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510(k) Data Aggregation

    K Number
    K020465
    Manufacturer
    Date Cleared
    2002-05-22

    (99 days)

    Product Code
    Regulation Number
    876.5540
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    MEDCOMP ASH SPLIT-CATH II

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

    THE MEDCOMP ASH SPLIT-CATH II IS INDICATED FOR USE IN ATTAINING LONG-TERM VASCULAR ACCESS FOR HEMODIALYSIS AND APHERESIS. IT MAY BE INSERTED PERCUTANEOUSLY AND IS PRIMARILY PLACED IN THE INTERNAL JUGULAR VEIN. ALTERNATE INSERTION SITES INCLUDE THE SUBCLAVIAN VEIN AS REQUIRED. CATHETERS GREATER THAN 40cm ARE INTENDED FOR FEMORAL VEIN INSERTION.

    Device Description

    The Medcomp Ash Split-Cath II is a polyurethane, double lumen catheter used to remove and return blood through two-segregated lumen passages. Both lumens are "D" shaped, tapered at the distal tip, with eight side holes. The distal venous lumen extends beyond the arterial lumen to reduce recirculation. The fixed polyester cuff allows for tissue ingrowth for long term placement. The arterial and venous lumens are designed to be split, or peeled apart, prior to insertion to provide two free-floating lumens within the vessel. The side holes are orientated to allow 360-degree arterial uptake and venous return. The lumens are connected to the extensions via a soft pliable hub with suture wing. Red and blue luer connectors and clamps identify the arterial and venous extensions. Priming volume information is printed an identification ring housed within the extension line clamp.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for the Medcomp Ash Split-Cath II Hemodialysis Catheter. This is a medical device, not an AI/ML-driven solution or a diagnostic tool that would typically involve acceptance criteria based on metrics like sensitivity, specificity, or reader performance. Therefore, many of the requested points are not applicable.

    The "acceptance criteria" for this type of medical device submission are typically met by demonstrating substantial equivalence to a previously legally marketed predicate device. This is achieved through performance data showing that the new device is as safe and effective as the predicate.

    Here's an analysis based on the information provided:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Demonstration of Substantial Equivalence)Reported Device Performance
    Material Formulation & CompatibilityThe new lumen materials meet the requirements of ISO 10993 for a permanent contact device through Biocompatibility testing. Performance is "substantially equivalent" to predicate.
    Tensile StrengthIn Vitro performance data demonstrates substantial equivalence to the legally marketed Ash Split-Cath I catheter.
    Joint StrengthIn Vitro performance data demonstrates substantial equivalence to the legally marketed Ash Split-Cath I catheter.
    LeakageIn Vitro performance data demonstrates substantial equivalence to the legally marketed Ash Split-Cath I catheter.
    RecirculationIn Vitro performance data demonstrates substantial equivalence to the legally marketed Ash Split-Cath I catheter.
    Flow PerformanceIn Vitro performance data demonstrates substantial equivalence to the legally marketed Ash Split-Cath I catheter.
    Flexural PropertiesIn Vitro performance data demonstrates substantial equivalence to the legally marketed Ash Split-Cath I catheter.
    Lumen PeelIn Vitro performance data demonstrates substantial equivalence to the legally marketed Ash Split-Cath I catheter.
    Intended UseExpanded to include femoral vein insertion (previously internal jugular and subclavian). Still deemed substantially equivalent to predicate device's intended use.
    Design SpecificationsIdentical to the predicate device.
    Insertion Method, Anatomical Location, Labeling, Manufacturing Process, SterilizationSubstantially equivalent to the predicate device.

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

    This is not applicable in the context of this 510(k) submission. The "test set" primarily refers to in vitro performance data of the device itself, comparing its physical and functional properties to the predicate. There is no mention of a "test set" in the sense of clinical data or patient data. The provenance of the in vitro test data is not specified (e.g., country of origin), but it would have been generated in a lab setting. It is not retrospective or prospective patient data.

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

    Not applicable. Ground truth for a physical device's performance (e.g., tensile strength, flow rate) is established through standardized engineering and laboratory testing protocols, not human expert consensus.

    4. Adjudication Method for the Test Set

    Not applicable. Adjudication methods (like 2+1, 3+1) are used for clinical assessments or image interpretations involving human readers. The performance data here is based on objective laboratory measurements.

    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 device is a catheter, not an AI-assisted diagnostic tool.

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

    Not applicable. This is not an algorithm.

    7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

    The "ground truth" for this device's performance relies on objective physical and mechanical testing standards (e.g., tensile strength, flow rate measurements as per ISO 10993 for biocompatibility) compared to the performance of the predicate device.

    8. The Sample Size for the Training Set

    Not applicable. This is not an AI/ML model that requires training data.

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

    Not applicable. This is not an AI/ML model.

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