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510(k) Data Aggregation

    K Number
    K233056
    Device Name
    EpiZact
    Date Cleared
    2023-11-02

    (38 days)

    Product Code
    Regulation Number
    880.5860
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    EpiZact is intended for use with an epidural needing a loss of resistance, which ads a clinician in verifying needle tip placement in the epidural space.

    Device Description

    EpiZact is a self-pressurizing piston syringe that uses the same loss of resistance technique used by physicians to detect the epidural space. The device is single use, sterile, hanually powered, and designed to connect to standard epidural Tuohy needles (16-18 gauge) with a standard Luer connector.

    EpiZact provides tactile feedback, in addition to visual feedback, when a loss of resistance is detected. The tactile feedback is also designed to help reduce the forward motion of the needle upon detection of loss of resistance. The device is comprised of copolyester plastic, stainless steel springs, silicone Orings, silicone and ABS valve, and polydimethylsiloxane (silicone) lubricant.

    AI/ML Overview

    The provided text describes the EpiZact device and its substantial equivalence to a predicate device, EpiFaith. However, it does not include detailed acceptance criteria or a study proving the device meets specific acceptance criteria in the format requested.

    The document lists performance tests conducted, but it doesn't specify the acceptance criteria for each test or the exact performance results of the EpiZact device against those criteria. It focuses on demonstrating that the EpiZact underwent similar testing as the predicate device and that the differences do not raise new safety or effectiveness concerns.

    Therefore, I cannot provide the specific table of acceptance criteria, reported device performance, sample sizes for test/training sets, ground truth establishment, or details about MRMC studies as requested, because this information is not present in the provided text.

    Based on the available text, here's what can be extracted and what is missing:

    Missing Information:

    • A table of acceptance criteria and the reported device performance.
    • Sample sizes used for the test set.
    • Data provenance (country of origin, retrospective/prospective).
    • Number of experts used to establish ground truth and their qualifications.
    • Adjudication method for the test set.
    • If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, the effect size.
    • If a standalone (algorithm only) performance study was done.
    • The type of ground truth used (expert consensus, pathology, outcomes data, etc.) for the performance benchtop testing.
    • The sample size for the training set.
    • How the ground truth for the training set was established.

    Information that can be inferred or directly stated from the text:

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

      • N/A. The document lists types of tests performed (e.g., ISO 7886-1 sections, ISO 80369-7 sections, USP ) but does not provide a table with specific acceptance criteria (e.g., "X% pass rate," "Y mm deviation maximum") or the quantitative results for the EpiZact against those criteria. It generally states that "Testing conducted under performance... demonstrate the subject device is substantially equivalent to the predicate device" and that the "conclusions drawn... demonstrate that the proposed device is as safe, as effective, and performs as well or better than the legally marketed predicate device."
    2. Sample sized used for the test set and the data provenance:

      • N/A. The document does not specify sample sizes for any of the performance tests. It also does not mention study data provenance like country of origin or whether data was retrospective or prospective.
    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

      • N/A. The document does not describe the establishment of ground truth by experts for the performance tests. The "Performance Benchtop Testing" and "Human Factors Studies" are mentioned, but no details on expert involvement for ground truthing are provided.
    4. Adjudication method for the test set:

      • N/A. No information on adjudication methods is provided.
    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:

      • N/A. This device (EpiZact) is a mechanical piston syringe, not an AI or imaging device with "readers" in the typical sense. Therefore, an MRMC study related to AI assistance for human readers is not applicable and not mentioned.
    6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:

      • N/A. This is a mechanical device. The concept of "algorithm only" or "standalone algorithm performance" does not apply. The device's function is to provide tactile and visual feedback to a human clinician during an epidural procedure.
    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

      • N/A. While "Performance Benchtop Testing" and "Human Factors Studies" were conducted, the specific type of ground truth (e.g., established by expert consensus on successful epidural placement in a simulated environment, or physical measurements against a standard) is not detailed. The tests listed (ISO standards, USP) typically have defined metrics and criteria that act as their own "ground truth" for compliance, rather than relying on external expert consensus in the way an imaging AI might.
    8. The sample size for the training set:

      • N/A. This device does not involve machine learning or AI, so there is no "training set" in that context.
    9. How the ground truth for the training set was established:

      • N/A. As there is no training set, this question is not applicable.
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