Portal of Medical Data Models: Application in Federated Data Capture.

So far, the portal for medical data models allows its users to download medical forms in a standardized format. Importing data models into electronic data capture software involved a manual step of downloading and importing the files. Now, the portal was enhanced with a web services interface to allow electronic data capture systems to automatically download the forms. This mechanism can be used in federated studies to ensure that all partners are working with identical definitions of study forms.

Migrating a Well-Established Longitudinal Cohort Database From Oracle SQL to Research Electronic Data Entry (REDCap): Data Management Research and Design Study.

BACKGROUND: Providing user-friendly electronic data collection tools for large multicenter studies is key for obtaining high-quality research data. Research Electronic Data Capture (REDCap) is a software solution developed for setting up research databases with integrated graphical user interfaces for electronic data entry. The Swiss Mother and Child HIV Cohort Study (MoCHiV) is a longitudinal cohort study with around 2 million data entries dating back to the early 1980s. Until 2022, data collection in MoCHiV was paper-based. OBJECTIVE: The objective of this study was to provide a user-friendly graphical interface for electronic data entry for physicians and study nurses reporting MoCHiV data. METHODS: MoCHiV collects information on obstetric events among women living with HIV and children born to mothers living with HIV. Until 2022, MoCHiV data were stored in an Oracle SQL relational database. In this project, R and REDCap were used to develop an electronic data entry platform for MoCHiV with migration of already collected data. RESULTS: The key steps for providing an electronic data entry option for MoCHiV were (1) design, (2) data cleaning and formatting, (3) migration and compliance, and (4) add-on features. In the first step, the database structure was defined in REDCap, including the specification of primary and foreign keys, definition of study variables, and the hierarchy of questions (termed "branching logic"). In the second step, data stored in Oracle were cleaned and formatted to adhere to the defined database structure. Systematic data checks ensured compliance to all branching logic and levels of categorical variables. REDCap-specific variables and numbering of repeated events for enabling a relational data structure in REDCap were generated using R. In the third step, data were imported to REDCap and then systematically compared to the original data. In the last step, add-on features, such as data access groups, redirections, and summary reports, were integrated to facilitate data entry in the multicenter MoCHiV study. CONCLUSIONS: By combining different software tools-Oracle SQL, R, and REDCap-and building a systematic pipeline for data cleaning, formatting, and comparing, we were able to migrate a multicenter longitudinal cohort study from Oracle SQL to REDCap. REDCap offers a flexible way for developing customized study designs, even in the case of longitudinal studies with different study arms (ie, obstetric events, women, and mother-child pairs). However, REDCap does not offer built-in tools for preprocessing large data sets before data import. Additional software is needed (eg, R) for data formatting and cleaning to achieve the predefined REDCap data structure.

De-novo FAIRification via an Electronic Data Capture system by automated transformation of filled electronic Case Report Forms into machine-readable data.

INTRODUCTION: Existing methods to make data Findable, Accessible, Interoperable, and Reusable (FAIR) are usually carried out in a post hoc manner: after the research project is conducted and data are collected. De-novo FAIRification, on the other hand, incorporates the FAIRification steps in the process of a research project. In medical research, data is often collected and stored via electronic Case Report Forms (eCRFs) in Electronic Data Capture (EDC) systems. By implementing a de novo FAIRification process in such a system, the reusability and, thus, scalability of FAIRification across research projects can be greatly improved. In this study, we developed and implemented a novel method for de novo FAIRification via an EDC system. We evaluated our method by applying it to the Registry of Vascular Anomalies (VASCA). METHODS: Our EDC and research project independent method ensures that eCRF data entered into an EDC system can be transformed into machine-readable, FAIR data using a semantic data model (a canonical representation of the data, based on ontology concepts and semantic web standards) and mappings from the model to questions on the eCRF. The FAIRified data are stored in a triple store and can, together with associated metadata, be accessed and queried through a FAIR Data Point. The method was implemented in Castor EDC, an EDC system, through a data transformation application. The FAIRness of the output of the method, the FAIRified data and metadata, was evaluated using the FAIR Evaluation Services. RESULTS: We successfully applied our FAIRification method to the VASCA registry. Data entered on eCRFs is automatically transformed into machine-readable data and can be accessed and queried using SPARQL queries in the FAIR Data Point. Twenty-one FAIR Evaluator tests pass and one test regarding the metadata persistence policy fails, since this policy is not in place yet. CONCLUSION: In this study, we developed a novel method for de novo FAIRification via an EDC system. Its application in the VASCA registry and the automated FAIR evaluation show that the method can be used to make clinical research data FAIR when they are entered in an eCRF without any intervention from data management and data entry personnel. Due to the generic approach and developed tooling, we believe that our method can be used in other registries and clinical trials as well.

