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510(k) Data Aggregation

    K Number
    K052197
    Manufacturer
    Date Cleared
    2006-06-09

    (301 days)

    Product Code
    Regulation Number
    862.3620
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    FIRST SIGN DRUG OF ABUSE URINE SCREENING TEST

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

    The First Sign Drug of Abuse Tests are rapid, chromatographic immunoassays for the qualitative detection of drugs-of-abuse in human urine. The tests may be run singly of in combinations of up to six drugs simultaneously. The cut-off concentrations and specific analytes tested for are listed below.

    This assay provides only a preliminary test result. A more specific alternate chemical method must be used to obtain a confirmed analytical result. Gas chromatography/mass spectrometry (GC/MS) is the preferred confirmatory method. Clinical and professional judgment must be applied to any drug of abuse test result, particularly when preliminary positive results are obtained.

    For Professional Use Only.

    Device Description

    The First Sign Drug of Abuse Tests are rapid, chromatographic immunoassays for the qualitative detection of drugs-of-abuse in human urine.

    AI/ML Overview

    Here's an analysis of the provided text, outlining the acceptance criteria and study details for the First Sign™ Drug of Abuse Urine Screening Tests:

    1. Table of Acceptance Criteria and Reported Device Performance

    The acceptance criteria for this type of device (qualitative drug screening) are typically based on strong agreement with a definitive, confirmatory method. While explicit percentage targets (e.g., "must achieve >95% positive agreement") aren't directly stated as "acceptance criteria" in the submission, the performance characteristics provided demonstrate successful achievement of high agreement rates relative to the gold standard.

    First Sign TestAcceptance Criteria (Implied by Predicate and Type of Test)Reported Device Performance (Positive Agreement)Reported Device Performance (Negative Agreement)Reported Device Performance (Overall Agreement)
    NortriptylineHigh agreement (e.g., >95%) with GC/MS or HPLC97.5%>99%98.7%
    SecobarbitalHigh agreement (e.g., >95%) with GC/MS or HPLC97.4%97.6%97.5%
    MDMAHigh agreement (e.g., >95%) with GC/MS or HPLC92.5%>99%96.2%
    OxazepamHigh agreement (e.g., >95%) with GC/MS or HPLC95.7%>99%97.5%
    MethadoneHigh agreement (e.g., >95%) with GC/MS or HPLC93.7%97.9%96.2%
    OxycodoneHigh agreement (e.g., >95%) with GC/MS or HPLC95%>99%97.5%

    Note on Acceptance Criteria: For a 510(k) submission, the "acceptance criteria" are implicitly met when the sponsor demonstrates substantial equivalence to predicate devices, which typically includes performance data that is comparable or superior to the predicate. High positive, negative, and overall agreement rates with a confirmatory method are standard for establishing performance for qualitative drug tests.

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

    • Sample Size: The document does not explicitly state the total sample size (number of urine samples/patients) used for the test set for each drug. It provides percentages (e.g., 97.5% positive agreement), which are derived from a sample, but the raw numbers of positive and negative samples are not given.
    • Data Provenance: The document does not specify the country of origin of the data. It mentions that WHPM, Inc. is in El Monte, CA, and WHPM, Bioresearch and Technology Co. Ltd. is in Beijing, China, and that product would be manufactured in both locations. However, where the clinical samples were collected is not stated. The study described is a "clinical evaluation," implying it was prospective or at least involved testing on clinical samples (as opposed to entirely in-silico or simulated data).

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

    • Number of Experts: Not applicable. The ground truth was established by laboratory methods (GC/MS or HPLC), not human experts interpreting the results of the rapid test.
    • Qualifications of Experts: Not applicable.

    4. Adjudication Method for the Test Set

    • Adjudication Method: Not applicable. The ground truth was established by Gas Chromatography/Mass Spectrometry (GC/MS) or High-Performance Liquid Chromatography (HPLC), which are objective analytical methods, not subjective interpretations requiring adjudication.

    5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

    • MRMC Study: No, an MRMC comparative effectiveness study was not done. This device is a rapid, chromatographic immunoassay, not an imaging or interpretive AI device where human readers are involved in the primary result generation. The test provides a direct positive/negative result, not an interpretation of complex data by a human.
    • Effect Size of Human Readers with/without AI: Not applicable, as there's no AI component or human interpretation in the workflow described.

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

    • Standalone Performance: Yes, the described performance results are for the device (the immunoassay) operating in a standalone capacity. The comparison is between the immunoassay result and the confirmatory laboratory method. There is no human interpretation or intervention in determining the First Sign test result itself (beyond reading the visible lines, which is standard for such tests).

    7. The Type of Ground Truth Used

    • Ground Truth Type: The ground truth was established using confirmatory analytical laboratory methods:
      • Gas Chromatography/Mass Spectrometry (GC/MS)
      • High-Performance Liquid Chromatography (HPLC)
        These are considered the gold standard for drug detection and quantification in urine.

    8. The Sample Size for the Training Set

    • Training Set Sample Size: The document does not mention a training set. This is typical for traditional immunoassay devices based on chemical reactions rather than machine learning algorithms. Immunoassays are "trained" during their development and optimization phases in the lab, but there isn't a distinct "training set" of patient data in the same way there would be for an AI algorithm.

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

    • Ground Truth for Training Set: Not applicable, as there is no specific "training set" of patient data in the context of an immunoassay. The inherent "ground truth" for developing such a test is the known presence/absence and concentration of analytes in controlled laboratory samples used during R&D.
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