Clinical Data Management Research Articles

DOP52 Safety of Inflammatory Bowel Disease drugs during pregnancy and breastfeeding: Mothers and babies’ outcomes (DUMBO registry)

Abstract Background Prospective registries are necessary to evaluate the safety of inflammatory bowel disease (IBD) treatment during pregnancy and in children in the long term. Aims The overall aim of DUMBO registry is to know the risk of serious adverse events (SAEs) during pregnancy and in children up to 4 years of age exposed during pregnancy to drugs for IBD (mainly focused on biologics), compared to unexposed children. In this analysis we aim to evaluate the risk of SAEs during pregnancy and the predictive factors of it (mainly focused on IBD drugs). Methods Prospective, observational and multicentre registry, which enrols pregnant women with IBD (Crohn’s disease, ulcerative colitis, IBD-unclassified) over 5 years in 70 centres in Spain. The registry was kicked off in September 2019. SAE was defined based on “Clinical Safety Data Management: Definitions and Standards for Expedited Reporting by European Medicines Agency”. Study protocol is summarized in figure 1. Results 433 women have been included so far; 241 got pregnant at least 9 months before this interim analysis (table 1). Mean age was 34 years, and 17% of women had active disease at any time during pregnancy. 23% of pregnancies were exposed to immumodulators (thiopurines), 25% to biologics and 10% to combo therapy (biologics and immunomodulators). 85 pregnancies (35%) were exposed to biologics (60 anti-TNF, 17 ustekinumab, and 8 vedolizumab) either in combo or in monotherapy. There were 237 newborns (227 singleton and 5 pair of twins), 9 miscarriages and 1 abortion. 72% of patients had vaginal delivery and 28% C-sections (18% due to perianal CD or active disease). A total of 59 pregnancies (24.5%) reported at least one SAE: 32% in exposed to biologics and 20.5% in non-exposed group (p>0.05) (figure 2). Four out of 17 pregnancies exposed to ustekinumab and 3 out of 8 exposed to vedolizumab had SAEs (non-related with the drug). In the multivariate analysis, adjusted by disease activity, in comparison with no immunosuppressive treatment, neither immunosuppressants [Odds ratio (OR)=1.1, 95% confidence interval (CI)=0.3–4.3] nor biologics in monotherapy or in combo (OR=0.8; 95%CI=0.2–3) were associated with higher risk of SAEs. 40 patients (17%) were hospitalised due to complications during pregnancy or delivery (figure 3). Two patients underwent surgery during pregnancy due to IBD complications Conclusion IBD treatment (either immunomodulators or biologics) does not increase the risk of SAEs during pregnancy. Nevertheless, one-quarter of IBD women suffer SAEs during pregnancy and about 20% need hospitalisation, which should be taken into account when managing IBD during pregnancy.

Optimization of Multidimensional Clinical Information System for Schizophrenia

Schizophrenia is a serious mental disease whose pathogenesis has not been fully elucidated. Its clinical evaluation and diagnosis still highly depend on the clinical experience of doctors. It is of great scientific value and clinical significance to study the inducing factors and neuropathological mechanism of schizophrenia. Based on the four research problems of schizophrenia, this paper analyzes the data types that need to be stored in clinical trials and scientific research, including basic information, case report data, neuropsychological and cognitive function evaluation, magnetic resonance data, electroencephalogram (EEG) data, and intestinal flora data. Through the demand analysis of the system, including the data management part, data analysis part, the functional demand of the system management part, and the overall nonfunctional demand of the system, the overall architecture design, functional module division, and database table structure design of the system are completed. Adopting Browser/Server (B/S) architecture and front-end and back-end separation mode and applying Java and Python programming language, based on spring framework and database, a multidimensional information management system for schizophrenia is designed and implemented, which includes four modules: data analysis, data management, system management, and security control. In addition, each functional module of the system is designed and implemented in detail, and the software operation flow of each module is illustrated with the sequence diagram. Finally, the multidimensional data of schizophrenia collected in our laboratory were used for system test to verify whether the system can meet the needs of clinical big data management of schizophrenia and the multidimensional information management system of schizophrenia can meet the needs of clinical big data management. The information management system helps schizophrenic researchers to carry out data management and data analysis. It also has advantages that are easy to use, safe, and efficient and has strong scalability in data management, data analysis, and scalability. It reflects the innovation of the system and provides a good platform for the management, research, and analysis of clinical big data of schizophrenia.

