people having characteristics targeted by the TSN projects), "TSN patient users" (people included in TSN experimentations or using particular services) and "TSN professional users" (healthcare professionals involved in TSN projects). Several samples will be made in each population depending on the objective, axis and stage of the study. Four types of data sources are considered: 1) extractions from the French National Heath Insurance Database (SNIIRAM) and the French Autonomy Personalized Allowance database, 2) Ad hoc surveys collecting information on knowledge of TSN projects, TSN program use, ease of use, satisfaction and understanding, TSN pathway experience and appropriateness of hospital admissions, 3) qualitative analyses using semi-directive interviews and focus groups and document analyses and 4) extractions of TSN implementation indicators from TSN program database. DISCUSSION: EvaTSN is a challenging French national project for the production of evidenced-based information on HI technologies impact and on the context and conditions of their effectiveness and efficiency. We will be able to support health care management in order to implement HI technologies. We will also be able to produce an evaluation toolkit for HI technology evaluation. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT02837406 , 08/18/2016.

Variable selection on large case-crossover data: application to a registry-based study of prescription drugs and road traffic crashes.

PURPOSE: In exploratory analyses of pharmacoepidemiological data from large populations with large number of exposures, both a conceptual and computational problem is how to screen hypotheses using probabilistic reasoning, selecting drug classes or individual drugs that most warrant further hypothesis testing. METHODS: We report the use of a shrinkage technique, the Lasso, in the exploratory analysis of the data on prescription drugs and road traffic crashes, resulting from the case-crossover matched-pair interval approach described by Orriols and colleagues (PLoS Med 2010; 7:e1000366). To prevent false-positive results, we consider a bootstrap-enhanced version of the Lasso. To highlight the most stable results, we extensively examine sensitivity to the choice of referent window. RESULTS: Antiepileptics, benzodiazepine hypnotics, anxiolytics, antidepressants, antithrombotic agents, mineral supplements, drugs used in diabetes, antiparkinsonian treatment, and several cardiovascular drugs showed suspected associations with road traffic accident involvement or accident responsibility. CONCLUSION: These results, in relation to other findings in the literature, provide new insight and may generate new hypotheses on the association between prescription drugs use and impaired driving ability.

Pilot evaluation of an automated method to decrease false-positive signals induced by co-prescriptions in spontaneous reporting databases.

PURPOSE: To test an automated method to decrease the number of false-positive (FP) signals of disproportionate reportings (SDRs) generated by co-prescription. METHODS: Automated backward stepwise removal of reports concerning the drug associated with the highest ranked SDR for an event was tested for gastric and oesophageal haemorrhages (GOH), central nervous system haemorrhages and cerebrovascular accidents (CNSH), ischaemic coronary artery disorders and muscle pains (MP) using the reporting odds ratio in the French spontaneous reporting research database. After ranking SDRs detected in the complete dataset on the lower limit of the reporting odds ratio 95% confidence interval, reports concerning the drug with the highest ranked SDR were removed. In the dataset thus generated, SDRs were again identified, ranked and reports related to the drug involved in the newly highest ranked SDR removed. The process was repeated until no signal was detected. Initially detected SDRs eliminated using this technique were assessed regarding the summary of products characteristics and the literature to determine their FP nature. RESULTS: Seventeen SDRs were successively eliminated for GOH, 37 for CNSH, 15 for ischaemic coronary artery disorders, and 36 for MP. Four were FP for GOH, 29 for CNSH, 7 for ACI and none were FP for MP. The positive predictive value of the backward stepwise removal procedure in identifying FP SDRs ranged from 0% (MP) to 78.4% (CNSH). CONCLUSIONS: Although further adjustment is needed to improve the method presented herein, our results suggest that numerous FP signals because of co-prescription bias could be eliminated using an automated method.

Prescription-drug-related risk in driving: comparing conventional and lasso shrinkage logistic regressions.

BACKGROUND: Large data sets with many variables provide particular challenges when constructing analytic models. Lasso-related methods provide a useful tool, although one that remains unfamiliar to most epidemiologists. METHODS: We illustrate the application of lasso methods in an analysis of the impact of prescribed drugs on the risk of a road traffic crash, using a large French nationwide database (PLoS Med 2010;7:e1000366). In the original case-control study, the authors analyzed each exposure separately. We use the lasso method, which can simultaneously perform estimation and variable selection in a single model. We compare point estimates and confidence intervals using (1) a separate logistic regression model for each drug with a Bonferroni correction and (2) lasso shrinkage logistic regression analysis. RESULTS: Shrinkage regression had little effect on (bias corrected) point estimates, but led to less conservative results, noticeably for drugs with moderate levels of exposure. Carbamates, carboxamide derivative and fatty acid derivative antiepileptics, drugs used in opioid dependence, and mineral supplements of potassium showed stronger associations. CONCLUSION: Lasso is a relevant method in the analysis of databases with large number of exposures and can be recommended as an alternative to conventional strategies.

RAVEL: retrieval and visualization in ELectronic health records.

Because of the ever-increasing amount of information in patients' EHRs, healthcare professionals may face difficulties for making diagnoses and/or therapeutic decisions. Moreover, patients may misunderstand their health status. These medical practitioners need effective tools to locate in real time relevant elements within the patients' EHR and visualize them according to synthetic and intuitive presentation models. The RAVEL project aims at achieving this goal by performing a high profile industrial research and development program on the EHR considering the following areas: (i) semantic indexing, (ii) information retrieval, and (iii) data visualization. The RAVEL project is expected to implement a generic, loosely coupled to data sources prototype so that it can be transposed into different university hospitals information systems.

