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510(k) Data Aggregation

    K Number
    K954687
    Date Cleared
    1996-04-09

    (181 days)

    Product Code
    Regulation Number
    866.3510
    Reference & Predicate Devices
    N/A
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The ACCESS® Rubella IgG assay aids in the diagnosis of Rubella infection and the determination of immunity.

    Device Description

    The ACCESS® Rubella IgG assay is a paramagnetic-particle, chemiluminescent immunoassay for the qualitative and quantitative determination of IgG antibodies to the Rubella virus in human serum, using the ACCESS® Immunoassay System.

    AI/ML Overview

    Here’s an analysis of the provided information, structured to address your request:

    Acceptance Criteria and Device Performance for ACCESS® Rubella IgG Reagents

    Acceptance CriteriaReported Device Performance
    Relative Sensitivity (compared to HAI)98%
    Relative Specificity (compared to HAI)99%
    Concordance with predicate (Abbott IMx)90%
    Within Site Precision (QC1)15%
    Within Site Precision (QC2)6%
    Within Site Precision (High Positive)9%
    Total Precision (QC1)15%
    Total Precision (QC2)6%
    Total Precision (High Positive)11%

    Study Details:

    1. Sample sizes used for the test set and the data provenance:

      • Comparison to HAI: 784 patient serum samples.
      • Comparison to Abbott IMx Rubella IgG: 670 patient serum samples.
      • Precision Studies: Not explicitly stated, but includes QC1, QC2, and a high positive sample (implying multiple measurements on these controls).
      • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). The document refers to "clinical studies," which often implies prospective collection, but this is not confirmed.
    2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

      • Not stated. The ground truth for the primary effectiveness claims (sensitivity and specificity) was established by HAI (Hemagglutination Inhibition Assay), which is described as a "standard laboratory reference method." This is a laboratory test, not an expert human consensus.
    3. Adjudication method for the test set:

      • Not applicable as the ground truth was established by a laboratory reference method (HAI), not human adjudication.
    4. If a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done:

      • No, an MRMC study was not done. This device is an automated immunoassay for antibody detection, not an imaging or diagnostic device requiring human interpretation alongside AI.
    5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

      • Yes, this study represents a standalone evaluation of the ACCESS® Rubella IgG assay. It is an automated immunoassay system, and its performance is assessed independently against reference methods and a predicate device. Human interpretation is not part of its operational mechanism.
    6. The type of ground truth used:

      • Expert Consensus: No.
      • Pathology: No.
      • Outcomes Data: No.
      • Reference Method/Laboratory Standard: The primary ground truth for sensitivity and specificity was HAI (Hemagglutination Inhibition Assay), described as a "standard laboratory reference method." For concordance, the ground truth was the performance of the Abbott IMx Rubella IgG, which is another commercially available, validated assay.
    7. The sample size for the training set:

      • Not applicable as this is an assay kit. Immunoassays typically do not have "training sets" in the same way machine learning models do. The assay is developed and optimized, but the concept of a training set for an algorithm is not relevant here.
    8. How the ground truth for the training set was established:

      • Not applicable, as there is no "training set" for this type of device. The assay development would involve extensive R&D, reagent optimization, and analytical validation using known positive and negative controls, but this is distinct from establishing ground truth for a machine learning training set.
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