DeepBlockShield: Blockchain Agent-Based Secured Clinical Data Management Model from the Deep Web Environment

With the growth of artificial intelligence in healthcare and biomedical research, many researchers are interested in large amounts of data in hospitals and medical research centers. Then the need for remote medicine services and clinical data utilization are expanding. However, since the misuse and abuse of clinical data causes serious problems, the scope of its use is bound to have a limited range physically and logically. Then a security-enhanced data distribution system for medical deep web environments. Therefore, in this paper, we propose a blockchain-based clinical data management model named DeepBlockshield to prevent information leakage between the deep web and the surface web. Blockchain supports data integrity and user validation to support data sharing in closed networks. Meanwhile, the agent performs integrity verification between the blockchain and the deep web and strengthens the security between the surface web and the deep web. DeepBlockShield verifies the user’s validity through the records of the deep web and blockchain. Furthermore, we wrap the results analyzed by the valid request into a web interface and provide information to the requester asynchronously. In the experiment, the block generation cycle and size on the delay time was analyzed for verifying the stability of the blockchain network. As a result, it showed that the proposed approach guarantees the integrity and availability of clinical data in the deep web environment.

Characterizing Pain Points in Clinical Data Management and Assessing the Impact of Mid-Study Updates.

BackgroundThe causes, degree and disruptive nature of mid-study database updates and other pain points were evaluated to understand if and how the clinical data management function is managing rapid growth in data volume and diversity.MethodsTufts Center for the Study of Drug Development (Tufts CSDD)—in collaboration with IBM Watson Health—conducted an online global survey between September and October 2020.ResultsOne hundred ninety four verified responses were analyzed. Planned and unplanned mid-study updates were the top challenges mentioned and their management was time intensive. Respondents reported an average of 4.1 planned and 3.7 unplanned mid-study updates per clinical trial.ConclusionMid-study database updates are disruptive and present a major opportunity to accelerate cycle times and improve efficiency, particularly as protocol designs become more flexible and the diversity of data, most notably unstructured data, increases.

Fieldwork and Field Trials in Hospitals: Co-Designing A Robotic Solution to Support Data Collection in Geriatric Assessment

Comprehensive geriatric assessment (CGA) is a multidimensional and multidisciplinary diagnostic instrument that helps provide personalized care to older adults by evaluating their state of health. This evaluation is based on extensive data collection in order to develop a coordinated plan to maximize overall health with aging. In the social and economic context of growing ageing populations, medical experts can save time and effort if provided with interactive tools to efficiently assist them in doing CGAs, managing either standardized tests or data collection. Recent research proposes the use of social robots as the central part of this optimization of clinicians’ time and effort. This paper presents the first and last steps of the research made around the design and evaluation of the CLARC robot: fieldwork (analysis of needs and practices concerning clinical data management) and field trials (pilot experiment in real-life conditions in a rehab hospital). Based on an extensive literature review of social robotics applications for health and ageing, it discusses the practical and methodological questions raised around how to design and test assistive social robots for clinical routine, and questions the feasibility of an automated CGA procedure.

A Pilot to Initiate Research Data Management Services Within Academic Libraries Helps Librarians to Learn About, Engage With, and Enhance Skills Within Their Research Communities

