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510(k) Data Aggregation

    K Number
    K193493
    Date Cleared
    2020-01-15

    (29 days)

    Product Code
    Regulation Number
    866.5510
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    ADVIA Centaur Total IgE (tIgE)

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

    For in vitro diagnostic use in the quantitative determination of total IgE in serum and plasma (EDTA and lithium heparin) using the ADVIA Centaur®, ADVIA Centaur XP, and ADVIA Centaur XPT systems.

    Device Description

    The ADVIA Centaur Total IgE (tlgE) assay is a two-site sandwich immunoassay using direct chemiluminometric technology, which uses constant amounts of two antibodies to IqE. Results are determined using a calibration curve that is generated specifically on each instrument by a 2-point calibration and a master curve with the reagent bar code. The ADVIA Centaur Total IgE (tlgE) assay is intended for use on the ADVIA Centaur family of analyzers. The ADVIA Centaur Calibrator 80 is a set of 2 level calibrators for the assay. Siemens Healthcare Diagnostics recommends the use of commercially available quality control materials with at least 2 levels (low and high).

    AI/ML Overview

    Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) summary:

    Device: ADVIA Centaur Total IgE (tIgE) Assay

    Purpose of Submission: Addition of plasma (EDTA and lithium heparin) sample claim and updating the detection capability claim.


    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria CategorySpecific Acceptance CriteriaReported Device Performance
    Detection CapabilityLoB: 1.5 IU/mLLoB: 1.5 IU/mL
    LoD: 2.0 IU/mLLoD: 2.0 IU/mL
    LoQ: 2.5 IU/mLLoQ: 2.5 IU/mL
    Specimen EquivalenceThe assay is designed to have a slope of 0.90–1.10 for alternate tube types versus serum.Dipotassium EDTA Plasma vs. Serum: 0.99 (95% CI: 0.975 - 1.012)
    Lithium Heparin vs. Serum: 1.00 (95% CI: 0.989 - 1.020)
    Interference(Implicit, likely a pre-defined acceptable bias percentage for reported interferents)Dipotassium EDTA (9.0 mg/mL): Bias -1.7% (at 121.51 IU/mL), Bias 1.4% (at 1624.13 IU/mL)
    Heparin (75 U/mL): Bias -1.7% (at 167.48 IU/mL), Bias -1.1% (at 1450.12 IU/mL)

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

    • Specimen Equivalence by Method Comparison:

      • Sample Size: N = 73 for both Dipotassium EDTA Plasma vs. Serum and Lithium Heparin vs. Serum.
      • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). However, given it's for an in vitro diagnostic device and involves method comparison, it typically involves collected patient samples.
    • Detection Capability (LoB, LoD, LoQ) and Interferences:

      • Sample Size: Not explicitly stated how many individual samples were used to determine LoB, LoD, LoQ, or for interference testing. These typically involve a series of measurements on spiked or known concentration samples rather than a large set of patient samples like method comparison.
      • Data Provenance: Not explicitly stated.

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

    This submission document primarily focuses on analytical performance of an in vitro diagnostic (IVD) assay and does not involve human expert interpretation of results to establish ground truth in the way a medical imaging AI would. Therefore, this section is not applicable in the traditional sense for this type of device. The "ground truth" for this device's performance is established by the known concentrations of analytes in controls, calibrators, and spiked samples, and comparison to a reference method (in the case of method comparison studies).


    4. Adjudication Method for the Test Set

    Not applicable. As noted above, this is an analytical performance study for an IVD assay, not a study involving human reader interpretation requiring adjudication.


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

    No. A MRMC study is typically performed for AI-assisted diagnostic imaging devices to evaluate the impact of AI on human reader performance. This submission is for an in vitro diagnostic assay, which does not involve human interpretation of images in this context.


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

    This device is a standalone device in terms of its operation. It's an automated immunoassay system (ADVIA Centaur family of analyzers) that quantitatively determines total IgE. Its performance characteristics (detection capability, specimen equivalence, interference) are evaluated as the algorithm/system running independently on samples. There isn't a human-in-the-loop interacting with the direct measurement as there would be with an AI assisting image interpretation.


    7. The Type of Ground Truth Used

    • Detection Capability (LoB, LoD, LoQ): Established using statistical methods defined by CLSI EP17-A2, relying on repeated measurements of blank samples and samples with known low concentrations of the analyte.
    • Specimen Equivalence by Method Comparison: The "ground truth" for the comparison (serum) is considered the established method against which the alternative sample types (EDTA plasma, lithium heparin plasma) are being evaluated. This relies on the accuracy of the serum measurement itself.
    • Interferences: Established by spiking known interfering substances at specified concentrations into samples with known IgE concentrations and measuring the resulting bias.

    8. The Sample Size for the Training Set

    Not applicable. This is an analytical performance study for a chemical assay. There is no "training set" in the machine learning sense for this device. The assay's parameters and calibration are established through laboratory procedures, calibrators, and master curves, not through a data-driven training process in the way an AI algorithm is trained.


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

    Not applicable. As there is no "training set" in the AI sense for this device, there is no ground truth established for it. The assay relies on known chemical reactions, calibrated reagents, and master curves set during the assay's development and manufacturing.

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