Clinical Data Management System Research Articles

Heterogeneity prevails: the state of clinical trial data management in Europe - results of a survey of ECRIN centres

BackgroundThe use of Clinical Data Management Systems (CDMS) has become essential in clinical trials to handle the increasing amount of data that must be collected and analyzed. With a CDMS trial data are captured at investigator sites with "electronic Case Report Forms". Although more and more of these electronic data management systems are used in academic research centres an overview of CDMS products and of available data management and quality management resources for academic clinical trials in Europe is missing.MethodsThe ECRIN (European Clinical Research Infrastructure Network) data management working group conducted a two-part standardized survey on data management, software tools, and quality management for clinical trials. The questionnaires were answered by nearly 80 centres/units (with an overall response rate of 47% and 43%) from 12 European countries and EORTC.ResultsOur survey shows that about 90% of centres have a CDMS in routine use. Of these CDMS nearly 50% are commercial systems; Open Source solutions don't play a major role. In general, solutions used for clinical data management are very heterogeneous: 20 different commercial CDMS products (7 Open Source solutions) in addition to 17/18 proprietary systems are in use. The most widely employed CDMS products are MACRO™ and Capture System™, followed by solutions that are used in at least 3 centres: eResearch Network™, CleanWeb™, GCP Base™ and SAS™. Although quality management systems for data management are in place in most centres/units, there exist some deficits in the area of system validation.ConclusionsBecause the considerable heterogeneity of data management software solutions may be a hindrance to cooperation based on trial data exchange, standards like CDISC (Clinical Data Interchange Standard Consortium) should be implemented more widely. In a heterogeneous environment the use of data standards can simplify data exchange, increase the quality of data and prepare centres for new developments (e.g. the use of EHR for clinical research). Because data management and the use of electronic data capture systems in clinical trials are characterized by the impact of regulations and guidelines, ethical concerns are discussed. In this context quality management becomes an important part of compliant data management. To address these issues ECRIN will establish certified data centres to support electronic data management and associated compliance needs of clinical trial centres in Europe.

Impact of Postoperative Pancreatic Fistula on Surgical Outcome—The Need for a Classification-driven Risk Management

BackgroundThe International Study Group of Pancreatic Fistula (ISGPF) classification allows comparison of incidence and severity of postoperative pancreatic fistula (POPF). Its post hoc character, however, does not provide a guideline for the treatment of POPF in individual patient. We therefore studied the association of POPF type A-C on secondary surgical morbidity and mortality in patients undergoing pancreatic resection. Patients and methodsBetween 3/2001-12/2007, 483 patients underwent pancreatic resections. POPF were classified according to the ISGPF classification. All patient data were entered in a clinical data management system prospectively. ResultsPatients who developed POPF had significantly more vascular but not other surgical complications than patients without POPF. Patients with POPF A had no vascular or surgical complications. Twenty one of the 29 patients with POPF C had surgical complications (17 vascular complications). Mortality attributed to surgical complications after POPF C was 5/29. A soft pancreatic consistency (OR 8.5; p < 0.008) and a high drain lipase activity on postoperative day 3 (OR 4.4; p = 0,065) were predictors for the development of POPF C. DiscussionPOPF C is associated with vascular complications like erosion bleeding and other surgical complications like delayed gastric emptying or pleural effusions. A soft pancreatic consistency and a high drain lipase activity on postoperative day 3 are early predictors for the development of POPF C.

Information technology in pharmacovigilance: Benefits, challenges, and future directions from industry perspectives

Risk assessment during clinical product development needs to be conducted in a thorough and rigorous manner. However, it is impossible to identify all safety concerns during controlled clinical trials. Once a product is marketed, there is generally a large increase in the number of patients exposed, including those with comorbid conditions and those being treated with concomitant medications. Therefore, postmarketing safety data collection and clinical risk assessment based on observational data are critical for evaluating and characterizing a product's risk profile and for making informed decisions on risk minimization. Information science promises to deliver effective e-clinical or e-health solutions to realize several core benefits: time savings, high quality, cost reductions, and increased efficiencies with safer and more efficacious medicines. The development and use of standard-based pharmacovigilance system with integration connection to electronic medical records, electronic health records, and clinical data management system holds promise as a tool for enabling early drug safety detections, data mining, results interpretation, assisting in safety decision making, and clinical collaborations among clinical partners or different functional groups. The availability of a publicly accessible global safety database updated on a frequent basis would further enhance detection and communication about safety issues. Due to recent high-profile drug safety problems, the pharmaceutical industry is faced with greater regulatory enforcement and increased accountability demands for the protection and welfare of patients. This changing climate requires biopharmaceutical companies to take a more proactive approach in dealing with drug safety and pharmacovigilance.

