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510(k) Data Aggregation

    K Number
    K953854
    Date Cleared
    1996-03-11

    (217 days)

    Product Code
    Regulation Number
    876.5820
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    WATERCHECK REAGENT STRIP FOR CHLORINE & PEROXIDE

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

    WaterCleck™ CP Reagent Strip is intended to be used in detecting residual chlorine or peroxide in dialysis water. It also is used for detection of effective high level of chlorine in the disinfecting solution.

    Device Description

    WaterCleck™ CP Reagent Strip is a paper based dry chemistry reagent strip. It consists of a single reagent pad, 0.2x0.2 inch square, adhered to one end of a 0.2x2.5 inch plastic handle with a double sided adhesive. The chemistry of the regent strip allows the same single reagent pad to react with either chlorine or peroxide. It is self-contained and is ready to use as a dip-and-read reagent strip without other additional reagent.

    AI/ML Overview

    The provided text describes a medical device called "WaterCheck™ CP Reagent Strip for Chlorine and Peroxide." It focuses on its intended use, technological comparison to existing products, and a statement of substantial equivalence. However, the document does not contain the detailed acceptance criteria and study information typically found in a comprehensive medical device submission that would allow for a complete answer to your request.

    Specifically, the document lacks:

    • A table of acceptance criteria with reported device performance values.
    • Details on sample sizes for test sets, data provenance, or information about training sets.
    • Information on experts used to establish ground truth, adjudication methods, or MRMC studies.
    • Data regarding standalone algorithm performance or the type of ground truth used in a formal study.

    Therefore, I cannot fully answer your request with the provided information.

    However, based on the limited information about performance claims, I can infer what might be considered acceptance criteria based on its comparison to predicate devices, but this is a reconstruction and not explicitly stated in the document.

    Inferred Potential Acceptance Criteria and Reported Device Performance (Based on Substantial Equivalence Claims):

    ParameterAcceptance Criteria (Inferred from Predicate Equivalence)Reported Device Performance (from text)
    Residual Chlorine DetectionDetect 0.5 ppm or less of chlorineDetect 0.5 ppm or less of chlorine. Can extend detection range to 1000 ppm or more (for high-level disinfection monitoring).
    Hydrogen Peroxide DetectionDetect 1.0 ppm of hydrogen peroxideDetect 1.0 ppm of hydrogen peroxide. More sensitive to 0.5 ppm or less of hydrogen peroxide (compared to Renalin Strip).
    Hydrogen Peroxide StabilityStable at room temperature (compared to predicate)Stable at room temperature.

    Missing Information (Cannot be extracted from the provided text):

    1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not available.
    2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience): Not available.
    3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not available.
    4. 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 is not an AI device or an imaging study requiring human reader performance).
    5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable (this is a chemical reagent strip, not an algorithm).
    6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not explicitly stated, but for chemical detection, the "ground truth" would typically be established by laboratory methods using known concentrations of chlorine or peroxide.
    7. The sample size for the training set: Not applicable (this is a chemical reagent strip, not a machine learning model requiring a training set).
    8. How the ground truth for the training set was established: Not applicable.
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