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510(k) Data Aggregation

    K Number
    K132127
    Device Name
    TRANSCEND AUTO
    Date Cleared
    2013-10-17

    (99 days)

    Product Code
    Regulation Number
    868.5905
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Transcend Auto provides positive airway pressure for treatment of obstructive sleep apnea (OSA) in adults weighing over 66 pounds (30 kg). The device is intended for home and hospital/institutional use.

    Device Description

    The Transcend Auto is a microprocessor-controlled, blower-based system that generates positive airway pressure to support treatment of obstructive sleep apnea. It utilizes a differential pressure sensor connected to an internal pneumotach positioned in the airstream to determine flow levels. This flow signal, coupled with pressure sensing, is used to monitor breathing and adjust pressure. When interfaced with a mask, the system provides pressure from 4 to 20 cmHzO above the ambient atmospheric pressure to a patient's oral/nasal airway.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study information for the Transcend Auto device, based on the provided text:

    Acceptance Criteria and Reported Device Performance

    CriteriaAcceptance Criteria (Predicate Device)Reported Device Performance (Transcend Auto)
    Pneumatic Performance:
    Therapy Pressure (cm H2O)4-204-20
    Pressure RegulationTranscend CPAP: ±1 cm H2O or 10%, whichever is greaterREMstar Auto CPAP: <10.0 cm H2O (±0.5 cm H2O), ≥10.0 to 20.0 cm H2O (±1.0 cm H2O)±1 cm H2O or 10%, whichever is greater (Identical to Transcend CPAP, equivalent to REMstar)
    Ramp Time (min)0-45 (5 min increments)0-45 (5 min increments)
    Environmental Performance:
    Operating Altitude (ft)Transcend CPAP: 0 to 8,000REMstar Auto CPAP: 0 - 75000 to 8,000 (Identical to Transcend CPAP)
    Operating Temperature (°C / °F)5° C (41° F) to 35° C (95° F)5° C (41° F) to 35° C (95° F)
    Operating Humidity (% relative humidity)Transcend CPAP: 10 - 80%, non-condensingREMstar Auto CPAP: 15 - 95%, non-condensing10 - 80%, non-condensing (Identical to Transcend CPAP)
    Shipping/Storage Temperature (°C / °F)-20° C (-4° F) to 60° C (140° F)-20° C (-4° F) to 60° C (140° F)
    Shipping/Storage Humidity (% relative humidity)Transcend CPAP: 10 - 90%, non-condensingREMstar Auto CPAP: 15 - 95%, non-condensing10 - 90%, non-condensing (Identical to Transcend CPAP)
    Functional Features:
    Auto AdjustTranscend CPAP: NoREMstar Auto CPAP: YesYes (Equivalent to REMstar)
    Expiratory Pressure ReliefTranscend CPAP: NoREMstar Auto CPAP: YesYes (Identical to REMstar)
    Data Storage/DownloadTranscend CPAP: PC connectionREMstar Auto CPAP: Data card; PC connectionPC connection (Identical to Transcend CPAP)
    Electrical/Safety:
    Power Supply (VAC, Hz)100-240 VAC, 50/60 Hz100-240 VAC, 50/60 Hz
    IEC 60601 ClassificationClass II, Type BFClass II, Type BF
    Other:
    Warranty (years)22
    Intended Population of UseAdultAdult

    Study Information

    1. Sample Size Used for the Test Set and Data Provenance:

      • Sample Size: 41 adult patients.
      • Data Provenance: The document does not explicitly state the country of origin. It does state that the study was a clinical evaluation, which implies it was prospective.
    2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

      • The document describes a clinical evaluation based on "apnea hypopnea index (AHI) during treatment." While an AHI is a clinical metric derived from sleep study data, the document does not specify if multiple independent experts reviewed the sleep studies to establish this "ground truth" or the qualifications of any such experts. It focuses on the device's performance in reducing AHI, rather than the process of establishing AHI as ground truth.
    3. Adjudication Method for the Test Set:

      • The document does not describe an adjudication method for establishing ground truth for the AHI. The focus is on the measured AHI during treatment rather than a consensus interpretation of raw data.
    4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

      • No, an MRMC comparative effectiveness study was not done in the context of human readers analyzing cases. This study was a clinical evaluation comparing two devices (Transcend Auto vs. REMstar Auto) based on their therapeutic effectiveness (AHI reduction) in patients. The comparison was of device performance, not human reader performance with or without AI assistance.
    5. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

      • Yes, the clinical evaluation essentially represents a standalone performance. The Transcend Auto device automatically adjusts pressure without direct human intervention during the night. The study assessed the device's ability, by itself, to achieve non-inferiority in AHI compared to a predicate auto-CPAP device.
    6. Type of Ground Truth Used:

      • The ground truth used was clinical outcome data, specifically the apnea hypopnea index (AHI) during treatment. AHI is a measure of the severity of sleep apnea based on the number of apneas and hypopneas per hour of sleep. The study aimed to demonstrate non-inferiority in reducing this index.
    7. Sample Size for the Training Set:

      • The document does not provide information about a separate "training set" for the Transcend Auto's algorithm. The study described is a clinical validation study, not an algorithm development study where training sets are typically discussed.
    8. How the Ground Truth for the Training Set Was Established:

      • As no training set is described, there is no information on how its ground truth might have been established. It's common for such devices to rely on established physiological principles and prior clinical knowledge in their auto-adjusting algorithms, rather than training on a specific, explicitly defined dataset in the way a machine learning model would be.
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