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510(k) Data Aggregation

    K Number
    K042723
    Date Cleared
    2004-12-07

    (67 days)

    Product Code
    Regulation Number
    864.7320
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Triage® Profiler S.O.B. (Shortness of Breath) Panel is a fluorescence immunoassay to be used with the Triage® Meter Plus for the quantitative determination of creatine kinase MB, myoglobin, troponin I, B-type natriuretic pentide, and cross-linked fibrin degradation products containing D-dimer in EDTA whole blood and plasma specimens. The test is used as an aid in the diagnosis of myocardial infarction (injury), an aid in the diagnosis and assessment of severity of heart failure, an aid in the assessment and evaluation of patients suspected of having disseminated intravascular coagulation or thromboembolic events including pulmonary embolism, and an aid in the risk stratification of patients with acute coronary syndromes.

    Device Description

    The Triage® Profiler S.O.B. (Shortness of Breath) Panel is a fluorescence immunoassay to be used with the Triage® Meter Plus for the quantitative determination of creatine kinase MB, myoglobin, troponin 1, B-type natriuretic peptide, and cross-linked fibrin dearadation products containing D-dimer in EDTA whole blood and plasma specimens.

    AI/ML Overview

    This 510(k) summary explicitly states that no new study was performed for this submission (K042723).

    The key statement is in section E: "There have been no changes to the test performance, design, reagents and manufacture. These are identical to 510(k) document number K040437. The indications for use have been revised for clarity."

    Therefore, the acceptance criteria and study details would be found in the original submission K040437, not in this document. Since this document refers to K040437 for identical test performance, design, reagents, and manufacture, it implies that the device met the acceptance criteria defined in K040437.

    Without access to the K040437 document, I cannot provide the specific details of the acceptance criteria and the study that proved the device met them.

    Based solely on the provided text, the following information can be extracted:

    1. A table of acceptance criteria and the reported device performance: Not provided in this document. The document states that the performance is "identical to 510(k) document number K040437."

    2. Sample size used for the test set and the data provenance: Not provided in this document.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided in this document.

    4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not provided in this document.

    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 fluorescence immunoassay for quantitative determination of biomarkers, not an AI-assisted diagnostic imaging device for human readers.

    6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable in the context of an "algorithm only" as understood for AI devices. It is a standalone diagnostic test in the sense that it provides quantitative results of biomarkers. This document confirms the test performance is identical to the prior submission, implying standalone performance was evaluated earlier.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not provided in this document. For an immunoassay, the "ground truth" would typically refer to a reference method or clinical diagnosis based on established guidelines/outcomes at the time of the original submission (K040437).

    8. The sample size for the training set: Not provided in this document. Immunoassays typically don't have "training sets" in the same way machine learning models do. Performance is validated through analytical and clinical studies.

    9. How the ground truth for the training set was established: Not applicable for "training set" in this context.

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