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510(k) Data Aggregation

    K Number
    K132403
    Manufacturer
    Date Cleared
    2014-01-10

    (162 days)

    Product Code
    Regulation Number
    888.3070
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    ANNEXTM ADJACENT LEVEL SYSTEM

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

    When used with pedicle screw fixation systems of the noncervical spine in skeletally mature patients, the Annex™ Adjacent Level System is indicated for degenerative spondylolisthesis with objective evidence of neurologic impairment, fracture, dislocation, scoliosis, kyphosis, spinal tumor, and failed previous fusion (pseudoarthrosis).

    The Annex™ Adjacent Level System is also indicated for pedicle screw fixation in skeletally mature patients with severe spondylolisthesis (Grades 3 and 4) at the LS-S1 vertebral joint, having fusions with autogenous bone graft, with the device fixed or attached to the lumbar and sacral spine (levels of pedicle screw fixation are L3-S1), and for whom the device is intended to be removed after solid fusion is attained.

    The Annex™ Adjacent Level System can be linked to 5.5mm diameter rod systems, including the CapSure® or Sniper® Spine Systems.

    Device Description

    The Annex™ Adjacent Level System consists of a selection of non-sterile, single use connectors manufactured from titanium alloy (ASTM F136) and cobalt chrome alloy (ASTM F1537), for attachment to an existing 5.5mm diameter rod construct to extend a rigid spinal construct. The connectors are provided in a variety of sizes and shapes to accommodate variations in anatomy and spacing/positioning of existing screw and rod hardware.

    AI/ML Overview

    The Spine Wave Annex™ Adjacent Level System is a medical device, and the provided document details its 510(k) submission to the FDA. For medical devices seeking 510(k) clearance, the "acceptance criteria" are typically demonstrating substantial equivalence to a predicate device, primarily through performance testing and comparison of technological characteristics, rather than meeting specific statistical metrics against a ground truth as would be common for AI/ML devices.

    Here's an analysis based on the provided text, structured to answer your questions where applicable:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Demonstrates Substantial Equivalence via Performance Testing)Reported Device Performance
    Mechanical Performance:
    • Static axial compression bending
    • Dynamic axial compression bending
    • Static torsion testing | The device underwent static axial compression bending, dynamic axial compression bending, and static torsion testing according to ASTM F1717. These tests were performed to demonstrate that the Annex™ Adjacent Level System is substantially equivalent to the predicate devices. |
      | Material Compatibility:
    • Materials used in the device are suitable for the intended use and similar to predicate devices. | The device consists of connectors manufactured from titanium alloy (ASTM F136) and cobalt chrome alloy (ASTM F1537). These materials are commonly used in spinal implants and are consistent with industry standards and predicate devices. |
      | Intended Use Compatibility:
    • The system's use for extending existing 5.5mm diameter rod constructs is functionally sound. | The system is designed to attach to an existing 5.5mm diameter rod construct to extend a rigid spinal construct. It can link to 5.5mm diameter rod systems, including the CapSure® or Sniper® Spine Systems, demonstrating compatibility with existing spinal fixation systems. |

    2. Sample Size Used for the Test Set and Data Provenance

    The provided document describes mechanical performance testing (static and dynamic axial compression bending, static torsion) of the device itself, not a clinical study involving human patients or a software algorithm evaluating data. Therefore, there isn't a "test set" of patient data or data provenance in the typical sense for algorithm evaluation.

    The "sample size" for the mechanical tests would refer to the number of physical device units tested for each specific test (e.g., how many screws, rods, or connectors were subjected to compression bending). This information is not provided in the summary. The provenance is the testing laboratory that performed the ASTM F1717 tests, but specific details are not given.

    3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

    This concept is not applicable to this 510(k) submission. The "ground truth" for this device is its mechanical integrity and performance, established through standardized ASTM F1717 testing, not through expert consensus on medical images or clinical outcomes.

    4. Adjudication Method for the Test Set

    This concept is not applicable. Mechanical testing results are objective measurements (e.g., load at failure, displacement under load) and do not require expert adjudication.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

    No, an MRMC comparative effectiveness study was not done. This type of study is typically conducted for diagnostic devices (e.g., AI in radiology) to assess human reader performance with and without AI assistance. The Annex™ Adjacent Level System is a physical spinal implant, not a diagnostic or AI-powered device.

    6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

    No, a standalone algorithm performance study was not done. The device is a physical medical implant, not a software algorithm.

    7. The Type of Ground Truth Used

    The "ground truth" employed in this submission is the specified mechanical performance standards outlined in ASTM F1717. The device's performance was compared against these objective engineering standards and against the established performance of the legally marketed predicate devices.

    8. The Sample Size for the Training Set

    There is no training set for this device submission. This is a physical medical device, not an AI/ML algorithm that requires a training set.

    9. How the Ground Truth for the Training Set Was Established

    This question is not applicable as there is no training set.

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