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510(k) Data Aggregation

    K Number
    K030212
    Manufacturer
    Date Cleared
    2003-03-27

    (65 days)

    Product Code
    Regulation Number
    878.4840
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    MONODEK SYNTHETIC ABSORBABLE SURGICAL SUTURE

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

    Monodek™ Synthetic Absorbable Surgical Sutures are indicated for use in all types of soft tissue approximation, including use in pediatric cardiovascular tissue where growth is expected to occur and opthalmic surgery. Monodek suture is not indicated in adult cardiovascular tissue, microsurgery and neural tissue. These sutures are particularly useful where the combination of an absorbable suture and extended wound support (up to six weeks) is desirable.

    Device Description

    Monodek Absorbable Surgical Suture meets all USP requirements except for oversized diameter. Monodek is available in sizes 6-0 through 0 (metric sizes 0.7 through 3.5), undyed and dyed (violet). The suture is a sterile, monofilament and is provided in a variety of lengths, with or without needles and may be supplied in a variety of cut lengths or on ligating reels.

    AI/ML Overview

    The provided text describes a 510(k) summary for the Monodek™ Synthetic Absorbable Surgical Suture, which focuses on demonstrating substantial equivalence to existing predicate devices rather than proving performance against specific acceptance criteria through novel studies.

    Therefore, the document does not contain information about:

    • A table of acceptance criteria and reported device performance (beyond meeting USP requirements for sutures except for oversized diameter)
    • Sample sizes for a test set or data provenance for such a test
    • Number and qualifications of experts for ground truth establishment
    • Adjudication method
    • Multi-reader multi-case (MRMC) comparative effectiveness study
    • Standalone algorithm performance
    • Type of ground truth for a test set
    • Sample size for a training set
    • How ground truth for a training set was established

    The information provided focuses on regulatory equivalence:

    Acceptance Criteria for Substantial Equivalence:

    The primary "acceptance criteria" for the device, as presented, are based on demonstrating substantial equivalence to predicate devices and conformance with established standards and guidance documents. These implicitly act as the performance benchmarks.

    Acceptance Criteria (Demonstrated Conformance/Equivalence)Reported Device Performance
    Similar in intended use to cleared predicate devicesIndicated for soft tissue approximation, pediatric cardiovascular tissue, and ophthalmic surgery, similar to CP Medical Mono-Dox and Ethicon PDS II.
    Similar in materials to cleared predicate devicesSynthetic absorbable suture, similar to CP Medical Mono-Dox and Ethicon PDS II.
    Similar in design to cleared predicate devicesMonofilament, available in sizes 6-0 through 0, undyed and dyed, similar to predicate devices.
    Similar in performance characteristics to cleared predicate devicesMeets all USP requirements for absorbable surgical sutures, except for oversized diameter. (Implicitly, the performance is considered equivalent due to meeting standards and similarity to predicate devices).
    Conformance with ANSI/AAMI/ISO 10993-1 Biological Evaluation of Medical DevicesConformance stated as a basis for substantial equivalence.
    Conformance with USP Section XXV - Absorbable Surgical SuturesThe device meets all USP requirements except for oversized diameter. This is explicitly stated.
    Conformance with Guidance Document "Guidance for Surgical Suture 510(k)s" (August 10, 2000)Conformance stated as a basis for substantial equivalence.
    Conformance with FDA "Class II Special Controls Guidance Document: Surgical Sutures; Guidance for Industry and FDA" (December 19, 2002)Conformance stated as a basis for substantial equivalence.

    Study Proving Acceptance Criteria:

    The "study" used to demonstrate that the device meets these acceptance criteria is a regulatory submission based on comparison to predicate devices and adherence to established standards.

    • Study Type: This is not a clinical trial or a performance study in the typical sense of a novel device proving its efficacy or accuracy against specific clinical endpoints. Instead, it is a 510(k) premarket notification demonstrating substantial equivalence to legally marketed predicate devices.
    • Methodology: The determination of substantial equivalence was based on:
      • A detailed device description.
      • Performance testing (implied by meeting USP requirements, though specific test results or protocols are not detailed in this summary).
      • Conformance with voluntary performance standards (ANSI/AAMI/ISO 10993-1, USP Section XXV).
      • Adherence to FDA guidance documents ("Guidance for Surgical Suture 510(k)s" and "Class II Special Controls Guidance Document: Surgical Sutures").

    Missing Information (as per your requested categories):

    The document does not describe any specific studies that would involve:

    • Sample sizes for a test set (as there wasn't a separate "test set" in the context of an AI device)
    • Data provenance
    • Number of experts or their qualifications for ground truth in a performance study
    • Adjudication method
    • MRMC comparative effectiveness study or effect size (as this is a physical medical device, not an AI diagnostic tool)
    • Standalone performance (not applicable for this type of device)
    • Type of ground truth (e.g., pathology, outcomes data) for a diagnostic performance study
    • Sample size for a training set
    • How ground truth for a training set was established

    In summary, for this specific medical device (a surgical suture), the "acceptance criteria" are met by demonstrating broad equivalence to existing, approved products and adherence to industry standards and regulations, rather than through a performance study that measures accuracy or efficacy against a defined ground truth in a test and training set, which would be typical for AI/diagnostic devices.

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