Using SAS®9 in clinical research

AbstractFor 30 years, SAS has been used in pharmaceutical research settings for data management, analytics and reporting. Traditionally, these roles were staffed by the BASE SAS programmer and the statistician. At the same time, dramatic shifts in the research informatics eco-system have occurred with advances in standards, such as CDISC, ICH and HL7, and the convergence of clinical data management systems and modernization strategies, such as electronic data capture and 21 CFR Part 11. This paper provides a broad based view of solutions SAS provides across the research information spectrum. We are at an interesting crossroads: on the one hand, the power of traditional SAS approaches found in BASE and STAT; on the other hand, a new range of technologies grown out of the SAS 9 platform, such as the Metadata Server, Data Integration Studio and SAS Business Intelligence products. The SAS Drug Development solution merges the accessibility of the Internet with the clinical trial workflow. Whether your focus is...

A statistical metadata model for clinical trials’ data management

We introduce a statistical, process-oriented metadata model to describe the process of medical research data collection, management, results analysis and dissemination. Our approach explicitly provides a structure for pieces of information used in Clinical Study Data Management Systems, enabling a more active role for any associated metadata. Using the object-oriented paradigm, we describe the classes of our model that participate during the design of a clinical trial and the subsequent collection and management of the relevant data. The advantage of our approach is that we focus on presenting the structural inter-relation of these classes when used during datasets manipulation by proposing certain transformations that model the simultaneous processing of both data and metadata. Our solution reduces the possibility of human errors and allows for the tracking of all changes made during datasets lifecycle. The explicit modeling of processing steps improves data quality and assists in the problem of handling data collected in different clinical trials. The case study illustrates the applicability of the proposed framework demonstrating conceptually the simultaneous handling of datasets collected during two randomized clinical studies. Finally, we provide the main considerations for implementing the proposed framework into a modern Metadata-enabled Information System.

Operationalization of the UFuRT methodology for usability analysis in the clinical research data management domain

Data management software applications specifically designed for the clinical research environment are increasingly available from commercial vendors and open-source communities, however, general-purpose spreadsheets remain widely employed in clinical research data management (CRDM). The suitability of spreadsheets for this use is controversial, and no formal comparative usability evaluations have been performed. We report on an application of the UFuRT (user, function, representation, and task (analyses) methodology to create a domain-specific process for usability evaluation. We demonstrate this process in an evaluation of differences in usability between a spreadsheet program (Microsoft® Excel) and a commercially available clinical research data management system (Phase Forward Clintrial™). Through this domain-specific operationalization of UFuRT methodology, we successfully identified usability differences and quantified task and cost differences, while differentiating these from socio-technical aspects. UFuRT can be generalized to other domains.

EZ-Entry: A clinical data management system

In this paper, a new data management system named EZ-Entry is introduced. Five major functions are enclosed in this system: (1) user authentication; (2) database construction; (3) double data entry with instant alignment; (4) revision tracking; (5) query management. The practical application performed on two clinical trials indicates that EZ-Entry meets the requirements of clinical data management with high efficiency and security. This software is freely available on request from the authors for academic purposes.

Could an Open-Source Clinical Trial Data-Management System Be What We Have All Been Looking For?

The authors argue that research organizations and funders should combine efforts to produce an open-source solution for trial data management.

On Educating about Medical Data Management

To summarize background, challenges, objectives, and methods for the usability of patient data, in particular with respect to their multiple use, and to point out how to lecture medical data management. Analyzing the literature, providing an example based on Simpson's paradox and summarizing research and education in the field of medical data management, respectively health information management (in German: Medizinische Dokumentation). For the multiple use of patient data, three main categories of use can be identified: patient-oriented (or casuistic) analysis, patient-group reporting, and analysis for clinical studies. A so-called documentation protocol, related to study plans in clinical trials, supports the multiple use of data from the electronic health record in order to obtain valid, interpretable results. Lectures on medical data management may contain modules on introduction, basic concepts of clinical data management and coding systems, important medical coding systems (e.g. ICD, SNOMED, TNM, UMLS), typical medical documentation systems (e.g. on patient records, clinical and epidemiological registers), utilization of clinical data management systems, planning of medical coding systems and of clinical data management systems, hospital information systems and the electronic patient record, and on data management in clinical studies. Usability, the ultimate goal of recording and managing patient data, requires, besides technical considerations, in addition appropriate methodology on medical data management, especially if data is intended to be used for multiple purposes, e.g. for patient care and quality management and clinical research. Medical data management should be taught in health and biomedical informatics programs.

Pivoting approaches for bulk extraction of Entity–Attribute–Value data

Entity-Attribute-Value (EAV) data, as present in repositories of clinical patient data, must be transformed (pivoted) into one-column-per-parameter format before it can be used by a variety of analytical programs. Pivoting approaches have not been described in depth in the literature, and existing descriptions are dated. We describe and benchmark three alternative algorithms to perform pivoting of clinical data in the context of a clinical study data management system. We conclude that when the number of attributes to be returned is not too large, it is feasible to use static SQL as the basis for views on the data. An alternative but more complex approach that utilizes hash tables and the presence of abundant random-access-memory can achieve improved performance by reducing the load on the database server.

