Search Filters

Search Results

Found 1 results

510(k) Data Aggregation

    K Number
    K221128
    Manufacturer
    Date Cleared
    2022-10-05

    (170 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K130217, K201522

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

    The Arthrex ACL TightRope®, PCL TightRope®, and TightRope® II devices are intended to be used for fixation of bone to bone or soft tissue to bone, and are intended as fixation bridge, or for distributing suture tension over areas of ligament or tendon repair. Specifically, Arthrex will be offering these devices for ACL/PCL repair and reconstruction for the adult and pediatric patient population.

    Device Description

    The Arthrex ACL TightRope®, PCL TightRope®, and TightRope® II devices are comprised of a suture loop that may include passing sutures and/or metallic button. The suture loop and passing sutures are braided nonabsorbable surgical sutures. The button is made of titanium with holes to permit suture passage and assembly with Arthrex sutures.

    The proposed devices are available in various device models referred to as TightRope® ABS, Implant; TightRope®, PCL; ACL TightRope® RT; ACL TightRope® II RT; ACL TightRope® II RT, Double Loaded Passing Sutures; BTB TightRope® II; BTB TightRope® II, Double Loaded Passing Sutures; and TightRope® II ABS, Implant Open.

    AI/ML Overview

    This document describes the 510(k) premarket notification for Arthrex ACL TightRope®, PCL TightRope®, and TightRope® II devices, seeking to expand their indications to include the pediatric patient population. The submission focuses on demonstrating substantial equivalence to previously cleared predicate devices for this new patient group.

    1. Table of Acceptance Criteria and Reported Device Performance

    Test TypeAcceptance CriteriaReported Device Performance
    Ultimate Load TestingProposed devices must be equivalent to the predicate device.The test results demonstrate that the proposed and predicate devices are equivalent.
    Cyclic DisplacementProposed devices must be equivalent to the predicate device.The test results demonstrate that the proposed and predicate devices are equivalent.
    Bacterial Endotoxin (Pyrogen)Device must meet pyrogen limit specifications as per EP 2.6.14/USP .Pyrogen testing was conducted, demonstrating that the device meets pyrogen limit specifications.
    Clinical Effectiveness (Pediatric)Device must be effective when used in the proposed pediatric patient population.Clinical literature reviewed shows the device is effective when used in the pediatric patient population.
    Real-World Outcomes (Pediatric)No statistical differences in outcomes between patients less than 22 years of age and those greater than 22 years of age.Real-world data from the Surgical Outcomes System registry shows no statistical differences in patient outcomes between those less than 22 years of age versus those greater than 22 years of age.

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

    The document does not specify a distinct "test set" in the context of an AI/ML device where performance metrics like sensitivity, specificity, etc., are usually evaluated on a separate test set. Instead, the provided information relates to:

    • Mechanical Testing (Ultimate Load & Cyclic Displacement): The sample sizes for these tests are not explicitly stated in the summary but generally involve a specific number of devices/constructs for each test condition.
    • Clinical Literature Review: This involves reviewing existing clinical studies, so the "sample size" is the cumulative number of patients across all included studies. The provenance is "clinical literature" implying published research.
    • Real-World Data/Evidence: Derived from the "Surgical Outcomes System registry." The country of origin is not specified, but registries often collect data from multiple institutions, potentially spanning different regions. This is retrospective data.

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

    This information is not applicable. The submission is for a medical device (fixation appliances) and not an AI/ML diagnostic or predictive algorithm that relies on expert-established ground truth for its performance evaluation for this specific 510(k) submission. The "ground truth" for mechanical tests is defined by engineering specifications and physical measurements. For clinical effectiveness, it's defined by patient outcomes in surgical settings described in clinical literature and registries.

    4. Adjudication Method for the Test Set

    This information is not applicable. As stated above, this is a mechanical device, not an AI/ML system requiring human adjudication for ground truth establishment.

    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

    This information is not applicable. This 510(k) submission is for a mechanical surgical fixation device, not an AI-assisted diagnostic or imaging device that would typically involve MRMC studies.

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

    This information is not applicable. This is not an AI/ML algorithm.

    7. The Type of Ground Truth Used

    • Mechanical Testing (Ultimate Load & Cyclic Displacement): The ground truth is based on the physical properties and performance characteristics measured according to established engineering and biomechanical testing standards.
    • Clinical Literature: The ground truth is clinical outcomes and effectiveness observed in human patients as reported in peer-reviewed medical literature.
    • Real-World Data/Evidence: The ground truth is patient outcomes recorded in a surgical registry.

    8. The Sample Size for the Training Set

    This information is not applicable. This is not an AI/ML algorithm that employs a "training set."

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

    This information is not applicable. This is not an AI/ML algorithm that employs a "training set."

    Ask a Question

    Ask a specific question about this device

    Page 1 of 1