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510(k) Data Aggregation

    K Number
    K101484
    Device Name
    COMPRESSYN BAND
    Manufacturer
    Date Cleared
    2011-04-01

    (308 days)

    Product Code
    Regulation Number
    888.3010
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K070169, K930015, K993286, K931271, K946173, K990088

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

    The Compressyn™ Band is intended for use in stabilization and fixation of anterior chest wall fractures including sternal fixation subsequent to sternotomy and sternal reconstructive procedures.

    Device Description

    The Compressyn™ Band System consists of a stainless steel coupler preloaded with a polyester fiber band. It is a cerclage fixation device that is placed around or through the sternum and locked in place to provide stabilized fixation.

    AI/ML Overview

    The acceptance criteria and study proving the device meets them are summarized below based on the provided document.

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Test Performed)Reported Device Performance
    Sternal compressionNot explicitly stated; device found substantially equivalent to predicates.
    Static performance to a 5mm gapNot explicitly stated; device found substantially equivalent to predicates.
    Static performance to point of failureNot explicitly stated; device found substantially equivalent to predicates.
    Cyclic performance to a 5mm gapNot explicitly stated; device found substantially equivalent to predicates.
    Cyclic performance to point of failureNot explicitly stated; device found substantially equivalent to predicates.
    Cyclic performance to 2000 cyclesNot explicitly stated; device found substantially equivalent to predicates.

    Note: The document states, "The testing demonstrated that the Compressyn™ Band is substantially equivalent to the predicate devices." Specific numerical acceptance criteria and the Compressyn™ Band's performance against them are not provided, only that the outcome met the substantial equivalence criteria.

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

    The document does not specify the sample size for the test set (number of devices tested) or the data provenance (country of origin, retrospective/prospective). It only mentions that "All necessary verification and validation testing has been performed."

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

    N/A. This is a medical device (cerclage bone fixation) testing, not an AI or diagnostic imaging study that would require expert-established ground truth. The testing involves mechanical and performance characteristics.

    4. Adjudication Method for the Test Set

    N/A. The testing described is performance-based (mechanical tests) typically without human adjudication in the context of diagnostic agreement.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

    N/A. This is a medical device (cerclage bone fixation) testing, not an AI or diagnostic imaging study that would involve human readers.

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

    N/A. This device is a physical medical implant, not an algorithm.

    7. The Type of Ground Truth Used

    For the performance testing, the "ground truth" or reference for comparison appears to be the performance characteristics of the predicate devices. The study aimed to demonstrate "substantial equivalence" of the Compressyn™ Band's mechanical and physical performance to these predicate devices. The specific physical properties or failure points of the predicate devices would form the benchmark for comparison.

    8. The Sample Size for the Training Set

    N/A. This is not an AI/machine learning study, so there is no concept of a "training set."

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

    N/A. As there is no training set for an AI model, this question is not applicable.

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