Modeling cancer clinical trials using HL7 FHIR to support downstream applications: A case study with colorectal cancer data.

BACKGROUND AND OBJECTIVE: Identification and Standardization of data elements used in clinical trials may control and reduce the cost and errors during the operational process, and enable seamless data exchange between the electronic data capture (EDC) systems and Electronic Health Record (EHR) systems. This study presents a methodology to comprehensively capture the clinical trial data element needs. MATERIALS AND METHODS: Case report forms (CRF) for clinical trial data collection were used to approximate the clinical information need, whereby these information needs were then mapped to a semantically equivalent field within an existing FHIR cancer profile. For items without a semantically equivalent field, we considered these items to be information needs that cannot be represented in current standards and proposed extensions to support these needs. RESULTS: We successfully identified 62 discrete items from a preliminary survey of 43 base questions in four CRFs used in colorectal cancer clinical trials, in which 28 items are modeled with FHIR extensions and their associated responses for colorectal cancer. We achieved promising results in the data population of the CRFs with average Precision 98.5 %, Recall 96.2 %, and F-measure 96.8 % for all base questions. We also demonstrated the auto-filled answers in CRFs can be used to discover patient subgroups using a topic modeling approach. CONCLUSION: CRFs can be considered as a proxy for representing information needs for their respective cancer types. Mining the information needs can serve as a valuable resource for expanding existing standards to ensure they can comprehensively represent relevant clinical data without loss of granularity.

Biblioteca para el diseño del cuaderno de recogida de datos en ensayos clínicos en Openclinica / Library for Designing the Case Report Form of Clinical Trials in Openclinica

Introducción: El Centro Nacional Coordinador de Ensayos Clínicos (CENCEC) utiliza el software OpenClinica para diseñar los Cuadernos de Recogida de Datos (CRD) de los ensayos clínicos. Cada ensayo tiene características específicas, pero existen datos comunes a todos los ensayos que pueden ser estandarizados. Objetivo: Desarrollar una biblioteca de plantillas para el diseño de los CRD. Método: Se realizó un análisis documental de los estándares para el manejo de datos en los ensayos clínicos y se analizaron los diseños utilizados en cuatro ensayos desarrollados en OpenClinica. Resultados: Según los tipos de datos que se registran en los ensayos clínicos se diseñaron 14 plantillas. Cada plantilla, es un fichero Excel con cinco hojas de trabajo donde se registran todas las definiciones del CRD. Las plantillas se han utilizado en tres ensayos clínicos con resultados favorables. Conclusión: Las 14 plantillas que componen la biblioteca CRD fueron diseñadas permitiendo su futura reutilización en la gestión de datos de nuevos ensayos clínicos(AU)

Introduction: CENCEC uses OpenClinica software to design the database of the Case Report Forms (CRF) of the clinical trials (CT). Every trial has specific characteristics although some of them are common to all trials which permit to standardize the process. Objective: To develop a library of templates in order to design the CRF. Methods: A documentary analyses of the standards for data management in clinical trials was performed and in addition of that four designs developed at OpenClinica were reviewed. Results: A library of 14 templates according to data types of CT was proposed. Every template is an Excel file with five sheets in which the definitions of the CRF is registered. The templates have been used in three CT. Conclusions: The 14 templates that make up the CRF library were designed allowing their future reuse in the management of data from new clinical trials(AU)

Outcomes of patients with anemia and renal dysfunction in hospitalized heart failure with preserved ejection fraction (from the CN-HF registry).

