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510(k) Data Aggregation

    K Number
    K160109
    Date Cleared
    2017-05-19

    (486 days)

    Product Code
    Regulation Number
    868.5630
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The LiteAire is a collapsible, disposable dual-valved holding chamber designed to aid in the delivery of aerosolized medications delivered via a pressurized metered dose inhaler (MDI).

    The LiteAire features a standard port designed for compatibility with standard MDI mouthpieces. It is a non-sterile device for single-patient use.

    The LiteAire is intended to be used by adults, adolescents and children ages 5 and up who are able to use a holding chamber without the aid of a mask and who are under the care or treatment of a physician or licensed healthcare professional.

    Device Description

    The Thayer Medical LiteAire Dual-valved, Collapsible MDI Holding Chamber is intended for use in the inhalation of medications delivered via a pressurized metered dose inhaler (pMDI). The device consists of a collapsible paperboard housing and 2 one-way valves to control the direction of air flow when the patient inhales and exhales through the device. The LiteAire is not sterile, but clean and ready to use right out of the package. The LiteAire can be assembled by gently pushing in the edges of the device. The holding chamber can also be collapsed flat between uses and is anti-static.

    AI/ML Overview

    The provided text describes modifications to a medical device (LiteAire Dual-Valved, Collapsible MDI Holding Chamber) and evaluates its substantial equivalence to a predicate device. The information presented focuses on non-clinical performance testing rather than a clinical study involving human readers or AI.

    Here's an analysis of the provided text in the context of your questions:

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

    The document discusses performance in the context of non-clinical testing, specifically regarding the aerosolization efficiency of the device with different MDI products. The "acceptance criteria" are implied by the comparison to the predicate and the general expectation for an MDI holding chamber to deliver appropriate doses. The "reported device performance" is given in tables for Total Delivered Dose, Coarse Particle Dose, Fine Particle Dose, MMAD, and GSD.

    Acceptance Criteria (Implied / Functional Requirements):

    • Aerosol Delivery Performance: The ability to effectively deliver the prescribed dose from an MDI, with appropriate particle size distribution, comparable to or better than predicate devices. This is assessed via "Total Emitted Dose, Course Particle Dose or Fine Particle Dose."
    • Mechanical Integrity: Maintain structural integrity and functionality (pop-ability, collapsibility, valve resistance) over expected shelf-life and under environmental stresses.
    • Biocompatibility: No adverse biological reactions when in contact with lung tissue, skin, and mucosal membranes.
    • Anti-static Properties: Exhibit anti-static properties to minimize drug adherence to the chamber walls.

    Reported Device Performance:

    The document provides specific data in Tables 2 and 3 for the subject device (K160109) with three different MDI products (Proventil HFA, Atrovent HFA, QVAR 80 mcg) at adult (28 L/min) and pediatric (12 L/min) flow rates. The values reported are 95% confidence intervals.

    Table 1: Device Performance (Excerpts from Tables 2 & 3 Combined)

    Performance MetricMDI Product Tested (Example Range)Flow Rate
    Total Delivered Dose (µg/actuation)39.77 – 77.62Adult/Pediatric
    Coarse Particle Dose (>4.7 µm) (µg/actuation)0 – 3.42Adult/Pediatric
    Fine Particle Dose (<4.7 µm) (µg/actuation)39.81 – 75.34Adult/Pediatric
    MMAD (µm)0.48 – 2.44Adult/Pediatric
    GSD (µm)0 – 6.08Adult/Pediatric

    Note: The specific ranges reflect the minimum and maximum observed across all MDI products and flow rates for each metric in the tables.

    Additional Performance Points:

    • Shelf-life testing: "There were no differences in appearance, pop-ability and collapsibility, Total Emitted Dose, Course Particle Dose or Fine Particle Dose between the new and aged devices. Furthermore, the inhalation and exhalation valve resistances of both devices were found to be acceptably low."
    • Environmental and mechanical testing (Temperature and Humidity): "There were no differences in Total Emitted Dose, Course Particle Dose, Fine Particle Dose and breakability upon dropping between the new devices and devices exposed to high temperature and humidity. Furthermore, the inhalation and exhalation valve resistances of 'exposed' devices were found to be acceptably low."
    • Anti-static surface resistivity testing: "The LiteAire has a surface resistivity of 1.0 x 10^10 Ω/sq (at 23°C/ 50% RH). This meets the requirement for being 'dissipative' or 'anti-static'."

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

    • Sample Size: For Particle Characterization testing (Tables 2 & 3), the sample size was:
      • 3 LiteAire devices / 3 ACI replicates each = 9 ACI runs/drug for each MDI product (Proventil HFA, Atrovent HFA, QVAR 80 mcg) and each flow rate (Adult 28 L/min, Pediatric 12 L/min).
    • Data Provenance: The document does not specify the country of origin for the data. The data appears to be prospective wet-lab testing conducted for the purpose of the 510(k) submission, not data collected from a clinical patient population.

    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 the provided document. The "ground truth" for this device's performance is established through physical and chemical measurements (e.g., cascade impaction for particle size, resistance measurements for valves, surface resistivity) and biocompatibility testing, not through expert human interpretation of medical images or patient outcomes. Therefore, no experts in the sense of medical diagnosticians were used to establish "ground truth" for this type of performance study.

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

    This question is not applicable. Adjudication methods are typically used in studies involving human interpretation or subjective assessments to resolve discrepancies. The performance tests described here are objective, quantifiable 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.

    This question is not applicable. The study described is a non-clinical performance evaluation of a mechanical medical device (MDI holding chamber), not a study involving AI assistance to human readers for diagnostic interpretation.

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

    This question is not applicable. There is no algorithm or AI involved in the performance evaluation described. The device is a physical, non-electronic medical device.

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

    The "ground truth" for this device performance evaluation is based on objective physical and chemical measurements performed in a laboratory setting, in accordance with recognized standards (e.g., ISO 10993 for biocompatibility, ASTM-D-257 for anti-static properties, and implicit standards for aerosol characterization via cascade impaction). This is not a clinical ground truth derived from patient outcomes, pathology, or expert consensus. It's a "ground truth" of the device's functional characteristics.

    8. The sample size for the training set.

    This question is not applicable. This is a performance study of a physical device, not an AI model. Therefore, there is no "training set" in the context of machine learning.

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

    This question is not applicable for the same reason as #8.

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