4 min read
Rescue Studies: Salvaging Clinical Trials Through Expert Data Management
When a clinical trial faces data quality challenges, every day counts. Rescue studies have become an increasingly critical component in...
What are the important items to be aware of? In this blog, we will look at these and other questions, with a focus on the data management/database build work within medical device studies for the US and EU.
Medical devices are an important part of healthcare. They run the spectrum from the common (e.g., bandages), to diagnostic tests and complex surgical interventions and drug delivery mechanisms. Before they can be put on the market, they need to be shown to be safe and effective. This is accomplished through testing based on regulatory requirements.
Depending on the ‘risk’ of the device and the geographic area (US, EU) of approval, the FDA has categorized this risk into 3 categories:
Classification | Risk | FDA requirements | Examples |
Class I | low | General controls | Bandages, hand-held surgical instruments |
Class II | moderate | Special controls, such as Premarket Notification 510(k) | CT scanners, powered wheelchair, arthroscopic repair |
Class III | high | Premarket Approval (PMA) | Implantable pacemaker, implanted prosthetics |
Together, the FDA and EU requirements present special challenges for clinical trial processes and gathering of necessary data for analyses to support the approval of medical devices.
A database-build directly influences the quality of the study data and is an important part of the medical device study. The regulatory informational needs from both the FDA and EU need to be included in the overall strategy for building a quality and effective database.
The term ‘database’ often refers to both the collected data as well as the database management system (DBMS) — or software that interacts with the end-users, applications, and data itself.
Types of data include:
The set-up of a database plans for all data streams and may include strategies for skip logic, intelligent guided entry, and pop-ups to simplify the entry of data points following the ALCOA principles (Attributable, Legible, Contemporaneous, Original and Accurate) and to maintain the GCP principles for data reporting and data integrity. It must also be able to handle large amounts of data, found in many post-market surveillance device studies.
The ability to integrate the various sources of data, from the eCRF data to the electronic sources, into an effective clinical solution that is accessible and supports regulatory reporting requirements, can be a challenge.
Finding the right EDC system to meet the study needs and requirements at the right price point is needed for both the satisfaction of the sponsor as well as the data management team. Will the trial have decentralized monitoring? Are advanced analytics and visualizations needed? Working knowledge of the various EDC systems as well as the technology to support needed capabilities is important.
Specific information on the devices is needed for reporting purposes.
As a CRO specializing in medical devices, we have supported database builds for Class I, II and III trials. The needs for Class I trials are the least of all trials, requiring only a study protocol and source document for the few PRO (patient-reported outcome) forms. A Class III trial (PMA) is the most extensive effort of the three with systems designed to capture secondary and tertiary endpoint information.
Class 1 | Class 2 | Class 3 |
Low Risk devices: Surgical Instruments, Bandages, etc. | Moderate Risk devices: Syringes, Sutures, etc | High Risk devices: Pacemakers, Shoulder rotator cuff, etc. |
Needed information for Database Build:
|
|
|
Concentrating on the uniqueness of device trials, rather than the typical efforts that contribute to a database build would include:
These challenges are different from the processes and data needs of a clinical trial for a drug and are best met with the expertise of professionals working with device trials based on relevant regional experience.
1. Drozda JP Jr, Graham J, Muhlestein JB, Tcheng JE, Roach J, Forsyth T, Knight S, McKinnon A, May H, Wilson NA, Berlin JA, Simard EP. Multi-institutional distributed data networks for real-world evidence about medical devices: building unique device identifiers into longitudinal data (BUILD). JAMIA Open. 2022 May 25;5(2):ooac035. doi: 10.1093/jamiaopen/ooac035. PMID: 35663113; PMCID: PMC9154019. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9154019/
4 min read
When a clinical trial faces data quality challenges, every day counts. Rescue studies have become an increasingly critical component in...
4 min read
Risk-based quality management (RBQM) represents a core area of clinical development that systematically focuses on both safety and data...
7 min read
As artificial intelligence continues to revolutionize medical device development, the FDA’s evolving regulatory framework presents both...