Description The EHR4CR central query workbench supports pharma and CRO users in various stages of the multi-centric clinical trial lifecycle ranging from protocol feasibility, over subject identification and recruitment to clinical trial execution and adverse event reporting. It offers an intuitive graphical user interface for building, managing and executing formalized eligibility criteria queries (Figure 1) to support protocol feasibility studies using real-life data residing in distributed clinical data warehouses as evidence. As these data warehouses reside at the clinical sites and no patient-level information is disclosed (only patient counts aggregated over demographic categories), patient privacy is respected. The EHR4CR central workbench facilitates the patient recruitment process by offering workflow-driven administration and monitoring of the recruitment process for multi-centric trials. The study manager can select clinical sites of interest and invite them to participate in the study. Study metadata including protocol definition and
Necessity for a novel form of interactive conferenceClinical trials are the foundation for the advancement of medical research, but they are also complex, time consuming and expensive.To ensure scientific validity, clinical trials must record large amounts of data on health and treatment of a carefully selected group of patients or trial participants.That is why a robust and highly flexible IT infrastructure that permits rapid reconfiguration to support different trials while maintaining consistent data models is needed to ensure that clinical data can be shared and used for analysis.Recently, EU projects, like TRANSFoRm, p-medicine, EHR4CR and BioMedBridges, are creating advanced clinical research information systems consisting of sets of tools that assist in the preparation and conduct of clinical trials and the re-use of care data for research purposes.These tools are also able to assemble data from heterogeneous sources to answer complex research questions, including functions for data mining and support of research process workflows to meet the needs of translational and personalized medical research [1].Because the translation of biomedical discoveries into clinical applications is an aim of the EU Commission, clinical research support is expected to be a major component of EU funded projects [2].In this context, the EU Framework Program 7 (FP7, http://ec.europa.eu/research/fp7/index_en.cfm)has promoted translational research, the so-called 'from bench-to-bedside' approach, that is expected to have practical benefits and improvements of the quality of life for patients.In this context, it is planned that many of the tools developed in ESFRI (European Strategy Forum on Research Infrastructures) and IMI projects will be used by ECRIN, the European Clinical Research Infrastructures Network [3] to advance clinical trials.To provide information on these new research tools and to evaluate their usability for the clinical research community, the CRI Solutions Day was organised by ECRIN together with other EUfunded projects (TRANSFoRm, EHR4CR, p-medicine, BioMedBridges and ECRIN-IA) providing interactive "hands-on" sessions to allow visitors to see these tools in action.The necessity for such a solutions day was based on the experience of ECRIN that the conventional, class room style presentation of research tools is not sufficient to demonstrate comparability and usability to many researchers.Thus, presenters from academia, research infrastructures and EU projects presented their developments with interactive sessions embedded in a framework of presentations and joint discussions.
Figure 1 User interface of the Quality Tool indicating results for three data types.The moving of the slide bars results in the selection of primary care sites where all criteria are met, allowing a researcher to weigh data quality against a desired number of practices.The tool is an example of how different metrics can be used to select data that is fit for purpose.
In many ESFRI infrastructures [1] and EU projects, tools to support data sharing and biomedical research are being developed. At the Clinical Research Informatics Solutions Day (May 26-27, 2014 at Heinrich-Heine University, Dusseldorf, Germany) new tool developments, their interoperability and sustainability issues in connection with EU projects, like BioMedBridges, BBMRI, ECRIN, EATRIS, ELIXIR, TRANSFoRm, p-medicine and EHR4CR, were discussed. These discussions resulted in three main insights: first, biomedical research increasingly needs the sharing of data from electronic health records (EHR), reinforcing the necessity for trust and privacy protection; second, requirements are increasing for semantic integration of data from very heterogeneous resources (clinical records, biosamples, images, genetic repositories); third, big data and health care data will be used more intensively to determine patient subgroups, for better patient recruitment and to improve trial feasibility [2]. The graphic method developed by the TRANSFoRm project to describe and analyse data privacy frameworks using a zone model [3] addresses this development and may be useful for all projects that have to deal with sharing care and research data. Tools and services that support similar functionalities were presented in joint workshops allowing developers and users to interact and compare tools. CRI has chosen this format to give developers and users opportunities to collaborate with each other to improve usability of tools. In separate workshops tools for clinical data management (OpenClinica, ObTiMA, VISTA), for bridging experimental and clinical research data (tranSMART, MOLGENIS, i2b2), for the reuse of EHR data for clinical research (Feasibility Service, Patient Screening Tool, Recruitment and Feasibility Tools); for advanced clinical data management (TRANSFoRm eCRF, mobile eHealth Solution), for imaging in clinical research (XNAT, DoctorEye), and for biobanking in clinical research (p-BioSPRE, Biobanking Catalogue, BBMRI Catalogue) were presented and discussed.
