ATRI EDC: a novel cloud-native remote data capture system for large multicenter Alzheimer's disease and Alzheimer's disease-related dementias clinical trails

Abstract

Objective: The Alzheimer's Therapeutic Research Institute (ATRI) developed a novel clinical data management system, the ATRI electronic data capture system (ATRI EDC), to address the complex regulatory, operational, and data requirements that arise in the conduct of multicenter Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRDs) clinical trials. We describe the system, its utility, and the broader implications for the field of clinical trials and clinical research informatics.

Materials and methods: The ATRI EDC system was developed, tested, and validated using community-based agile software development methods and cloud-native single-page application design principles. It offers an increasing number of application modules, supports a high degree of study-specific configuration, and empowers study teams to effectively communicate and collaborate on the accurate and timely completion of study activities.

Results: To date, the ATRI EDC system supports 10 clinical studies, collecting study data for 4596 participants. Three case descriptions further illustrate how the system's capabilities support diverse study-specific requirements.

Discussion: The ATRI EDC system has several advantages: its modular capabilities can accommodate rapidly evolving research designs and technologies; its community-based agile development approach and community-friendly licensing model encourage collaboration per the principles of open science; finally, with continued development and community building efforts, the system has the potential to facilitate the effective conduct of clinical studies beyond the field of AD/ADRD.

Conclusion: By effectively addressing the requirements of multicenter AD/ADRD studies, the ATRI EDC system supports ATRI's scientific mission of rigorously testing new AD/ADRD therapies and facilitating the effective conduct of multicenter clinical studies.

Keywords: Alzheimer disease; clinical research informatics; clinical trials; data management; remote electronic data capture.

Figure 1.

ATRI EDC AWS architecture. This cloud-based IT architecture assures strict isolation of configurations, data, and study-specific code between studies. It is implemented using a multi-account strategy that compartmentalizes core functions (production, staging and development workloads, audit, analytics, and logs) to improve security by minimizing the impact of a potential breach. Security groups and policies enforce network security, strong encryption at rest and in transit, and multifactor authentication for administrative accounts. High durability and availability are ensured via multi-region data replication and auto-scaling application groups. IT infrastructure is managed programmatically to minimize the possibility of human error in configuration management. VPC: virtual private network

Figure 2.

ATRI EDC study-CDMS architecture. Each study-specific CDMS is built using a dedicated set of application components hosted on shared cloud-based IT infrastructure. This design guarantees strict isolation of configurations, data, and study-specific code between studies. It also allows IT resources (compute, service, storage) to be optimized to address study-specific regulatory, operational, data, and fiscal requirements. From the user perspective, this design offers authorized users a single point of access to multiple study-specific application modules. Access to these modules is governed by role-based permissions. This approach facilitates communication and collaboration in the accurate and timely completion of study activities.

Figure 3.

ATRI EDC entity relationship diagram (ERD). The ATRI EDC ERD visualizes the system’s data model. The data model contains over 100 entities that serve as the foundation for the system’s database. Each entity may be classified into 1 of 4 types: (1) data ledger, (2) data, (3) metadata, and (4) audit trail or history. Entities may also be divided into core and application-specific entities. To further illustrate this design, the inset diagram presents the ERD for a specific application. This application-level ERD contains 3 entity types and demonstrates the relationships between them.

Figure 4.

ATRI EDC Participant Event Matrix interface. This interface offers a unified overview of a participant’s study record by integrating information from multiple sources—eCRFs, queries, file uploads, data locking, clinical monitor review, and source document verification (SDV). Panel A demonstrates the “Events Overview” tab view, which includes the following features: (1) case navigation and orientation, (2) interface tab selector, (3) event dashboard with study event lock status, eCRF completion progress meter, and unresolved query counts, (4) list of required eCRFs, (5) eCRF completion status, and (6) list of supplemental eCRFs and completion status. Panel B demonstrates the “Find a Form” tab view, which includes the following features: (7) eCRF completion status per study event dashboard, and (8) eCRF name search. Panel C demonstrates the “Queries” tab view, which features the unresolved query dashboard (9). Panel D demonstrates the “Attachments” tab view, which features the file uploads dashboard (10). Panel E demonstrates the “Monitor Review” tab view, which features the eCRF review status per study event dashboard (11).

Figure 5.

ATRI EDC integrated eCRF-Query-Audit Trail interface. This novel interface integrates multiple data sources—eCRF, source document verification (SDV), record locking, eCRF- and item-level queries, eCRF- and item-level audit trail, and eCRF version and revision information. It supports accurate and timely communication and collaboration between study teams and sites by offering a shared view of these data sources and a conversational interface for data query creation and resolution. Panel A demonstrates the primary eCRF interface, which includes the following features: (1) case navigation and orientation, (2) 1-click eCRF-level query and audit trail review, (3) eCRF version and language settings, (4) dates of last modification and SDV, (5) various input types with accompanying prompt, units, notes, and 1-click item-level query and audit trail review, (6) real-time item-level error feedback with query integration, (7) item indenting, missing data check box, and skip patterns, (8) color-coded action buttons, (9) integrated query listing, details, and creation, (10) conversational query interface, (11) query comments, response, and closure, (12) query conversation history, and (13) field navigation, orientation, and query status. Panel B demonstrates the integrated Audit Trail interface, which includes the following features: (14) summary and detailed view of eCRF insert, (15) summary and detailed view of eCRF update with item-level change summary and query status, (16) eCRF update with resolved query status, and (17) SDV summary.

Figure 6.

ATRI EDC additional modules example: Adverse Event Coding module. This module supports the coding of adverse event (AE) data. It leverages a code suggestion feature powered by a text classification API that combines natural language processing (NLP) and machine learning methods. Panel A demonstrates the Event Coding Summary view, which includes the following features: (1) navigation and orientation and (2) event coding summary dashboard. Panel B demonstrates the Event Coding interface, which includes the following features: (3) event filtering and grouping, (4) event coding selection, and (5) event code suggestion and search. LLT: MedDRA lowest level term.