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The iQ 2 Nasal Vented Mask and Phantom 2 Nasal Vented Mask are intended to be used with positive airway pressure devices, such as CPAP or bi-level, operating at 3 cmH2O to 30 cmH2O. The masks are to be used on adult patients (>30 kg) for whom positive airway pressure therapy has been prescribed. The masks are intended for single-patient multi-use in the home, hospital or institutional environment.

The Sleepnet iQ 2 and Phantom 2 Nasal Vented masks are nearly identical to the Predicate Sleepnet iQ 2 and Phantom 2 Nasal Mask (K211274). The subject of this submission is: Addition of a contraindication related to magnets on the iQ 2 Nasal Vented Mask and Phantom 2 Nasal Vented Mask; Update to the warning related to magnets on the iQ 2 Nasal Vented Mask and Phantom 2 Nasal Vented Mask. Device provides a seal over the nose to allow for delivery of pressurized air from a positive airway pressure device. Device has an exhalation port for flushing out exhaled CO2. The device is passive until connected to the positive pressure device.

This document is a 510(k) Premarket Notification from the FDA regarding the iQ 2 Nasal Vented Mask and Phantom 2 Nasal Vented Mask. The primary purpose of this submission is to address regulatory changes related to the addition of a contraindication regarding magnets and an update to the warning related to magnets for these devices.

The document states that the Sleepnet iQ 2 and Phantom 2 Nasal Vented masks are "nearly identical" to their predicate devices (Sleepnet iQ 2 and Phantom 2 Nasal Mask, K211274). Therefore, the study described here is fundamentally a comparative study to a predicate device rather than a de novo study proving the device meets new acceptance criteria independently. The acceptance criteria for the subject devices are the performance characteristics of the predicate device.

1. A table of acceptance criteria and the reported device performance

The acceptance criteria are implicitly defined by the performance of the predicate device (K211274). The reported device performance for the subject device (K241469) is stated to be "identical" to the predicate for all technical parameters, as no new performance testing was conducted.

Regarding the study that proves the device meets the acceptance criteria:

The provided document describes a 510(k) Premarket Notification which focuses on demonstrating substantial equivalence to a previously cleared predicate device (K211274). The core argument for meeting acceptance criteria is that the subject devices are "nearly identical" to the predicate, with the only changes being updates to contraindications and warnings regarding magnets.

Therefore, the "study" to prove the device meets acceptance criteria is primarily a comparison of characteristics and leveraging of previous test results.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: No new test set sample size is mentioned for the subject device (K241469) because "No new performance testing was conducted on the subject device, since the device designs are identical to the predicates." The acceptance criteria are based on the performance of the predicate device, for which the original testing details are not provided in this document.
• Data Provenance: Not explicitly stated for the original predicate testing, but the context implies data would have been generated as part of the K211274 submission.
• Retrospective or Prospective: Not specified, but generally, 510(k) submissions for devices leverage existing data or conduct prospective bench testing. In this case, existing data from the predicate was leveraged.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• This question is not applicable to this submission. The device is a medical mask, and the evaluation relies on direct device performance metrics (e.g., exhaust flow, dead space, resistance to flow, sound levels), not on expert interpretation of medical images or data requiring ground truth establishment by clinical experts. The "ground truth" for these engineering metrics would be established by validated test methods and equipment.
• The significant change in this submission relates to the contraindication and warning for magnets, which would have been driven by post-market surveillance or new scientific understanding concerning magnet interactions with medical implants (as indicated by "New as per recall event #94169"), likely involving medical experts but not in the context of establishing ground truth for a test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• This is not applicable. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies involving interpretation of subjective data (e.g., image reading) where disagreement among experts needs resolution. This submission focuses on objective engineering performance characteristics of a physical device.

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

• This is not applicable. MRMC studies are specific to evaluating AI/CAD systems that assist human readers in tasks like image interpretation. This submission is for a physical medical device (nasal mask) and does not involve AI assistance or human readers in that context.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• This is not applicable. This submission is for a physical medical device and does not involve an algorithm or AI.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• As explained in point 3, this is not applicable in the traditional sense of medical ground truth. The "ground truth" for the device's technical specifications (exhaust flow, dead space, resistance, sound) would be established through scientific measurement and adherence to relevant industry standards (bench testing), which were performed on the predicate device and deemed sufficient for the subject device due to identical designs.

8. The sample size for the training set

• This is not applicable. This submission is for a physical medical device and does not involve AI or machine learning that would require a training set.

9. How the ground truth for the training set was established

• This is not applicable for the same reasons as point 8.

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