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510(k) Data Aggregation

    K Number
    K162133
    Date Cleared
    2017-05-16

    (288 days)

    Product Code
    Regulation Number
    888.3060
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    VerteFIT Corpectomy Cage System

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

    The VerteFIT™ Corpectomy Cage System is a vertebral body replacement device intended for use in the thoracolumbar spine (T1 to L5) to replace collapsed, damaged or unstable vertebral bodies due to tumor or trauma (i.e., fracture). The VerteFIT Corpectomy Cage System is designed to provide anterior spinal column support even in the absence of fusion for a prolonged period. The VerteFIT Corpectomy Cage System device may be used with allograft or autograft. The VerteFIT implants are intended to be used with supplemental spinal fixation systems that have been cleared by FDA for use in the thoracic and/or lumbar spine (e.g., posterior pedicle screw and rod systems, and anterior screw and rod systems).

    Device Description

    VerteFIT Corpectomy Cages are vertebral body replacement devices used to provide structural stability in skeletally mature individuals following corpectomy or vertebrectomy. The components include central PEEK cores and titanium-coated PEEK endcaps which are available in a range of sizes and options to accommodate the anatomical needs of a wide variety of patients. The core and endcaps can be intraoperatively assembled to best fit patient anatomy requirements. Each endcap has a central axial chamber to allow autogenous bone graft or allograft to be packed inside of the spacer. Unidirectional teeth on the superior and inferior surfaces grip the endplates of the adjacent vertebrae to resist expulsion. VerteFIT endcaps (superior and inferior) have slots to accommodate VerteBRIDGE anchoring plates.

    AI/ML Overview

    This document describes the VerteFIT™ Corpectomy Cage System, a vertebral body replacement device. The provided text outlines the device's indications for use and non-clinical performance data, but it does not describe an acceptance criteria table, a study proving device adherence to acceptance criteria, nor does it contain information typically associated with studies of AI/ML devices (e.g., sample sizes for training/test sets, ground truth establishment, expert qualifications, MRMC studies, or standalone performance).

    The information provided pertains to the non-clinical performance data which focuses on mechanical testing to demonstrate substantial equivalence to predicate devices, rather than clinical performance data or AI/ML-specific study details.

    Here's a breakdown of the available information based on your request:

    1. A table of acceptance criteria and the reported device performance

    • Acceptance Criteria: Not explicitly stated in a table format. The document implies compliance with standards and guidance documents, such as "Guidance for Industry and FDA Staff, Guidance for Spinal System 510(k)s., May 3, 2004" and various ASTM standards (F2077, F2267, F2129).
    • Reported Device Performance: The document states that "Mechanical testing was conducted... to demonstrate substantial equivalence to the predicate system(s)." Specific numerical performance results or whether these results "met" an explicit acceptance criterion are not detailed in this summary.

    2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    • Sample Size: Not applicable. The document refers to mechanical testing, not clinical studies involving patient data.
    • Data Provenance: Not applicable. No patient data or clinical data is mentioned.

    3. 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. This is a mechanical device, and "ground truth" as typically understood in AI/ML clinical studies is not relevant. Mechanical testing relies on engineering standards and measurements.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

    • Not applicable. No form of adjudication is mentioned as it's not a clinical study involving human interpretation.

    5. 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 document pertains to a physical medical device (spinal implant), not an AI/ML diagnostic or assistive device. No MRMC study or human reader performance is mentioned.

    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

    • Not applicable. This is a physical medical device, not an algorithm.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

    • Not applicable. For mechanical testing, the "ground truth" is adherence to established engineering specifications and performance within defined limits of the ASTM standards mentioned.

    8. The sample size for the training set

    • Not applicable. There is no AI/ML component; therefore, no training set.

    9. How the ground truth for the training set was established

    • Not applicable. There is no AI/ML component; therefore, no training set ground truth.

    Summary of Non-Clinical Performance Data (as provided):

    • Tests Conducted:
      • Static & dynamic compression (ASTM F2077)
      • Static & dynamic shear compression (ASTM F2077)
      • Static & dynamic torsion (ASTM F2077)
      • Subsidence (ASTM F2267)
      • Cage and anchor expulsion (F-04.25.05.02)
      • Corrosion testing (ASTM F2129)
    • Purpose: To demonstrate substantial equivalence to predicate device(s) based on mechanical performance.
    • Clinical Performance Data: "Clinical testing was not required to demonstrate substantial equivalence."

    This document focuses on the regulatory clearance for a physical medical device through a 510(k) pathway, which typically involves demonstrating substantial equivalence to a legally marketed predicate device, often through non-clinical performance testing and comparison of technical characteristics, rather than extensive clinical efficacy studies or AI/ML performance evaluations.

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