A Review of: Read, K. B, Koos, J., Miller, R. S., Miller, C. F., Phillips, G. A., Scheinfeld, L., & Surkis, A. (2019). A model for initiating research data management services at academic libraries. Journal of the Medical Library Association, 107(3), 432–441. https://doi.org/10.5195/jmla.2019.545 Abstract Objectives – To initiate or expand research data management (RDM) services within the participating libraries serving health sciences populations. Design – Case report. Setting – Six institutions consisting of three academic health sciences and three university libraries within the National Network of Libraries of Medicine Middle Atlantic Region in the United States of America. Subjects – Between two and eight librarians participated from each institution, for a total of twenty-six librarian participants. Methods – Pre-pilot phone interviews were conducted and included open-ended questions about RDM services, the library’s motivation for participating, and their degree of institutional commitment. To deepen their understanding of RDM, the participants were required to complete eight educational modules that included text, videos, and quizzes. The participating institutions received data interview questions to connect with their research community to be better informed about their attitudes, language, and practices. The participants also received a Teaching Toolkit, complete with slides, a script, and an attendee evaluation form. The participants were provided with a data series, consisting of branded classes for teaching over a designated period with instructors from within and outside of the library. Collaboration with library partners was encouraged as was the use of a focused marketing plan. In fact, a major component of the pilot was the expert support, provided through biweekly meetings that included marketing tips and presentations on such topics as clinical research data management and data visualization. Finally, post-pilot program interviews were conducted, and the open-ended questions covered the pilot program as a whole and its individual components. Main Results – Of the six participating institutions, five institutions rated the RDM educational modules very positively. Conducting data interviews was valuable for all six institutions because it allowed the librarians to meet with researchers, build relationships, and use what they learned to develop RDM services for the future. The Teaching Toolkit was rated positively by the six institutions, especially for its adaptability, the time saved over developing the content from scratch, and its usability. Finally, the two institutions that held the data series courses stated that the series succeeded in further marketing the RDM services developed by the library. Conclusion – The pilot project met its objectives: the librarians at the participating institutions completed the educational modules, administered the data interviews, and taught an RDM foundations class based on the Teaching Toolkit. In addition, a data series was hosted at two institutions. The components of the pilot project had the intended results at each institution, and the classes were reviewed favorably. Based on the pilot participants’ positive outcomes, the authors are certain that the freely available program materials would achieve success elsewhere.

Risk-based Quality Management in CDM An inquiry into the value of generalized query-based data cleaning

INTRODUCTION: The essence of Risk-based Quality Management is to focus on what matters most. Sponsor companies are annually spending many billions of dollars on automated and manual data cleaning, utilizing internal and site resources to create the illusion of an error-free data base. However, over the last 15 years, a robust data acquisition process was developed, and much of the current query-based data cleaning approach is a self-imposed remnant of the error-prone processes of the past. OBJECTIVE: Motivated by previously reported insights regarding the limited value of generalized source data verification (SDV), seven pharmaceutical companies and a clinical trials solution vendor teamed up to answer the question “How efficient is the query-based data cleaning process?” METHODS: Twenty completed Phase III studies representing different sponsors and therapeutic areas (TAs) were randomly selected from a collective data pool. Query data was aggregated across studies and classified by query type (automatic/manual) and initiator role. Form types were standardized using a semi-supervised machine learning model. Each query record was classified as to whether the query resulted in a change (query efficacy) and whether a change was direct or indirect. Query efficacy was characterized by TA, query type, initiator role and form type and graphically represented using bar charts, histograms, heat maps and waffle plots. RESULTS: Combined, the studies represented more than 20,000 study participants, comprising nearly 50 million data points and over 1.9 million queries. While the overall query rate was 3.9%, even including indirect and non-informative modifications, fewer than half of these queries actually resulted in a data change, affecting less than 1.7% of entered data. CONCLUSION: While clear differences between the type of query, type of form, and query initiator provide important insights on approaches for further improving the Clinical Data Management (CDM) process, our data show that overall the current acquisition process is very robust. Considering the tremendous efforts that go into the generalized query-based data cleaning process, and the limited impact it has, it is concluded that this process does not contribute proportionally to the quality of the final database used for analysis. We recommend ending the current query process to correct errors of non-critical data in Phase 3 studies, and placing more emphasis on tools and techniques that help identify systemic issues in the data collection process. This would be a great leap forward for CDM towards a risk-based approach to quality management as described in ICH E6 (R2).

