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510(k) Data Aggregation

    K Number
    K092017
    Manufacturer
    Date Cleared
    2009-12-01

    (148 days)

    Product Code
    Regulation Number
    888.3080
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    PIVOTEC LUMBAR INTERBODY FUSION DEVICE (LIFD)

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Pivotec LIFD implant is an intervertebral body fusion device indicated for intervertebral body fusion at one level or two contiguous levels in the lumbar spine from L2 to S1 in patients with degenerative disc disease (DDD) with up to Grade 1 spondylolisthesis at the involved level(s). DDD is defined as back pain of discogenic origin with degeneration of the disc confirmed by patient history and radiographic studies. These patients should be skeletally mature and have had six months of non-operative treatment. The Pivotec LIFD Implant is designed for use with autograft to facilitate fusion and is intended for use with supplemental fixation systems cleared for use in the lumbar spine.

    Device Description

    The Pivotec LIFD is an intervertebral body fusion device for use with autogenous bone graft in the intervertebral disc space to stabilize spinal segments and promote fusion. The device is made of PEEK-Optima with Tantalum markers and is provided in various configurations and heights, containing a hollow core to receive bone autograft. Placement is achieved with an insertion instrument that allows for manipulation of the implant within the intra-vertebral disc space.

    AI/ML Overview

    The provided text describes a 510(k) summary for the Pivotec Lumbar Interbody Fusion Device (LIFD). This document focuses on demonstrating substantial equivalence to predicate devices, primarily through mechanical testing, rather than a clinical study evaluating diagnostic or prognostic performance in the way an AI/ML medical device might undergo.

    Therefore, many of the requested sections related to AI/ML device studies (like sample size for test sets, expert ground truth, MRMC studies, standalone performance, training set details) are not applicable to this type of device submission. The study described is a mechanical performance study.

    Here's a breakdown of the available information:

    Acceptance Criteria and Device Performance

    The acceptance criteria for this device are based on its mechanical performance, demonstrating functionality and safety comparable to legally marketed predicate devices. The "reported device performance" refers to the results of these mechanical tests.

    Acceptance Criteria CategorySpecific Criteria (Implicit from ASTM standards)Reported Device Performance
    Mechanical PerformanceStatic compression (conforms to ASTM F2077-03)"performed as designed"
    Dynamic compression (conforms to ASTM F2077-03)"performed as designed"
    Static compression shear (conforms to ASTM F2077-03)"performed as designed"
    Dynamic compression shear (conforms to ASTM F2077-03)"performed as designed"
    Subsidence (conforms to ASTM F2267-04)"performed as designed"
    Product SpecificationsAll product specifications"met, or exceeded, all product specifications"
    Material EquivalencePEEK-Optima with Tantalum markers, hollow coreSimilar to predicate devices
    Design EquivalenceVarious configurations and heightsSimilar to predicate devices
    Indications for Use EquivalenceDDD (L2-S1), up to Grade 1 spondylolisthesis, autograft, supplemental fixationSimilar to predicate devices
    Functional EquivalenceIntervertebral body fusion to stabilize and promote fusionSimilar to predicate devices

    Key Finding on Performance: "The device performed as designed and met, or exceeded, all product specifications."

    Study Details (Mechanical Testing)

    1. Sample size used for the test set and the data provenance:

      • Sample Size: Not explicitly stated for mechanical testing, but typically involves a number of device samples (e.g., n=5 or n=10 per test condition) to perform the specified ASTM tests. The focus here is on device physical properties, not patient data.
      • Data Provenance: Not applicable in the sense of patient data. The "data" comes from engineering laboratory testing of the manufactured devices.
    2. 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. Mechanical testing does not involve human experts establishing ground truth in the context of diagnostic or prognostic accuracy. The "ground truth" is defined by the objective physical properties and performance benchmarks established by the ASTM standards.
    3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

      • Not Applicable. Adjudication methods are used to resolve disagreements among human reviewers on clinical data. Mechanical testing involves objective measurements of physical properties.
    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 a mechanical device, not an AI/ML diagnostic or prognostic tool.
    5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

      • Not Applicable. This is a mechanical device.
    6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

      • Mechanical Standards: The "ground truth" (or benchmark for performance) is established by the relevant ASTM (American Society for Testing and Materials) standards:
        • ASTM F2077-03 (Standard Test Methods for Intervertebral Body Fusion Devices)
        • ASTM F2267-04 (Standard Test Method for Measuring Load Induced Subsidence of Intervertebral Body Fusion Device Under Static Axial Compression)
      • The device's performance is compared against the requirements and typical performance characteristics of predicate devices that have successfully met these standards.
    7. The sample size for the training set:

      • Not Applicable. This device uses mechanical testing, not a machine learning algorithm that requires a training set.
    8. How the ground truth for the training set was established:

      • Not Applicable. (See point 7).

    Conclusion of the Study:
    "The documentation provided demonstrates that the Pivotec LIFD is substantially equivalent to the predicate devices listed above. This conclusion is based on the devices' similarities in materials, design, indications for use, function and mechanical function."

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