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510(k) Data Aggregation

    K Number
    K132914
    Manufacturer
    Date Cleared
    2014-04-03

    (198 days)

    Product Code
    Regulation Number
    870.1250
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The ACUITY™ Pro Lead Delivery System is intended to access the coronary venous system, and may be used alone (9F) or in a dual catheter delivery (9F with 7F). The catheter serves as a conduit for the delivery of contrast medium and devices, including implantable coronary venous leads, introduced into the coronary venous system.

    Device Description

    The ACUITY™ Pro Lead Delivery System is designed for venous use to aid in the selective placement of cardiac resynchronization therapy (CRT) implantable venous leads in the cardiac vasculature. The catheter shafts are comprised of a PTFE inner iner, a reinforcing layer of stainless steel braid, and an outer polymer jacket. The distal end has a radiopaque polymer tip, while the proximal end has a hub with flush luer fitting to allow flush, contrast injection and aspiration polymer .

    The approximate working lengths of the catheters are 45-54cm for the 9F design and 60-69 cm for the 7F design.

    The ACUITY Pro 9F is provided with the following accessories:

    Guidewire Introducer Guidewire Torquer

    Venous Access Dilator ACUITY™ Universal Cutter

    Transvalve Introducer Tool (2)

    AI/ML Overview

    The provided document describes the ACUITY™ Pro Lead Delivery System, a percutaneous guide catheter. However, it does not contain acceptance criteria for device performance or a study that specifically proves the device meets such criteria in the context of an AI/ML device.

    The document details a Traditional 510(k) submission, which focuses on demonstrating substantial equivalence to predicate devices through a summary of non-clinical testing. This regulatory pathway does not typically involve the type of clinical performance studies and acceptance criteria that would be relevant for an AI/ML device.

    Therefore, the following information cannot be extracted from the provided text:

    • A table of acceptance criteria and the reported device performance
    • Sample size used for the test set and the data provenance
    • Number of experts used to establish the ground truth for the test set and their qualifications
    • Adjudication method for the test set
    • Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done, or the effect size of human readers with vs. without AI assistance.
    • Whether a standalone (algorithm only) performance study was done.
    • The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
    • The sample size for the training set
    • How the ground truth for the training set was established

    What the document does provide is a summary of non-clinical (bench) testing performed to demonstrate substantial equivalence of a medical device (a guide catheter) to its predicates, which is a different type of evaluation than what is asked for regarding an AI/ML device.

    The summary of non-clinical testing includes:

    • Dimensional Verification
    • Tensile
    • Pushability and Shaft Stiffness
    • Shaft Cutting
    • Kink Resistance
    • Curve Shape
    • Radiopacity
    • Tip Deflection
    • Torque Strength
    • Hub Leak
    • Hub Separation
    • Hub Cutting / Catheter Removal
    • Compatibility With Accessories And Adjunctive Devices
    • Lead Passage
    • Product Marking And Identification
    • Product Integrity
    • Ease Of Removal Of Device And Accessories From Packaging
    • Particulates
    • Microbiology Endotoxin
    • EO Residuals
    • Biocompatibility Testing (Cytotoxicity, Sensitization, Irritation Or Intracutaneous Reactivity, Systemic Toxicity (Acute), Hemocompatibility, Latex, USP Physicochemical)

    The document explicitly states: "Clinical Evaluation was not required for these devices." This reinforces that the evaluation was based on bench testing for substantial equivalence, not clinical performance for an AI/ML algorithm.

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