Electronic Data Capture-Study Implementation and Start-up

Web-based Electronic Data Capture is a mainstay of form-based data collection and management in clinical studies. This chapter reviews the implementation and start-up tasks for clinical studies using web-based EDC for form-based data collection and management (hereafter EDC.) Topics covered include designing, developing, testing, and implementing workflow and data flow in clinical studies using EDC systems. Topics of focus include data collection, work flow, and data flow associated with data collection and processing, data processing such as exchange, integration, cleaning, and coding in EDC systems, implementation at study sites including training and account management, and working with EDC system vendors. The chapter emphasizes common responsibilities of Clinical Data Management (CDM) professional in the implementation of an EDC application for a clinical study.

Electronic Data Capture-Study Conduct, Maintenance and Closeout

Electronic Data Capture (EDC) has become a common a proven tool for data collection and management in clinical trials. Thus, understanding the principles and methods for EDC use has become a major component of clinical data management (CDM) professional practice. This chapter focuses on using the EDC system and accruing data to support study conduct, maintaining an EDC system during a study, and concluding active data collection through database lock. The regulatory basis for minimum standard and recommended best practices are discussed.

Direct Data Extraction and Exchange of Local Labs for Clinical Research Protocols: A Partnership with Sites, Biopharmaceutical Firms, and Clinical Research Organizations

INTRODUCTION: Manual transcription of site clinical trial data into sponsor Electronic Data Capture (EDC) systems is labor intensive and error prone. Herein, we describe Direct Data Extraction (DDE) best practices identified by the Society for Clinical Data Management eSource Consortium that will enable other groups to implement DDE for their own clinical research efforts. OBJECTIVES: The primary objective of this study was to show the efficiency gains and return on investment for implementing DDE compared to traditional manual data entry methods. METHODS: A DDE Proof of Concept (PoC) at Memorial Sloan Kettering Cancer Center (MSK) and Yale University compared manual EDC transcription and DDE. Sites continued to manually transcribe data into Lilly’s EDC in parallel. Data entry timestamps were captured and analyzed for: 1) data latency, 2) transcription errors, 3) query rate, and 4) time and effort savings. Novartis tracked similar efficiency gains when implementing DDE with MSK in 2012, and National Cancer Center Hospital East (NCCE) in 2014. RESULTS: Compared to manual transcription, the Lilly-MSK-Yale DDE PoC decreased: data latency from 20.4 to 3.5 days; transcription errors from 6.7% to 0%; site effort by 8 hrs. per patient, per study; site queries by 2.5 queries per patient, per visit; and monitoring activity by 3 hrs. per patient per study. The NVS-MSK local lab DDE productivity analysis found that 20–24% of manually entered data were removed, and queries were reduced by approximately 50%. A similar productivity analysis between NVS and NCCE showed a 99% reduction in traditional data review activities by NVS, and a 96% reduction in queries to the site. CONCLUSION: DDE increased the productivity of an existing clinical trial data transfer process by decreasing data latency, transcription errors, and queries. It allows for the more efficient use of both sponsor, CRO, and site staff time and effort.

Beyond EDC

INTRODUCTION: The last two decades have seen Electronic Data Capture (EDC) scale the adoption curve in the therapeutic development industry. The history of EDC adoption can inform development and implementation of similar, future advances. OBJECTIVE: This synthesis documents the progression to EDC in clinical research towards improving the development, implementation, and use of emerging and similar technology in multicenter clinical studies. METHODS: The 158 full-text sources identified in the Good Clinical Data Management Practices (GCDMP)systematic literature search were used as the basis for this scoping review.  A single reader evaluated this literature to determine rationale for and barriers to EDC implementation as well as remaining gaps in the use and optimization of EDC technology in clinical research. Quantitative evaluations of EDC technology were identified and summarized. Two reviewers examined the findings to identify omitted seminal work and alternate perspectives. RESULTS: Metrics and outcomes used in EDC evaluation are detailed. Three major gaps between current EDC implementation and fully realized electronically supported clinical trials. Similarities and differences between EDC and new clinical study technology highlighted to improve methods in implementation and evaluation of new technology in clinical studies.   CONCLUSIONS: Foreseeable implementation challenges delayed but did not necessarily extend the EDC adoption curve relative to contemporaneous innovation. Multiple adjunct new technologies have potential to surpass the current EDC performance plateau. Of these, direct EHR-to-eCRF data collection parallels EDC in terms of workflow disruption and technology interaction at clinical sites. Thus, similar adoption challenges are expected. Based on our observations, if developing standards are successful, they will make data more quickly and computationally accessible to significantly broaden opportunities for therapeutic development beyond those available in today’s EDC.

