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The XLR8 Plus (XLR8+) Wound Vacuum System is indicated for use in patients who would benefit from negative pressure wound therapy particularly as the device may promote wound healing by the removal of excess exudates, infectious material and tissue debris.

The XLR8 Plus (XLR8+) Wound Vacuum System is a portable, rechargeable battery powered wound suction pump with the intention to generate negative pressure to remove exudate from the wound.

This document describes a 510(k) premarket notification for the Genadyne XLR8 Plus (XLR8+) Negative Pressure Wound Therapy device. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving safety and effectiveness through extensive clinical trials as would be required for a novel device or a PMA.

Therefore, the typical metrics and study designs associated with proving a device meets specific acceptance criteria via a study (especially for AI/ML-driven devices with performance metrics like sensitivity, specificity, or AUC) are not present in this document. The "acceptance criteria" here are primarily met by demonstrating comparable performance and characteristics to the predicate device.

Here's an analysis based on the provided text, addressing your points where applicable:

1. Table of Acceptance Criteria and Reported Device Performance

For this 510(k) submission, the "acceptance criteria" are implicitly defined by the characteristics of the predicate device (A4-XLR8 Wound Vacuum System, K090638). The new device, XLR8 Plus, aims to be substantially equivalent. The table provided is a comparative table, not a pure acceptance criteria table with a "pass/fail" for an independent study.

Acceptance Criteria (Implied by Predicate Performance) and Reported Device Performance:

Note: For the 510(k) process, the new device can be better than the predicate in some aspects (like suction capacity or additional canister size) as long as it doesn't raise new questions of safety or effectiveness.

2. Sample Size for the Test Set and Data Provenance

This document does not describe a clinical study with a "test set" in the context of an AI/ML device validating performance on a dataset of patient cases. The testing conducted for this device is focused on engineering verification and validation (e.g., measuring suction, power, verifying dimensions, and confirming compliance with electrical safety standards). These tests are typically performed on a small number of production or pre-production units.

• Sample Size: Not specified in terms of patient data or clinical cases, as this is not an AI/ML driven device requiring such clinical validation. Laboratory (bench) testing of the device itself would likely involve a small number of units (e.g., 3-10).
• Data Provenance: Not applicable in the context of patient data. The provenance for the device's technical specifications would be from internal engineering and manufacturing. This is a physical medical device, not a software device processing patient data.

3. Number of Experts and Qualifications for Ground Truth

Not applicable. This device is a negative pressure wound therapy system, not an imaging analysis or diagnostic AI/ML device requiring expert interpretation for ground truth establishment.

4. Adjudication Method

Not applicable. There is no expert review or adjudication process outlined for this type of device submission. Performance is based on engineering specifications and bench testing.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Not applicable. This is not an AI/ML diagnostic or assistive device that would participate in an MRMC study to compare human reader performance with and without AI assistance.

6. Standalone (Algorithm Only) Performance

Not applicable. This is a physical medical device, not a standalone algorithm.

7. Type of Ground Truth Used

Not applicable. Ground truth for an AI/ML context doesn't apply here. The "truth" for this device lies in its adherence to engineering specifications, safety standards, and functional performance metrics (like creating negative pressure). Bench testing and electrical safety compliance form the basis of "truth."

8. Sample Size for the Training Set

Not applicable. This is not an AI/ML device that requires a training set.

9. How the Ground Truth for the Training Set Was Established

Not applicable. As above, no training set for an AI/ML model is involved.

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