Description This tool supports administering and monitoring the recruitment process for multi-centric trials within a single clinical centre. It works together with the EHR4CR central workbench and allows receiving, accepting or rejecting invitations to participate in clinical trials (Figure 1). Local users can be assigned to clinical trials under a given role and engaged as such in different steps of the local subject identification and recruitment process. The tool also supports automatic fetching of a list of potential candidate patients for recruitment from a local clinical data warehouse based on formalized eligibility criteria included in the trial metadata. Once identified, potential candidate patients and their subsequent recruitment status can be managed and monitored (ranging from the status of potential candidate over confirmed candidate, patient in screening, patient has consented, etc.). Role-based re-identification of pseudonymised patient records avoids that the patient’s identity is exposed before the patient has been contacted by a treating physician and before the patient has agreed to participate in the recruitment process.
Description MOLGENIS/catalogue is a generic toolbox for building biobank and study catalogues and is used in BBMRI-NL, EU-BioSHaRE, EU-BioMedBridges, LifeLines, CTMM/ TraIT, Durrer Center, PALGA NL pathology network. The catalogue can host four levels of information: 1) Biobank/study descriptions using custom or MIABIS standard of BBMRI-ERIC format; 2) Data schema/data dictionary of data elements; 3) Aggregate data/sample availability counts and; 4) Individual level data ready for analysis.
Background Network biology currently focuses primarily on metabolic pathways, gene regulatory, and protein-protein interaction networks. While these approaches have yielded critical information, alternative methods to network analysis will offer new perspectives on biological information. A little explored area is the interactions between domains that can be captured using domain co-occurrence networks (DCN). A DCN can be used to study the function and interaction of proteins by representing protein domains and their co-existence in genes and by mapping cancer mutations to the individual protein domains to identify signals. Results The domain co-occurrence network was constructed for the human proteome based on PFAM domains in proteins. Highly connected domains in the central cores were identified using the k-core decomposition technique. Here we show that these domains were found to be more evolutionarily conserved than the peripheral domains. The somatic mutations for ovarian, breast and prostate cancer diseases were obtained from the TCGA database. We mapped the somatic mutations to the individual protein domains and the local false discovery rate was used to identify significantly mutated domains in each cancer type. Significantly mutated domains were found to be enriched in cancer disease pathways. However, we found that the inner cores of the DCN did not contain any of the significantly mutated domains. We observed that the inner core protein domains are highly conserved and these domains co-exist in large numbers with other protein domains. Conclusion Mutations and domain co-occurrence networks provide a framework for understanding hierarchal designs in protein function from a network perspective. This study provides evidence that a majority of protein domains in the inner core of the DCN have a lower mutation frequency and that protein domains present in the peripheral regions of the k-core contribute more heavily to the disease. These findings may contribute further to drug development.