Regulatory and operational challenges in conducting Asian International Academic Trial for expanding the indications of cancer drugs.

There are many differences between Asian regions in terms of the regulatory requirements and operational procedures in conducting international academic clinical trials for the approval of new drugs. The National Cancer Center Hospital in Japan has launched an international investigator‐initiated registration‐directed trial (IIRDT) in Japan, Korea, Taiwan, and Singapore, aiming at obtaining pharmaceutical approval in participating regions. Differences in regulatory and operational procedures were identified while coordinating the trial. In Japan, regulatory authority reviews should be performed after approval by institutional review boards for IIRDT, whereas in other regions these can be done in parallel. There were disparities in Good Manufacturing Practice‐related documents between regions. Several differences were found regarding investigational product (IP) management, specifically concerning labeling, import/export procedures, and customs clearance costs. On the other hand, safety reporting procedures were relatively well‐harmonized in accordance with International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use, Clinical Safety Data Management: Definitions and Standards for Expedited Reporting (ICH‐E2A). Regions also differed in per‐patient costs, due to varying regulations for academic registration‐directed trials. In conclusion, the observed differences among Asian regions should be harmonized to facilitate international academic trials in Asia and thus resolve unmet patient needs worldwide. Study Highlights WHAT IS THE CURRENT KNOWLEDGE ON THE TOPIC? International clinical trials have become common because they make it possible to accrue patients faster and obtain new drug approval in wider areas. However, pharmaceutical regulatory differences hinder the efficient conduct of international clinical trials, especially in academia. WHAT QUESTION DID THIS STUDY ADDRESS? We conducted an academic international clinical trial on new drug applications in four Asian countries and clarified pharmaceutical regulatory differences and operational difficulties. WHAT DOES THIS STUDY ADD TO OUR KNOWLEDGE? The study identified differences between countries in terms of regulatory affairs, institutional review board (IRB) review processes, investigational new drug (IND) dossiers, investigational product (IP) management procedures, and clinical trial costs, while safety reporting procedures were relatively harmonized. Japan utilizes investigator‐initiated registration‐directed trials, an advanced regulatory system for new drug application by academia, but the other countries do not. HOW MIGHT THIS CHANGE CLINICAL PHARMACOLOGY OR TRANSLATIONAL SCIENCE? Harmonization of pharmaceutical regulations and trial initiation procedures, and regulatory reform of clinical trial costs are important to accelerate academic international clinical trials for new drug applications.

WITHDRAWN: Trusted Data Collection Gateway for BlockChain Traceability Applications and Edge Computing

Blockchain technology has Traceability as the realization solution of decentralized trust systems. In this system, the model business is still guaranteed by the underlying blockchain need to understand the best blockchain possible. Design science methods have practical prototypes developed that can change opportunities and challenges from a perspective. This is exemplified by adopting innovative project technologies that help blockchain deliver effective project calculations and resource allocation. Blockchain technology reduces regulatory approval delays, and the management of advanced clinical trial data plays an important role in the medical field. The frequency of spreading false reports is increasing. The trendy supply chain is diverse and can streamline investor contact and not confirm public panic and irrational behavior in existing platforms. Therefore, the development of blockchain-based tracking systems is essential to gaining credibility and credibility from public and government agencies. This is a proposed method on data collected from various external sources and improving various blockchain applications and opportunities and tracking systems.