Hemodynamic variables related to outcome in septic shock

To assess the impact of hemodynamic variables on the outcome of critically ill patients in septic shock and to identify the optimal threshold values related to outcome with special reference to continuously monitored mean arterial pressure (MAP) and mixed venous oxygen saturation (SvO2). Retrospective cohort study in a university hospital intensive care unit (ICU). All consecutive 111 patients with septic shock treated in our ICU between 1 Jan. 1999 and 30 Jan. 2002. The data on the hemodynamic and respiratory monitoring and circulation-related laboratory tests over the first 48 h of treatment in the ICU were collected from the clinical data management system. Data from 6 h and 48 h were analyzed separately. The 30-day mortality rate was 33% (36 of 111). Univariate analysis and forward stepwise logistic regression analysis were performed using the 30-day mortality as the primary endpoint. Mean MAP and lactate on arrival during 6 h, while mean MAP, the area of SvO2 under 70%, and mean CVP during 48 h were independently associated with mortality. MAP level of 65 mmHg and SvO2 of 70% had the highest areas under receiver characteristics curves. MAP, SvO2, CVP, and initial lactate were independently associated with mortality in septic shock, with threshold values supporting those published in recent guidelines.

Managing complex change in clinical study metadata

AbstractIn highly functional metadata-driven software, the interrelationships within the metadata become complex, and maintenance becomes challenging. We describe an approach to metadata management that uses a knowledge-base subschema to store centralized information about metadata dependencies and use cases involving specific types of metadata modification. Our system borrows ideas from production-rule systems in that some of this information is a high-level specification that is interpreted and executed dynamically by a middleware engine. Our approach is implemented in TrialDB, a generic clinical study data management system. We review approaches that have been used for metadata management in other contexts and describe the features, capabilities, and limitations of our system.

Integration of Web-based and PC-based Clinical Research Databases

We have created a Web-based repository or data library of information about measurement instruments used in studies of multi-factorial geriatric health conditions (the Geriatrics Research Instrument Library - GRIL) based upon existing features of two separate clinical study data management systems. GRIL allows browsing, searching, and selecting measurement instruments based upon criteria such as keywords and areas of applicability. Measurement instruments selected can be printed and/or included in an automatically generated standalone microcomputer database application, which can be downloaded by investigators for use in data collection and data management. Integration of database applications requires the creation of a common semantic model, and mapping from each system to this model. Various database schema conflicts at the table and attribute level must be identified and resolved prior to integration. Using a conflict taxonomy and a mapping schema facilitates this process. Critical conflicts at the table level that required resolution included name and relationship differences. A major benefit of integration efforts is the sharing of features and cross-fertilization of applications created for similar purposes in different operating environments. Integration of applications mandates some degree of metadata model unification.

Temporal query of attribute-value patient data: utilizing the constraints of clinical studies

We describe an interface and architecture for ad hoc temporal query of TrialDB, a clinical study data management system (CSDMS). A clinical study focuses primarily on the effect of therapy on a group of patients, who have individually enrolled in a study at different times. Relative times (chronological offsets from the time of enrollment) are therefore more useful than absolute times when collectively describing therapeutic or adverse events. For logistic reasons, study parameter values are typically recorded at fixed relative times (‘study events’), which serve as time-stamps and can be used by CSDMS temporal query algorithms to simplify temporal computations. The entity-attribute-value model of clinical data storage, used by both CSDMSs and clinical patient record systems, complicates temporal query. To apply temporal operators, data for parameters of interest must first be transiently converted into conventional relational form, with one column per parameter.

Metadata-driven creation of data marts from an EAV-modeled clinical research database

Generic clinical study data management systems can record data on an arbitrary number of parameters in an arbitrary number of clinical studies without requiring modification of the database schema. They achieve this by using an Entity-Attribute-Value (EAV) model for clinical data. While very flexible for creating transaction-oriented systems for data entry and browsing of individual forms, EAV-modeled data is unsuitable for direct analytical processing, which is the focus of data marts. For this purpose, such data must be extracted and restructured appropriately. This paper describes how such a process, which is non-trivial and highly error prone if performed using non-systematic approaches, can be automated by judicious use of the study metadata—the descriptions of measured parameters and their higher-level grouping. The metadata, in addition to driving the process, is exported along with the data, in order to facilitate its human interpretation.

