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The Mirage Swift is intended for Multiple Patient Reuse by adult patients (>30 Kg) prescribed continuous positive airway pressure or bilevel therapy for use in home, hospitals or clinics.

The Mirage Swift is designed for adult patients for the delivery of non-invasive ventilatory support using continuous positive airway pressure or bi-level therapy. It is intended for multiple patient re-use and is minimally obtrusive to the user providing a high level of comfort, ease-ofuse and seal.

The provided document is a 510(k) summary for the ResMed Mirage Swift Nasal Mask, seeking clearance for "Multiple Patient Reuse" after initially being cleared as a single patient reuse device.

Based on the provided text, there is no specific performance study with acceptance criteria and a detailed report of device performance as one might expect for a new AI/software device. This document is for a physical medical device (a CPAP mask) and focuses on demonstrating substantial equivalence to predicate devices for an expanded indication for use (multiple patient reuse), not for establishing performance metrics of a novel diagnostic or therapeutic algorithm.

Therefore, many of the requested points regarding acceptance criteria, sample sizes for test/training sets, expert adjudication, MRMC studies, or standalone algorithm performance are not applicable or discoverable from this particular 510(k) summary.

Here's an attempt to answer the questions based only on the provided text:

1. Table of acceptance criteria and the reported device performance

The document does not explicitly state quantitative acceptance criteria or a direct performance report in the context of a new diagnostic/therapeutic device. Instead, the "acceptance criterion" is implicitly demonstrating substantial equivalence to predicate devices that are already cleared for multiple patient reuse. The reported "performance" is that this substantial equivalence has been met.

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

Not applicable. This is not a study assessing diagnostic performance against a test set. The "performance data" mentioned would likely pertain to cleaning, disinfection, and material compatibility relevant to multiple patient reuse, rather than a clinical outcome study with a "test set" of patients.

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)

Not applicable. "Ground truth" in the context of expert consensus for a diagnostic test is not relevant here. The "ground truth" for multiple patient reuse would be established through testing cleaning efficacy, material degradation after disinfection cycles, and biocompatibility, typically performed in a lab setting rather than by clinical experts establishing a diagnosis.

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

Not applicable. There is no "test set" in the context of expert adjudication for this type of submission.

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 document is about a physical CPAP mask, not an AI or software-assisted diagnostic device. Therefore, no MRMC study or AI improvement metrics are relevant or present.

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

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

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

The concept of "ground truth" as it applies to diagnostic algorithms is not relevant here. For the "Multiple Patient Reuse" claim, the "ground truth" would be objective evidence from laboratory testing demonstrating that the device can be effectively cleaned and disinfected according to a validated protocol without material degradation or compromising safety/performance, making it suitable for subsequent patient use. The document states that "Performance data and rationale are provided" to support this.

8. The sample size for the training set

Not applicable. This is a physical medical device, not an algorithm that requires a "training set."

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

Not applicable. As above, this is not an algorithm requiring a "training set" or "ground truth" in that context.

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Nasal-Aire Critical Care ventilator interface is intended for use with a CPAP or Bi-Level device to provide noninvasive ventilation in adult patients for the treatment of respiratory insufficiencies or obstructive sleep apnea. This single use disposable interface is intended for hospital settings.

Nasal-Aire Criticial Care is ventilator interface accessory intended for use with a CPAP or Bi-Level device to provide noninvasive ventilation in adult patients for the treatment of respiratory insufficiencies or obstructive sleep apnea. This single use disposable interface is intended for hospital settings.

Nasal-Aire Critical Care consists of a soft nosepiece with two exhaust ports and two soft nasal cannula for fitting into the nasal openings. Connected on each side of the nosepiece is a length of tubing. The two lengths of tubing are joined at the opposite ends by a "Y"-shaped coupling, forming a loop with the nosepiece at one end and the "Y" coupling at the other. At the base of the "Y" coupling is a port for connection of the Nasal-Aire Critical Care to a positive pressure ventilation device. The Nasal-Aire Critical Care is compatible with any positive pressure ventilator containing a 22mm connection. Nasal-Aire Critical Care will be offered in various sizes designed for a comfortable and compliant fit for a wide variety of nare sizes.

The provided document is a 510(k) summary for the Nasal-Aire Critical Care device. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than providing extensive clinical study data with acceptance criteria or performance metrics directly comparable to what is typically found in AI/ML device studies.

Therefore, the requested information, specifically regarding acceptance criteria and a study proving the device meets those criteria (especially in the context of an AI/ML device study), is largely not present in this regulatory document. This document outlines functional equivalence to existing devices through comparison of attributes.

