Appropriate, early planning for medical device development could help outshine the darkness of Alzheimer’s disease

Exciting developments in the area of medical devices for the diagnosis, prognosis, treatment/management, and monitoring of Alzheimer’s disease (AD) have progressed over the last few years, including a wrist-worn device that measures sleep and daytime activity as well as an augmented reality app, both to detect and diagnose AD early in the disease trajectory. Some, like a non-invasive, handheld device to detect AD and Parkinson’s disease blood biomarkers for AD diagnosis as well as a deep brain stimulation (DBS) device and devices using transcranial electromagnetic treatment and gamma frequency light and sound stimulation for disease treatment, have achieved Breakthrough Device Designation from the U.S. Food and Drug Administration (FDA).

These devices could potentially shift the diagnostic and treatment paradigms—for both research and clinical care—from expensive, timely, often invasive in-person methods to convenient, at-home options. This could reduce patient, caregiver, clinician, and researcher time as well as healthcare costs. Further, devices that collect data remotely could extend research and clinical care to individuals in geographically remote locations or with limited mobility.

Challenges with medical device development for AD

However, medical device development for AD is hampered by many of the same reasons that delay drug development: slow enrollment, long study timelines (especially for interventions), and high expense. Clinical trials for AD often cost more per participant than other trials, partially due to expensive screening and monitoring tests, including imaging, blood, and cognitive testing. A high screening failure rate (average of 44%) compounds these costs and extends study timelines, further adding to the costs.

To help accelerate recruitment, speed timelines, and collect critical data about device use in real life and routine clinical care, a number of alternative study designs as well as endpoints (intermediate clinical or surrogate endpoints) are often considered. Trial designs such as large pragmatic trials and real-world studies provide information about the benefits and risks of devices in practice. However, these can also add additional considerations and complexity to data collection, storage, monitoring, and analysis beyond that required for traditional randomized controlled trials, which can be difficult to manage for many study teams.

Plan early for data management, analysis, and submission

Because development timelines for AD devices are often prolonged due to inherent disease characteristics such as heterogeneity of underlying causes, symptoms, and disease progression, minimizing delays associated with data management, analysis, and submission is critical. Therefore, planning these activities appropriately—and early—and executing them efficiently represent key ways to ensure AD device trials stay on time and budget, contributing to more successful clinical trials and commercialization.

Medical devices for AD range from in-clinic DBS implantation to at-home use of devices to improve cognitive function and activities of daily living, each requiring specific considerations for the following: how the data will be collected and centralized, power calculations and the sample sizes required, comparisons against standard of care, randomization methodology, benefit-risk framework, and postmarket surveillance. Centralized monitoring, including risk-based monitoring, of at-home or site-based data collection and easy-to-understand visualizations of study progress in near real-time help determine if additional training is needed to adhere to the protocol and to make go/no-go decisions from the pilot stage through postmarketing.

Building all of these considerations into the protocol, statistical plans, and safety plans as early as possible in the process mitigates changes later in the lifecycle and therefore minimizes impacts to the study timeline and budget.

Many medical devices are addressing the challenges associated with AD research and treatment, by improving and accelerating screening and monitoring processes. However, device development and research teams first have to overcome the challenges for their own device development. Working with an experienced team that has the medical device expertise to help plan and execute data management, analyses, and submission preparation can streamline the process.