Background The dynamics of metabolomics in establishing a prediction model using partial least square discriminant analysis have enabled better disease diagnosis; with emphasis on early detection of diseases. We attempted to translate the metabolomics model to predict the health status of the Orang Asli community whom we have little information. The metabolite expressions of the healthy vs. diseased patients (cardiovascular) were compared. A metabotype model was developed and validated using partial least square discriminant analysis (PLSDA). Cardiovascular risks of the Orang Asli were predicted and confirmed by biochemistry profiles conducted concurrently. Results Fourteen (14) metabolites were determined as potential biomarkers for cardiovascular risks with receiver operating characteristic of more than 0.7. They include 15S-HETE (AUC = 0.997) and phosphorylcholine (AUC = 0.995). Seven Orang Asli were clustered with the patients’ group and may have ongoing cardiovascular risks and problems. This is supported by biochemistry tests results that showed abnormalities in cholesterol, triglyceride, HDL and LDL levels. Conclusions The disease prediction model based on metabolites is a useful diagnostic alternative as compared to the current single biomarker assays. The former is believed to be more cost effective since a single sample run is able to provide a more comprehensive disease profile, whilst the latter require different types of sampling tubes and blood volumes.
Description VISTA Trials is a professional, affordable and efficient Clinical Data Management System (CDMS) developed by the European Organisation for Research and Treatment of Cancer (EORTC). It will be available to research organizations looking for a solution to run their multinational clinical trials. VISTA Trials has been used by EORTC for many years and is now being upgraded to CDISC standards and ECRIN requirements. It is a fully integrated and web-based solution applicable to all therapeutic areas. VISTA Trials is composed of 4 modules built around different functionalities: TrialDesign for database and intelligent eCRF design (with built-in logic), Clinical
Genome-wide RNA interference (RNAi) screening is an emerging and powerful technique for genetic screens, which can be divided into arrayed RNAi screen and pooled RNAi screen/selection based on different screening strategies. To date, several genome-wide RNAi screens have been successfully performed to identify host factors essential for influenza virus replication. However, the host factors identified by different research groups are not always consistent. Taking influenza virus screens as an example, we found that a number of screening parameters may directly or indirectly influence the primary hits identified by the screens. This review highlights the differences among the published genome-wide screening approaches and offers recommendations for performing a good pooled shRNA screen/selection.
As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including '-omics'-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.
Multidrug resistant tuberculosis (MDR-TB) is a serious form of tuberculosis (TB). There is no recognized effective treatment for MDR-TB, although there are a number of publications that have reported positive results for MDR-TB. We performed a network meta-analysis to assess the efficacy and acceptability of potential antitubercular drugs. We conducted a network meta-analysis of randomized controlled clinical trials to compare the efficacy and acceptability of five antitubercular drugs, bedaquiline, delamanid, levofloxacin, metronidazole and moxifloxacin in the treatment of MDR-TB. We included eleven suitable trials from nine journal articles and six clinical trials from ClinicalTrials.gov, with data for 1472 participants. Bedaquiline (odds ratio [OR] 2.69, 95% CI 1.02-7.43), delamanid (OR 2.45, 95% CI 1.36-4.89) and moxifloxacin (OR 2.47, 95% CI 1.01, 7.31) were significantly more effective than placebo. For efficacy, the results indicated no statistical significance between each antitubercular drug. For acceptability, the results indicated no statistically significant difference between each compared intervention. There is insufficient evidence to suggest that any one of the five antitubercular drugs (bedaquiline, delamanid, levofloxacin, metronidazole and moxifloxacin) has superior efficacy compared to the others.
Description The Query Workbench is a tool of the TRANSFoRm project to support clinical studies and database research. It provides an interface to author, store and deploy queries of clinical data to identify potential subjects for clinical studies and can thus support the clinical study feasibility evaluation. The development of the Query Workbench system is driven by the TRANSFoRm Clinical Research Information Model (CRIM) [1], enabling the creation of a semantically aware software tool for
Tool description The recruitment and feasibility tool (earlier named Yakobo) was developed in the EURECA project to assess protocol feasibility and to find eligible patients for clinical trials. Protocol feasibility functions analyze the feasibility of a trial protocol by assessing the expected patient enrollment rate of a selection of sites given the protocol’s eligibility criteria, based on “historical” data (existing patient data). It allows for answering questions such as whether inclusion and exclusion criteria are useful for defining the proper study population, whether it is likely that the necessary volume of patients can be recruited in time to collect data with sufficient statistical power and/or the expected duration of a trial. Criteria are expressed in a domain specific language [1]. A Trial metadata repository contains protocol definitions (Figure 1) and the SNAQL engine executes the DSL of the criteria to find patients belonging to the cohort. The SNAQL engine accesses the semantic integration services which provide a query interface [2] with reasoning abilities. Once a clinical protocol has been finalized, the tool is used to find patients eligible for enrollment.
