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510(k) Data Aggregation

    K Number
    K032438
    Manufacturer
    Date Cleared
    2004-06-30

    (328 days)

    Product Code
    Regulation Number
    880.5200
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The proposed device is indicated for use in flushing compatible intravenous tubing systems and indwelling intravascular access devices.
    The 0.9% Sodium Chloride Flush Syringe is indicated for use in flushing compatible intravenous tubing systems and indwelling intravascular access devices.
    The Heparin Lock Flush Syringe, 10 and 100 units/ml is intended for use in flushing compatible intravenous administration sets and industrials and individually

    Device Description

    The proposed device is a sterile, single use, standard piston syringe of various sizes and fill volumes containing either 10 or 100 USP Heparin units/ml, or, 0.9% Sodium Chloride USP.

    AI/ML Overview

    This document describes the Kendall Monoject® Prefill Flush Syringes, which contain either heparin or 0.9% sodium chloride, and are intended for flushing intravenous tubing and access devices. The submission focuses on substantial equivalence to predicate devices, particularly regarding a change in the manufacturing process rather than an AI/ML device. Therefore, many of the requested criteria for AI/ML devices, such as those related to AI-specific performance metrics, reader studies, and ground truth establishment, are not applicable or not provided in this regulatory document.

    Here's the information that can be extracted or inferred from the provided text, primarily focusing on the non-AI aspects of the device and its testing:


    Acceptance Criteria and Reported Device Performance

    Given that this is a 510(k) summary for a medical device (pre-filled syringes) and not an AI/ML device, the "acceptance criteria" and "reported device performance" are based on traditional non-clinical testing for safety, function, and stability, rather than diagnostic accuracy metrics. The document states:

    Acceptance Criteria CategoryReported Device Performance (Summary)
    Microbiological TestingVerification testing was performed.
    Physical TestingVerification testing was performed.
    Functional TestingVerification testing was performed.
    Product Stability TestingVerification testing was performed.

    Note: The document only states that "Verification testing for the proposed change involved microbiological, physical, functional and product stability testing." It does not provide specific acceptance thresholds or detailed numerical results for these tests. The outcome of these tests was a determination of substantial equivalence (K032438).

    Study Details

    • 1. Sample sized used for the test set and the data provenance: Not applicable or not specified for an AI/ML device context. The verification testing mentioned (microbiological, physical, functional, product stability) would have involved sample sizes appropriate for those specific types of engineering tests. No details on these sample sizes are provided.
    • 2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. This is not an AI/ML device requiring expert ground truth for classification or diagnosis.
    • 3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This is not an AI/ML diagnostic or classification device.
    • 4. 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 is not an AI/ML device and no MRMC study was conducted or is relevant.
    • 5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an AI/ML device.
    • 6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable in the context of AI/ML ground truth. For the non-clinical testing, the "ground truth" would be established by validated test methods and specifications for sterility, physical properties (e.g., flow rate, burst strength), functional performance (e.g., proper flush), and stability (e.g., shelf-life studies).
    • 7. The sample size for the training set: Not applicable. This is not an AI/ML device.
    • 8. How the ground truth for the training set was established: Not applicable. This is not an AI/ML device.
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