Revising the ECRIN standard requirements for information technology and data management in clinical trials

[ECRIN standard requirements for good clinical practices-compliant data management in multinational clinical trials]

CONTEXT

Clinical studies involve an increasing amount of data collection and management. However, there is no specific quality standard sufficiently practical, in free access, and open for data management and the underlying IT-infrastructure in academic units. European Clinical Research Infrastructures Network (ECRIN) published standard requirements for certified data management units. We present a French version of these standards.

METHODS

A group of experts produced the standards, by consensus. The first version was revised after two pilot audits for data centre certification were performed.

RESULTS

The revised version includes 21 lists of five to ten standards, in three groups: information technologies, data management (DM) and "general".

CONCLUSIONS

These standards offer a clear description of DM and IT requirements for clinical studies. Initially created for ECRIN certification purposes, they offer a very useful reference for academic DM structures.

Data management of clinical trials during an outbreak of Ebola virus disease

INTRODUCTION

Clinical trial data management (DM) conducted during outbreaks like that of Ebola virus disease (EVD) in West Africa, 2014-2016, has to adapt to specific, unique circumstances. CTU Bern was asked to set up a safe data capture/management system that could be launched within a few weeks and cover two different vaccine trials. This article describes some of the challenges we faced and our solutions during the two different trials.

METHODS

Setting up a DM system was split into four phases/tasks: (1) quick set-up of the (electronic) data capture system (EDC) and mobile infrastructure in Bern, (2) moving the EDC and infrastructure to Conakry, Guinea and implementation of a local data management centre (DMC), (3) running the DMC, and (4) data cleaning. The DMC had to meet the following criteria: (1) quick implementation, (2) efficient maintenance and handling of data, and (3) procedures to guarantee data quality. The EDC (REDCap) was setup as a local area network. In order to ensure high data quality, double data entry, and then review of inconsistencies and offline plausibility checks were implemented.

RESULTS

From the start of CTU Bern's involvement to the productive EDC took 11 weeks. It was necessary to adapt processes for dealing with data continuously throughout the trial conduct phase. The data management team processed 171,794 case report form pages from a total of 14,203 participants in the period between March and December 2015.

CONCLUSION

Data management is a key task supporting trial conduct. For trials in emergency situations, many of our approaches are suitable, but we also provide a list of aspects that might be done differently.

A Good Practice-Compliant Clinical Trial Imaging Management System for Multicenter Clinical Trials: Development and Validation Study

Background

With the rapid increase in utilization of imaging endpoints in multicenter clinical trials, the amount of data and workflow complexity have also increased. A Clinical Trial Imaging Management System (CTIMS) is required to comprehensively support imaging processes in clinical trials. The US Food and Drug Administration (FDA) issued a guidance protocol in 2018 for appropriate use of medical imaging in accordance with many regulations including the Good Clinical Practice (GCP) guidelines. Existing research on CTIMS, however, has mainly focused on functions and structures of systems rather than regulation and compliance.

Objective

We aimed to develop a comprehensive CTIMS to meet the current regulatory guidelines and various required functions. We also aimed to perform computerized system validation focusing on the regulatory compliance of our CTIMS.

Methods

Key regulatory requirements of CTIMS were extracted thorough review of many related regulations and guidelines including International Conference on Harmonization-GCP E6, FDA 21 Code of Federal Regulations parts 11 and 820, Good Automated Manufacturing Practice, and Clinical Data Interchange Standards Consortium. The system architecture was designed in accordance with these regulations by a multidisciplinary team including radiologists, engineers, clinical trial specialists, and regulatory medicine professionals. Computerized system validation of the developed CTIMS was performed internally and externally.

Results

Our CTIMS (AiCRO) was developed based on a two-layer design composed of the server system and the client system, which is efficient at meeting the regulatory and functional requirements. The server system manages system security, data archive, backup, and audit trail. The client system provides various functions including deidentification, image transfer, image viewer, image quality control, and electronic record. Computerized system validation was performed internally using a V-model and externally by a global quality assurance company to demonstrate that AiCRO meets all regulatory and functional requirements.

Conclusions

We developed a Good Practice–compliant CTIMS—AiCRO system—to manage large amounts of image data and complexity of imaging management processes in clinical trials. Our CTIMS adopts and adheres to all regulatory and functional requirements and has been thoroughly validated.

Early warnings and repayment plans: novel trial management methods for monitoring and managing data return rates in a multi-centre phase III randomised controlled trial with paper Case Report Forms

Background

Monitoring and managing data returns in multi-centre randomised controlled trials is an important aspect of trial management. Maintaining consistently high data return rates has various benefits for trials, including enhancing oversight, improving reliability of central monitoring techniques and helping prepare for database lock and trial analyses. Despite this, there is little evidence to support best practice, and current standard methods may not be optimal.