SISTEM INFORMASI POLIKLINIK TERPADU

Polyclinic management is currently using a computerized system. However, the existing system is almost entirely made separate or in the form of a stand-alone system. Management of clinical data, financial data, dispensary data and queuing data are used with different systems. Based on previous studies there has been a clinical information system, medical records, and inpatient / outpatient care, but the application is still independent. There are many references to previous studies that only manage clinical data, in this case medical records and pharmacy data. Financial management has never been integrated into the same system, especially for the polyclinic information system. The solution provided by researchers is by designing and building a system that combines clinical data management, care data, financial data, pharmacy data, and queuing data in one integrated system. System development utilizes web technologies such as PHP, HTML5, CSS, and Javascript programming languages. Data storage is designed using the MySQL database server. The use of web technology allows the system to run lightly and can be accessed both intranet and internet.

Implementación y funcionamiento de un sistema de información clínica en una comunidad terapéutica

Las tecnologías de información y comunicación (TIC) tienen un impacto creciente en el sector de la salud orientado a la optimización de procesos y su aplicación abarca varios campos como la psicoterapia y la terapia ocupacional. El objetivo del presente estudio es describir el sistema de información Plus, una iniciativa de la comunidad terapéutica Takiwasi que nace de la necesidad de instaurar una plataforma de recopilación, almacenamiento y gestión de los diferentes procesos y datos clínicos que se generan en la institución a lo largo del tratamiento de pacientes drogodependientes. Las peculiaridades de este sistema son estrechamente vinculadas a la unicidad del protocolo de tratamiento de adicciones propuesto en Takiwasi, donde las prácticas de la medicina tradicional amazónica son asociadas a la psicoterapia moderna, al seguimiento biomédico clásico y a otros espacios terapéuticos de medicina alternativa y complementaria. Se presentan los diferentes módulos por el cual el sistema está constituido, así como el proceso de registro de la información. Esta TIC representa una herramienta fundamental para promover la investigación en las aplicaciones terapéuticas de la medicina tradicional y como retroalimentación para mejorar las intervenciones terapéuticas.

Rationale, design, and baseline characteristics of Chinese registry in early detection and risk stratification of coronary plaques (C-STRAT) study.

Rationale, design, and baseline characteristics of Chinese registry in early detection and risk stratification of coronary plaques (C-STRAT) study.

Perspective: Laboratory Considerations and Clinical Data Management for Human Nutrition Randomized Controlled Trials: Guidance for Ensuring Quality and Integrity

ABSTRACTIn human nutrition randomized controlled trials (RCTs), planning, and careful execution of clinical data collection and management are vital for producing valid and reliable results. In this article, we provide an overview of best practices for biospecimen collection and analyses, and for the fundamentals of clinical data management, including preparation and study startup; data collection, entry, cleaning, and authentication; and database lock. The reader is also referred to additional resources for information to assist in the planning and conduct of human RCTs. The tools and strategies described are expected to improve the quality of data produced in human nutrition research that can, therefore, be used to support food and nutrition policies.

Lesionia: A Digital Data Management System to Enhance Epidemiological and Clinical Data Management of Patients with Cutaneous Diseases

Lesionia: A Digital Data Management System to Enhance Epidemiological and Clinical Data Management of Patients with Cutaneous Diseases

A Novel Clinical Data Management Platform for Acute Pancreatitis

A Novel Clinical Data Management Platform for Acute Pancreatitis

Internet of things in health management systems: A review

SummaryIn today's health care context, the application of the Internet of Things (IoT) offers suitability for doctors and patients as we can use them in many medical fields. So, we have emphasized the particular use of the IoT in medicine and health care, such as clinical devices management, medication management, clinical data management, distant medicine, mobile medical care, and individual health management. We have done an organized review to enhance health management systems via many up‐to‐date IoT‐oriented health care applications. The article approbates with the methodological necessities of systematic literature reviews. We have identified several issues caused by the quick acceptance of IoT‐oriented systems' health management. IoT necessitates new safety infrastructure, according to the novel practical principles. Outcomes illustrate that health experts can assist in supplying necessities, information, or standards to improve IoT devices or systems. Also, they are able to present novel fields or applications for which IoT is fit. This survey aids the hospitals and relevant institutions to recognize IoT needs. Also, the article will serve as an initial location for future IoT security management and design. Policymakers and opinion formers need to understand the IoT and its implications.