Amidst the ultra-competitive medical device space, manufacturers are constantly striving to develop the newest, first-in-class and game-changing products. Without an effective strategy to register novel products, the excitement of innovation can be short-lived.
The most commonly used strategy to introduce innovative devices in the US is the de novo process.
For the uninitiated, the de novo process is a way by which a new type of medical device that has not been previously classified is “automatically” or “statutorily” classified as class III, irrespective of the risk it poses or the ability of general and special controls to assure its safety and effectiveness.
The de novo process offers the ability for the manufacturer to make a recommendation on what class they think their device should be. If a strong enough case is made and supporting evidence is provided, FDA can declassify the device to either class I or II. However, if the classification request is denied, the device remains a class III, which means significantly longer approval time, clinical trials and hefty FDA fees.
Therefore, it is prudent for the manufacturer to formulate a strong de novo case. Many companies may not be aware that manufacturers can use their patient population to strengthen their argument for declassification.
The FDA has stated that it values the insights of patients and care-partners who live with a disease or condition that the device can either help diagnose or treat. FDA uses the feedback from patients, formally defined as “Patient Preference Information” (PPI) when deciding on declassification.
It is important to note that PPI does not change the review standard for the de novo process nor does it create extra burden for sponsors. If your device has a direct patient interface, is a life-saving but high-risk device, fills an unmet need or has a novel technology, PPI may be a great strategy.
PPI requires a scientifically valid study (per FDA’s standards). The following must be considered when designing the PPI study:
- Patient-centric: The study should measure perspectives and preferences on the risks and benefits of well-informed patients, not healthcare professionals.
- Representative Sample: The study must be sizable enough to generalize results and must include people who are part of the population of interest.
- Showing differences in patient preferences: For the patient to make an analysis of the risk-benefit trade off, many factors like personal values, age, gender, race, cultural and socioeconomic background will play a role. PPI should reflect patients from the full spectrum of disease for which the device is intended to be used.
- Good Research Practices: The quality of the study can be established if it is seen to comply with good research practices, as, laid down by professional organizations.
- Explaining benefit, harm, risk and uncertainty: It is important to effectively communicate not just the benefits but also all the side effects and risks of the device to the patients.
- Reduce Cognitive Bias: Framing questions in terms of gains and losses, recording percentages a low, medium or high, and signaling reference values should be avoided.
- Logical Soundness: The study should meet the standards of logical validity and consistency.
- Relevance: Aspects of harm, risk, benefit and uncertainty should be present in the study results.
- Robust Analysis of Results: Once the measurements are made, an analysis should provide a clear picture of the parameters in play.
- Study Conduct: The study must comply with a predefined study protocol and patients must be trained to ensure they follow the protocol.
- Comprehension by Study Participants: Aspects of the study must be presented in layman terms such that all participants understand what is being communicated to them.
The process of collecting and presenting PPI can be painstaking, but very powerful. If you need help deciphering whether your de novo application is strong or whether it needs to be supplemented by PPI data, contact us at email@example.com to get on the path to faster approval.
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