Methods

We report novel methods from the Trial of Imaging and Schedule in Seminoma Testis (TRISST), a UK-based, multi-centre, phase III trial using paper Case Report Forms to collect data over a 6-year follow-up period for 669 patients. Using an automated database report which summarises the data return rate overall and per centre, we developed a Microsoft Excel-based tool to allow observation of per-centre trends in data return rate over time. The tool allowed us to distinguish between forms that can and cannot be completed retrospectively, to inform understanding of issues at individual centres. We reviewed these statistics at regular trials unit team meetings. We notified centres whose data return rate appeared to be falling, even if they had not yet crossed the pre-defined acceptability threshold of an 80% data return rate. We developed a set method for agreeing targets for gradual improvement with centres having persistent data return problems. We formalised a detailed escalation policy to manage centres who failed to meet agreed targets. We conducted a post-hoc, descriptive analysis of the effectiveness of the new processes.

Results

The new processes were used from April 2015 to September 2016. By May 2016, data return rates were higher than they had been at any time previously, and there were no centres with return rates below 80%, which had never been the case before. In total, 10 centres out of 35 were contacted regarding falling data return rates. Six out of these 10 showed improved rates within 6–8 weeks, and the remainder within 4 months.

Conclusions

Our results constitute preliminary effectiveness evidence for novel methods in monitoring and managing data return rates in randomised controlled trials. We encourage other researchers to work on generating better evidence-based methods in this area, whether through more robust evaluation of our methods or of others.

EsPRit: ethics committee proposals for Long Term Medical Data Registries in rapidly evolving research fields - a future-proof best practice approach

Background

Long-term data collection is a challenging task in the domain of medical research. Many effects in medicine require long periods of time to become traceable e.g. the development of secondary malignancies based on a given radiotherapeutic treatment of the primary disease. Nevertheless, long-term studies often suffer from an initial lack of available information, thus disallowing a standardized approach for their approval by the ethics committee. This is due to several factors, such as the lack of existing case report forms or an explorative research approach in which data elements may change over time.

In connection with current medical research and the ongoing digitalization in medicine, Long Term Medical Data Registries (MDR-LT) have become an important means of collecting and analyzing study data. As with any clinical study, ethical aspects must be taken into account when setting up such registries.

This work addresses the problem of creating a valid, high-quality ethics committee proposal for medical registries by suggesting groups of tasks (building blocks), information sources and appropriate methods for collecting and analyzing the information, as well as a process model to compile an ethics committee proposal (EsPRit).

Methods

To derive the building blocks and associated methods software and requirements engineering approaches were utilized. Furthermore, a process-oriented approach was chosen, as information required in the creating process of ethics committee proposals remain unknown in the beginning of planning an MDR-LT. Here, we derived the needed steps from medical product certification. This was done as the medical product certification itself also communicates a process-oriented approach rather than merely focusing on content. A proposal was created for validation and inspection of applicability by using the proposed building blocks. The proposed best practice was tested and refined within SEMPER (Secondary Malignoma - Prospective Evaluation of the Radiotherapeutics dose distribution as the cause for induction) as a case study.

Results

The proposed building blocks cover the topics of “Context Analysis”, “Requirements Analysis”, “Requirements Validation”, “Electronic Case Report (eCRF) Design” and “Overall Concept Creation”. Additional methods are attached with regards to each topic. The goals of each block can be met by applying those methods. The proposed methods are proven methods as applied in e.g. existing Medical Data Registry projects, as well as in software or requirements engineering.

Conclusion

Several building blocks and attached methods could be identified in the creation of a generic ethics committee proposal. Hence, an Ethics Committee can make informed decisions on the suggested study via said blocks, using the suggested methods such as “Defining Clinical Questions” within the Context Analysis. The study creators have to confirm that they adhere to the proposed procedure within the ethic proposal statement. Additional existing Medical Data Registry projects can be compared to EsPRit for conformity to the proposed procedure. This allows for the identification of gaps, which can lead to amendments requested by the ethics committee.

Raising standards in clinical research - The impact of the ECRIN data centre certification programme, 2011-2016

The nature and the purpose of the ECRIN Data Centre Certification Programme are summarised, and a very brief description is given of the underlying standards (129 in total, divided into 19 separate lists). The certification activity performed so far is described. In a pilot phase 2 centres were certified in 2012. Calls in 2014 and 2015 resulted in a further 8 certified centres, with 2 certifications still in progress, and the 2016 call has generated several additional applications. The impact and benefits of the programme are listed, divided into a) the effects of the introduction of the standards, b) the effects of the certification programme in general, and c) the effects of the certification programme on individual units. The discussion emphasises the generally positive impact of the programme so far but stresses the need to better clarify the perspective and role of the programme.

Ensuring sustainability of software tools and services by cooperation with a research infrastructure

Sustainability of project output and especially of the maintenance and further development of software is of growing concern for the research community. In the personalized medicine project p-medicine solutions that address this sustainability problem were developed and discussed in a workshop. They involve a number of interrelated and mutually supportive measures including the creation of a service center, building modular software, using common data standards, mutual service exchange with a research infrastructure, Open Source and fee-based software provision, joint promotion and deployment of tools in a regulated, clinical trial situation. These ideas join a nascent literature seeking to understand how project output can be put into a sustainable environment and to suggest solutions that may be useful for academic projects in general.