Evaluating the Relevance of Virtual Drugs.

Information 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.

Visualizing Food-Drug Interactions in the Thériaque Database.

This paper presents a prototype for the visualization of food-drug interactions implemented in the MIAM project, whose objective is to develop methods for the extraction and representation of these interactions and to make them available in the Thériaque database. The prototype provides users with a graphical visualization showing the hierarchies of drugs and foods in front of each other and the links between them representing the existing interactions as well as additional details about them, including the number of articles reporting the interaction. The prototype is interactive in the following ways: hierarchies can be easily folded and unfolded, a filter can be applied to view only certain types of interactions, and details about a given interaction are displayed when the mouse is moved over the corresponding link. Future work includes proposing a version more suitable for non-health professional users and the representation of the food hierarchy based on a reference classification.

Design and evaluation of a semantic approach for the homogeneous identification of events in eight patient databases: a contribution to the European EU-ADR project.

The overall objective of the EU-ADR project is the design, development, and validation of a computerised system that exploits data from electronic health records and biomedical databases for the early detection of adverse drug reactions. Eight different databases, containing health records of more than 30 million European citizens, are involved in the project. Unique queries cannot be performed across different databases because of their heterogeneity: Medical record and Claims databases, four different terminologies for coding diagnoses, and two languages for the information described in free text. The aim of our study was to provide database owners with a common basis for the construction of their queries. Using the UMLS, we provided a list of medical concepts, with their corresponding terms and codes in the four terminologies, which should be considered to retrieve the relevant information for the events of interest from the databases.

A semantic approach for the homogeneous identification of events in eight patient databases: a contribution to the European eu-ADR project.

The overall objective of the eu-ADR project is the design, development, and validation of a computerised system that exploits data from electronic health records and biomedical databases for the early detection of adverse drug reactions. Eight different databases, containing health records of more than 30 million European citizens, are involved in the project. Unique queries cannot be performed across different databases because of their heterogeneity: Medical record and Claims databases, four different terminologies for coding diagnoses, and two languages for the information described in free text. The aim of our study was to provide database owners with a common basis for the construction of their queries. Using the UMLS, we provided a list of medical concepts, with their corresponding terms and codes in the four terminologies, which should be considered to retrieve the relevant information for the events of interest from the databases.

Detecting Drug Non-Compliance in Internet Fora Using Information Retrieval and Machine Learning Approaches.

Non-compliance situations happen when patients do not follow their prescriptions and take actions that lead to potentially harmful situations. Although such situations are dangerous, patients usually do not report them to their physicians. Hence, it is necessary to study other sources of information. We propose to study online health fora. The purpose of our work is to explore online health fora with supervised classification and information retrieval methods in order to identify messages that contain drug non-compliance. The supervised classification method permits detection of non-compliance with up to 0.824 F-measure, while the information retrieval method permits detection non-compliance with up to 0.529 F-measure. For some fine-grained categories and new data, it shows up to 0.65-0.70 Precision.

Enabling West African Herbal-Based Traditional Medicine Digitizing: The WATRIMed Knowledge Graph.

The purpose of this study is to describe the design and development of the first release of the West African Herbal based Traditional Medicine Knowledge Graph (WATRIMed). It is a resource containing Traditional Medicine (TM) related entities and linked with publicly available knowledge bases in order to facilitate bringing West African TM into the digital world. The core model comprises currently 556 concepts including 143 identified West African medicinal plants and 108 recipes used by tradi-practitioners to treat 110 diseases and symptoms which are commonly encountered in this part of the world.

Design Considerations for a Knowledge Graph: The WATRIMed Use Case.

The World Health Organization estimates that as much as 80% of the population uses Traditional Medicine (TM) in some form, and in particular, herbal-based Traditional Medicine (HTM). However, TM is mostly orally transmitted and suffers from lack of standardizations and lack of computable TM data. Shareable standards could enable computational support of TM data management. In this paper, we outline the design and development of the West African Herbal Traditional Medicine (WATRIMed) Knowledge Graph (KG), which is an effort for bringing West Africa TM to the digital world and help establishing bridges with conventional medicine. WATRIMed entities have been enriched with knowledge from external publicly available knowledge bases and further mapped with the BioTopLite Upper Level Ontology. As of result, the model of the publicly available KG currently comprises 472 Concepts and 75 Properties (57 object properties and 18 data properties). It describes formally 115 medicinal plants, 179 chemical compounds and 67 recipes.

SmartCRF: A Prototype to Visualize, Search and Annotate an Electronic Health Record from an i2b2 Clinical Data Warehouse.

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.

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" (n = 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" (n = 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.

Romedi: An Open Data Source About French Drugs on the Semantic Web.

The W3C project, "Linking Open Drug Data" (LODD), linked several publicly available sources of drug data together. So far, French data, like marketed drugs and their summary of product characteristics, were not integrated and remained difficult to query. In this paper, we present Romedi (Référentiel Ouvert du Médicament), an open dataset that links French data on drugs to international resources. The principles and standard recommendations created by the W3C for sharing information were adopted. Romedi was connected to the Unified Medical Language System and DrugBank, two central resources of the LODD project. A SPARQL endpoint is available to query Romedi and services are provided to annotate textual content with Romedi terms. This paper describes its content, its services, its links to external resources, and expected future developments.