Project Abstract:

The National Evaluation System for health Technology Coordinating Center (NESTcc) is tasked to be a catalyst for the timely, reliable, and cost-effective development of real-world evidence (RWE) to enhance regulatory and clinical decision-making for medical devices.Real world data (RWD) reflects information relating to patient health status and the delivery of health care routinely collected from a variety of sources, including information obtained at the point of care. This type of data complements data generated from traditional clinical trials which possess the inherent challenges of being extremely costly, time consuming, and by design, highly limited in generalizability. The Federal Drug Administration (FDA)is committed to exploring new methodologies to enable the collection and use of data from routine medical care and developing valid scientific evidence that could be used for regulatory decision making. In the current environment, the absence of unique device identifiers (UDI) in electronic databases, and the phased implementation  and enforcement of unique device identification compliance by federal agencies has been a hurdle to conducting post marketing epidemiology studies for medical devices. Abroad, theEuropean Union (EU) issued new Medical Device Regulations (MDR)in May of 2017 that includes requirements for “proactive surveillance” for recertification of medical devices to remain on the market in the EU by May 2020. Companies that develop and manufacture medical devices will therefore need to develop methodology and approaches for proactive surveillance of medical devices that meets the EU’sMDR standards in order to keep medical devices accessible to patients in the EU.

The proposed test case seeks to determine the feasibility of using real-world data captured through PEDSnet, a participant of the NESTcc network of health systems (NESTcc Network), to conduct proactive post market surveillance for safety and effectiveness for craniomaxillofacial (CMF) distractors. PEDSnet is a national consortium of leading children’s hospitals that has established an analysis ready database of electronic health record(EHR)data for over 6 million children to be used to advance multi-institutional clinical research among children and youth(Forrest, C. B., Margolis, P. A., Charles Bailey, L., Marsolo, K., Del Beccaro, M. A., Finkelstein, J. A., Milov D.E., Vieland V.J., Wolf B.A., Yu F.B., Kahn, M. G. (2014). PEDSnet: A national pediatric learning health system. Journal of the American Medical Informatics Association, 21(4), 602–606. https://doi.org/10.1136/amiajnl-2014-002743).The project aims are outlined below.

Specific Aims and Significance:

While critically important to individual patients, use of implantable devices is generally rare in pediatrics. As a result, effective study of devices such as the CMF distractors addressed in this proposal is not possible at single institutions, or even large referral centers. Rather, it requires multi-site collaboration across tertiary care centers that provide surgical and other care to children with complex congenital or acquired midface anomalies. The low patient density, combined with significant per-site start-up costs, have made pediatric device studies difficult to execute. The work proposed here will test the potential of PEDSnet to make device surveillance feasible, and to provide insight into performance in practice of several models of CMF distractors. Execution will provide information not only about performance of specific devices, but about ways to improve research networks like PEDSnet’s ability to effectively support device research in collaboration with manufacturers and regulators. To accomplish this, we propose:

1. To assess the ability of PEDSnet to generate RWE about device performance by providing meaningful information that contributes to safety surveillance. This feasibility assessment includes: 

a. Data elements describing device exposure, including comparator devices 

b. Health service data relating to procedure type, outcomes, and clinical covariates needed to assess outcomes of interest

c. Estimation of sample size within data collaborators

d. Estimation of representativeness of the sample for purposes of generalizability to the patient population of users 

2. To describe the cohort of patients exposed to devices of interest, using the PEDSnet data network;

3. To enhance capacity for network study of medical devices, such as coordination of data use agreements, interoperable data elements describing device utilization, data analysis, data linkage across collaborators, and measures to decrease risk to patient privacy; 

4. To identify gaps in PEDSnet data capture impacting ability to study device utilization and related outcomes; and 

5. To explore methodology and approaches for proactive surveillance of medical devices for use in pediatric populations to meet future or existing US and EU regulatory standards.