Metadata-driven Ad Hoc Query of Patient Data: Meeting the Needs of Clinical Studies

Clinical study data management systems (CSDMSs) have many similarities to clinical patient record systems (CPRSs) in their focus on recording clinical parameters. Requirements for ad hoc query interfaces for both systems would therefore appear to be highly similar. However, a clinical study is concerned primarily with collective responses of groups of subjects to standardized therapeutic interventions for the same underlying clinical condition. The parameters that are recorded in CSDMSs tend to be more diverse than those required for patient management in non-research settings, because of the greater emphasis on questionnaires for which responses to each question are recorded separately. The differences between CSDMSs and CPRSs are reflected in the metadata that support the respective systems' operation, and need to be reflected in the query interfaces. The authors describe major revisions of their previously described CSDMS ad hoc query interface to meet CSDMS needs more fully, as well as its porting to a Web-based platform.

Clinical Data Interchange Standards Consortium (CDISC) Standards and Their Implementation in a Clinical Data Management System

The Submission Data Standards Group of the Clinical Data Interchange Standards Consortium (CDISC) has developed metadata standards for electronic submissions. These standards have been defined to guide the organization, content, and form of submission datasets for the 12 safety-related domains specified in the Food and Drug Administration (FDA) guidance documents for electronic submissions. Although the CDISC model provides consistency for variable names and data structures, it is also flexible, allowing for differences in what data sponsors collect and how they collect them. This paper is divided into two major parts. The first includes the background of CDISC and an overview of the CDISC model. The scope of the current CDISC effort, the elements of the metadata model, and the variables common to all domains are discussed, using the Demographics domain as an example. The second part uses the Physical Exam domain to illustrate the model's flexibility, as well as to show the practical application of the model in a clinical data management system. The benefits and disadvantages of several modeling approaches are discussed. While much of the focus is on modeling the Physical Exam domain in Oracle Clinical, the ultimate solution described herein could be implemented in any clinical data management system.

Transitioning Clinical Data Management from the 1980S to the 2010S: Strategies for Corporate Decision Making

Clinical development executives realize that their processes must be enabled by technology and other process innovations in order to handle the capacity, speed, and efficiency challenges of an increasingly competitive business environment. Technology companies are pushing for ever-more rapid iterations of generational change in software, systems, and information-handling, from improved versions of traditional clinical data management systems to electronic data capture, and on to electronic clinical trials and business intelligence. The business model for using and paying for these technologies is increasingly complex and confusing compared to the traditional sponsor/vendor relationship, with sponsors having the opportunity to decide how much and what is kept inside or provided from outside services. This paper reviews these conditions and evaluates strategies that corporate executives can follow in selecting and implementing the technologies and business models that are right for their operations today and in the short-term future. Managers must approach this complex matrix of information and options with a process for decision making, and understand the operational implications, costs, and benefits of each path. The paper covers the need to prepare the organization tactically after the strategy is selected, and the tasks required to implement new technologies and processes successfully.

RES3/417: The Development of a Clinical Trial Web Site: a proposal for a model

IntroductionThe Internet and the World Wide Web have been recently introduced in the management of some aspects of large scale clinical trials. We have developed a model for a clinical trial Web site in order to enhance communication among people involved in a clinical trial, to disseminate clinical trials information, and to decentralise some trials activities.MethodsOne section of the Web site should include general medical information such as summary of the background, aim, design of the ongoing trial and results of similar trials. A directory of investigators, members committees, sponsors and monitors (with their e-mail address) could also help people in improving communication. Another section should include centre based summaries of the trial: randomised patients, follow-up schedule, patients lost to follow-up, outstanding case report forms and queries, and statistics on centres quality are some examples. An additional section should be dealing with the latest trial news: trial newsletter, planned meetings, congresses presentations, archive of frequently asked questions on the application of the trial protocol. Patient randomisation/registration and remote data entry are two additional components of a clinical data management system that could be included in the site. In this case database access rules and security should be implemented. Other Web based applications are related with the supply of study material (protocol, case report forms, informed consent), the management of mailing lists for the investigators, the publishing of sub-analyses results.DiscussionAlthough some problems still exist (Internet connection limitations, security, lack of common standard for client software, personnel training), the Internet and the clinical trial Web sites could in the few years transform the way a clinical trial is conducted.

82 Internet security in clinical trial data management systems

The interconnection between field area networks and IP-based LANs as well as the Internet as a whole is becoming more and more popular. Typical realizations involve a gateway between the fieldbus and the LAN. The obvious security problems of such an interconnection have been neglected or underrated in the past. This paper is concerned with methods to secure a fieldbus/Internet gateway. We discuss basic security requirements for gateways and investigate the applicability of traditional methods like firewalls and security protocols. Particular emphasis is laid on authentication and encryption mechanisms. To cope with the logistical problem of key distribution, we propose to use smart cards as a secure key containment. Finally, we present a hierarchical approach to key derivation and report on performance measurements of an actual implementation with different cryptographic algorithms.