BACKGROUND: Although a large number of studies on heart failure with reduced ejection fraction (HFrEF) have found that anemia and renal dysfunction (RD) independently predicted poor outcomes, there are still few reports on patients with heart failure with preserved ejection fraction (HFpEF). METHODS: Clinical data of HFpEF patients registered in the China National Heart Failure Registration Study (CN-HF) were evaluated and the clinical features of patients with or without anemia/RD were compared to explore the impact of anemia and RD on all-cause mortality and all-cause re-hospitalization. RESULTS: 1604 patients with HFpEF were enrolled, the prevalence of anemia was 51.0%. Although anemia was associated with increased risk of all-cause mortality and all-cause re-hospitalization in univariate COX regression (p < 0.05), multivariate COX model confirmed that anemia was not independently associated with all-cause mortality [hazard ratio (HR) 1.14, 95% confidence interval (CI) 0.85-1.52, p = 0.386] and all-cause re-hospitalization (HR 1.13, 95% CI 0.96-1.33, p = 0.152). Similarly, RD was not an independent predictor of all-cause mortality (HR 1.18, 95% CI 0.88-1.57, p = 0.269) and all-cause re-hospitalization (HR 0.94, 95% CI 0.79-1.12, p = 0.488) as assessed in the adjusted COX regression model. The interaction between RD and anemia on end-points events was also not statistically significant. However, anemia was associated with increased all-cause re-hospitalization in patients with New York Heart Association (NYHA) class III-IV. CONCLUSIONS: In patients with HFpEF from CN-HF registry, anemia was common, but was not an independent predictor of all-cause mortality and all-cause re-hospitalization, except for the all-cause re-hospitalization in patients with NYHA class III-IV.Clinical Trial Registration: http://www.clinicaltrials.gov/ct2/home; ID: NCT02079428.

Clinical Trial Data Management Software: A Review of the Technical Features.

BACKGROUND: Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems. METHODS: Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study. RESULTS: Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development. CONCLUSION: It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

Using Response Evaluation Criteria in Solid Tumors in real-world evidence cancer research.

AIM: Real-world evidence of charted treatment responses to cancer drug therapy was compared with medical record derived radiographic measurements of target lesions per Response Evaluation Criteria in Solid Tumors (RECIST). MATERIALS & METHODS: 15 physicians treating 59 metastatic Merkel cell cancer (mMCC) patients contributed patient-level data. A comparison of medical record reported best response with radiographic measurements per RECIST of pre- and post-treatment target lesions. RESULTS: RECIST response rates were significantly lower compared with medical record reported with a concordance of 43.2% (95% CI: 28.0-58.4%). CONCLUSION: Subjective assessment of tumor response collected via traditional chart abstraction may overestimate benefit and limit the potential role of real-world evidence in value-based care research. The use of target lesion measurements presents an attractive alternative that better aligns with trial results.

Case Study: Converting Paper-based Case Report Forms to an Electronic Format (e-CRF) with ACASI Self-Report Integration.

This paper will discuss the integration of electronic Case Report Forms (e-CRFs) into an already existing Android-based Audio Computer-Assisted Self-Interview (ACASI) software solution that was developed for a public health project in Kampala, Uganda, the technical outcome results, and lessons learned that may be useful to other projects requiring or considering such a technology solution. The developed product can function without a connection to the Internet and allows for synchronizing collected data once connectivity is possible. Previously, only paper-based CRFs were utilized at the Uganda project site. A subset or select group of CRFs were targeted for integration with ACASI in order to test feasibility and success. Survey volume, error rate, and acceptance of the system, as well as the operational and technical design of the solution, will be discussed.

Standardizing data exchange for clinical research protocols and case report forms: An assessment of the suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).