For research in biomedical sciences, cross-domain searches through several different databases are an increasingly necessary task that often becomes a time consuming and labour-intense process. This is especially the case when different domain databases have to be combined, for example combined searches in clinical trials registries, publication databases and research databases. The Clinical Trial Information Mediator (CTIM) addresses this problem and offers a novel way for the combined search in ClinicalTrials.gov, PubMed and BioSamples. CTIM was developed based on a requirements analysis and implemented using open source technology. A search engine with a graphical user interface was developed in order to search linked data in the three databases ClinicalTrials.gov, PubMed and BioSamples; thereby enabling CTIM to bridge the gap between different knowledge domains of clinical trials, publications of research results and biosamples/genetic information. CTIM was applied in three use cases demonstrating that information retrieval could be considerably improved in sense for complex queries. These use cases show that more relevant results were obtained and more associated publications and biosamples could be retrieved in comparison to a separate single search. Main advantages of CTIM are identifying related information between clinical trials and publications employing a clinical trial centred kind of search, simplified access to its databases and thus reduced search time. In addition it can be used by researchers without prior training because of the intuitive usage.
Description The Open Source software i2b2 [1] provides a translational research platform for storing biomedical data and querying these data with a user-friendly interface for researchers (Figure 1). Despite its powerful features, it is lacking user-friendly tools for installation and configuration, the import of source data and the creation of a comprehensive navigational structure (i2b2 ontology). To close these gaps, the Integrated Data Repository Toolkit (IDRT), consisting of three software tools, has been created. The i2b2 Wizard provides a shell GUI for the installation and configuration of i2b2 instances, projects and users. The i2b2 Import Tool offers a GUI for browsing i2b2 projects and importing data in various standard data formats into i2b2 (e.g., textual (CSV), relational (SQL) or structured data (CDISC ODM/XML)), as well as a dedicated extractor for biomaterial data. During import, i2b2 ontologies are automatically created from metadata included in the source data. The i2b2 Ontology Editor (IOE), being part of the i2b2 Import Tool, can be used for enhancing these i2b2 ontologies. Besides standard functions like rearranging, adding, deleting and renaming folders and items, the IOE is capable of augmenting i2b2 ontologies with more advanced i2b2 functions. By utilizing the two windows of the IOE (one showing the unaltered source i2b2 ontology and the other the manually created target i2b2 ontology), mappings can be achieved by simple drag-and-drop operations. For example, start and end dates can be added to items by dragging a date item onto a fact item. Medical terminologies can easily be imported with the IDRT (e. g. ICD-10, LOINC) and can also be mapped via the same drag-and-drop operations to data elements (expandable beyond the supplied terminologies via a regular expression editor in the IOE). The IDRT tools support the more advanced i2b2 functionalities for “fact nesting”, called “modifiers”. Since the i2b2 web browser query application (i2b2 Web Client) does not support simple access and visualization of modifiers, an IDRT plugin was created that is able to display, combine and export related facts. Additional documentation for enhanced i2b2 usage is provided on the IDRT website [2].
• Study server – manages the connection between mobile and web applications and the TSS • Study database – stores all information about studies, patients, randomization etc. • Web application – enables filling in PROMs by patients and CROMs by GPs (investigators) • Mobile application – enables filling in PROMs by the patients using smartphones or other devices • Middleware ESB which serves as a connection, authorization and security layer between TSS and the rest of the infrastructure