Here's an analysis based on the provided text, highlighting what is (and isn't) available:

1. A table of acceptance criteria and the reported device performance
   The document does not explicitly state "acceptance criteria" for performance metrics in the way one would see for an AI/ML diagnostic or prognostic device (e.g., target sensitivity, specificity). Instead, it relies on a comparison of attributes of the "Nasal-Aire Critical Care (Subject device)" against several "Predicate devices" to demonstrate substantial equivalence.

   The "performance" here is described in terms of functional attributes and intended use. The table below summarizes the comparison, which serves as the basis for demonstrating that the device is "as safe and effective as the predicate devices."

   | Attribute | Nasal-Aire Critical Care
   (Subject device) | Predicate Devices (Composite from listed predicates) |
   | :-------------------------------------------------------- | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
   | Intended Use | Intended for use with CPAP or Bi-Level device to provide noninvasive ventilation for the treatment of respiratory insufficiencies or obstructive sleep apnea. | Similar intended uses, including non-invasive treatment for respiratory insufficiency and/or obstructive sleep apnea with CPAP/bi-level therapy. |
   | Intended Environment | Hospital/Institutional Use: Yes | Hospital/Institutional Use: Yes |
   | Offered in Various sizes | Yes | Yes (for interfaces) |
   | Intended for Single Patient Use | Yes | Yes (for interfaces, some predicates indicated "No, multiple patient" for full mask series, but the Nasal-Aire is single-use) |
   | Provided Non-Sterile | Yes | Yes (for interfaces) |
   | Compatible with 22mm ventilator connection | Yes | Yes |
   | Treatment delivered through Nasal Passages | Yes | Yes (for interfaces; one predicate mentions "Yes and mouth") |
   | Pressure delivery | 3-35 cm H2O: Yes | 3-35 cm H2O: Yes |
   | Allows for pressure monitoring | Yes | Yes |
   | Intended Population | Adult | Adult |
   | Patient Contact Material | Silicone | Silicone |
   | Interface: Tubing Length | 7" | Varies (15/18" for one predicate, unknown for others; 7" is within reasonable range for such a device compared to longer ventilator tubing) |
   | Latex Free | Yes | Yes (where specified) |

   Acceptance Criteria (Implied): The implied acceptance criteria are the attributes and performance characteristics of the predicate devices. The "study" (or rather, the justification for substantial equivalence) involved demonstrating that the Nasal-Aire Critical Care met or was comparable to these attributes.

2. Sample sizes used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
   This information is not provided in the 510(k) summary. Given that this is a physical medical device (ventilator interface) and not an AI/ML software device, the concept of "test set" in the context of data validation is not applicable in the way it would be for AI/ML. The "study" mentioned ("Biocompatability requirements and functional testing") would involve laboratory tests and potentially limited human factor testing, but details on sample sizes or data provenance are not included in this summary.

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 information is not provided and is not typically relevant for a 510(k) submission for a physical device like a ventilator interface. "Ground truth" in the context of expert consensus is specific to diagnostic AI/ML algorithms, not general medical devices. Functional testing would rely on engineering standards and measurement accuracy, not expert consensus on interpretations.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
   This information is not provided and is not relevant for this type of device submission. Adjudication methods are specific to processes involving human interpretation outcomes for establishing ground truth, typically in AI/ML validation.

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 information is not provided. MRMC studies are specific to evaluating AI/ML systems that assist human readers in tasks like image interpretation. This device is a physical ventilator interface, not an AI/ML assistant, so such a study would not apply.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
   This information is not provided. This question is specific to AI/ML algorithms and is not applicable to a physical medical device like the Nasal-Aire Critical Care.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
   For the functional and biocompatibility testing mentioned, the "ground truth" would be established by scientific and engineering standards, not expert consensus, pathology, or outcomes data in the sense used for AI/ML or clinical trials. For instance, biocompatibility testing would adhere to ISO standards, and functional testing would involve measuring parameters against predefined specifications (e.g., pressure delivery, connection compatibility). The document states "Biocompatability requirements and functional testing relative to the intended use of the Nasal-Aire Critical Care have been satisfactorily completed," implying compliance with relevant standards.

8. The sample size for the training set
   This information is not provided and is not applicable. There is no "training set" for a physical device in the way there is for an AI/ML algorithm.

9. How the ground truth for the training set was established
   This information is not provided and is not applicable for the same reasons as point 8.

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