Efficient communication of a clinical study protocol and case report forms during all stages of a human clinical study is important for many stakeholders. An electronic and structured study representation format that can be used throughout the whole study life-span can improve such communication and potentially lower total study costs. The most relevant standard for representing clinical study data, applicable to unregulated as well as regulated studies, is the Operational Data Model (ODM) in development since 1999 by the Clinical Data Interchange Standards Consortium (CDISC). ODM's initial objective was exchange of case report forms data but it is increasingly utilized in other contexts. An ODM extension called Study Design Model, introduced in 2011, provides additional protocol representation elements. Using a case study approach, we evaluated ODM's ability to capture all necessary protocol elements during a complete clinical study lifecycle in the Intramural Research Program of the National Institutes of Health. ODM offers the advantage of a single format for institutions that deal with hundreds or thousands of concurrent clinical studies and maintain a data warehouse for these studies. For each study stage, we present a list of gaps in the ODM standard and identify necessary vendor or institutional extensions that can compensate for such gaps. The current version of ODM (1.3.2) has only partial support for study protocol and study registration data mainly because it is outside the original development goal. ODM provides comprehensive support for representation of case report forms (in both the design stage and with patient level data). Inclusion of requirements of observational, non-regulated or investigator-initiated studies (outside Food and Drug Administration (FDA) regulation) can further improve future revisions of the standard.

Memorandum "Open Metadata". Open Access to Documentation Forms and Item Catalogs in Healthcare.

At present, most documentation forms and item catalogs in healthcare are not accessible to the public. This applies to assessment forms of routine patient care as well as case report forms (CRFs) of clinical and epidemiological studies. On behalf of the German chairs for Medical Informatics, Biometry and Epidemiology six recommendations to developers and users of documentation forms in healthcare were developed. Open access to medical documentation forms could substantially improve information systems in healthcare and medical research networks. Therefore these forms should be made available to the scientific community, their use should not be unduly restricted, they should be published in a sustainable way using international standards and sources of documentation forms should be referenced in scientific publications.

Cardiac Safety Research Consortium (CSRC): Cardiovascular Safety and Adverse Event Case Report Forms.

Detection of off-target cardiovascular (CV) effects remains a significant challenge to drug development. Documentation of CV events in non-CV trials is often inadequate to interpret imbalances between treatment arms, which may lead to concerns about potential CV safety "signals." The Cardiac Safety Research Consortium (CSRC) public-private partnership has developed CV case report forms (CRFs) for adverse CV events, including death. These CRFs are intended to encourage collection, as near to the occurrence of an event as possible, of the minimum information necessary to assess, or possibly adjudicate, the event. A broad range of stakeholders (representing industry, academia, and regulatory authorities) developed these forms with the goal of balancing the collection of key information with the resources likely to be available. Use of these forms is optional, and sponsors may modify them. These forms have not undergone any type of "validation" process. The CSRC will continue to sponsor a working group to invite public comment and feedback on these forms.

The clinical assessment and remote administration tablet.

Electronic data capture of case report forms, demographic, neuropsychiatric, or clinical assessments, can vary from scanning hand-written forms into databases to fully electronic systems. Web-based forms can be extremely useful for self-assessment; however, in the case of neuropsychiatric assessments, self-assessment is often not an option. The clinician often must be the person either summarizing or making their best judgment about the subject's response in order to complete an assessment, and having the clinician turn away to type into a web browser may be disruptive to the flow of the interview. The Mind Research Network has developed a prototype for a software tool for the real-time acquisition and validation of clinical assessments in remote environments. We have developed the clinical assessment and remote administration tablet on a Microsoft Windows PC tablet system, which has been adapted to interact with various data models already in use in several large-scale databases of neuroimaging studies in clinical populations. The tablet has been used successfully to collect and administer clinical assessments in several large-scale studies, so that the correct clinical measures are integrated with the correct imaging and other data. It has proven to be incredibly valuable in confirming that data collection across multiple research groups is performed similarly, quickly, and with accountability for incomplete datasets. We present the overall architecture and an evaluation of its use.