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510(k) Data Aggregation
(216 days)
The proposed device is to be used to improve the return circulation of body fluid that has pooled in one or more of a patient's extremities. This treatment reduces debilitating pain, swelling, ulceration, and risk of dermatological impact, infection, and amputation.
This is an electromechanical pneumatic device that employs a compressor and precision timer, regulator, and gage to sequentially inflate a sleeve appliance that fits over the patient's extremity. The device is approximately 8"W x 6.75"H x 5.5"D and weighs less than 15 lbs.
The prompt asks to describe the acceptance criteria and the study that proves the device meets them, based on the provided text. However, the provided text describes a "Wright Linear Pump" which is a sequential compression pump, not an AI/ML powered medical device. Therefore, many of the requested fields are not applicable to the given text.
Here's an analysis based on the provided information, noting where sections are not applicable:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Explicit or Implied) | Reported Device Performance |
|---|---|
| Functional / Performance: | |
| Produces sequential compression | Produces sequential compression |
| Max pressure < 101 mm Hg | Factory-preset for a maximum pressure less than 101 mm Hg |
| Cell pressures reach prescribed setting 10+ seconds before next cell activated | Pressures in each cell reach the prescribed setting 10 or more seconds before the next cell is activated. |
| Maximum pressure in proximal cell sustained before end of cycle | Maximum pressure in the proximal cell (cell C) is reached before the end of the cycle and, thus, sustained. |
| Disengage cells in quick succession (proximal to distal) ensuring no reverse gradient | Device disengages cells in quick succession, from proximal to distal, to ensure a reverse gradient is never applied despite any slight irregularities inherent in mechanical pneumatic systems. |
| Safety: | |
| Complies with physician-prescribed treatment pressures | Complies with physician-prescribed treatment pressures |
| Addresses potential for "slight irregularities inherent in mechanical pneumatic systems" | Designed to disengage cells in a specific sequence to avoid reverse gradient despite slight irregularities. |
2. Sample size used for the test set and the data provenance
- Sample Size for Test Set: Not applicable in this context. The "test set" described relates to non-clinical technical testing of the prototype device's mechanical functions, not a dataset for an AI/ML model.
- Data Provenance: Not applicable. The "data" refers to measurements from a prototype device's mechanical operation, not patient data or images.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Not applicable. Ground truth, in the context of AI/ML, refers to labels or diagnoses. Here, the "ground truth" for the non-clinical tests would have been the objective physical measurements themselves (e.g., pressure readings from precision regulators), not subjective expert assessment.
4. Adjudication method for the test set
- Not applicable. Adjudication methods are relevant for resolving disagreements among multiple human experts establishing ground truth for AI/ML validation. This device's testing involved direct physical measurements.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
- Not applicable. This is a mechanical medical device, not an AI-powered system designed to assist human readers (e.g., radiologists).
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- Not applicable. This is a mechanical device. The "standalone" performance here refers to the device operating according to its design specifications, without an AI algorithm.
7. The type of ground truth used
- Non-clinical test data: The ground truth was based on objective physical measurements from precision regulators and observation of the device's mechanical operation (e.g., timing of cell engagement/disengagement, pressure readings).
8. The sample size for the training set
- Not applicable. This is a mechanical device, not an AI/ML model that requires a training set. The "prototype device" was likely used for iterative design and testing, but not in the sense of an AI training set.
9. How the ground truth for the training set was established
- Not applicable. No AI training set.
Summary regarding the device and its testing based on the provided text:
The provided text details a mechanical sequential compression pump. The "study" described is a series of non-clinical tests conducted on a prototype device to verify its fundamental mechanical and pneumatic functions against design specifications. There are no mentions of clinical studies or any AI/ML components.
The device's performance was evaluated by directly measuring parameters like:
- Cell pressures using separate precision regulators.
- The timing of pressure activation and deactivation in different cells.
- The maximum pressure achieved.
The acceptance criteria are implied by these measurements confirming the device operates as designed, particularly in ensuring sequential compression, preventing reverse gradients, and staying within pressure limits. The primary "proof" that the device meets these criteria comes from these direct, objective physical measurements on the prototype.
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