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510(k) Data Aggregation

    K Number
    K992002
    Date Cleared
    1999-08-05

    (51 days)

    Product Code
    Regulation Number
    862.1475
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    AUTOHDL CHOLESTEROL REAGENT SET

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

    This product is to be used in a diagnostic laboratory setting, by qualified laboratory technologists, for the quantitative determination of high density lipoprotein (HDL) cholesterol in serum or plasma. HDL cholesterol is recognized as a useful tool in identifying patients who are at a higher risk for coronary heart disease. Low HDL cholesterol levels are associated with an increased risk. This reagent set is intended for in vitro diagnostic use only.

    Device Description

    Not Found

    AI/ML Overview

    This document is a 510(k) clearance letter for the autoHDL™ Cholesterol Reagent Set, which is an in vitro diagnostic device. The letter establishes its substantial equivalence to a legally marketed predicate device. As such, it does not contain the specific technical acceptance criteria or the study details that would typically be found in a performance study report for a novel medical device.

    Therefore, many of the requested details about acceptance criteria and study design are not available in the provided text.

    Based on the provided text, here's what can be extracted and what information is missing:

    1. A table of acceptance criteria and the reported device performance:

      • Not Available. The letter grants clearance based on substantial equivalence, implying that the device performance and characteristics are comparable to a predicate device, but it does not detail specific acceptance criteria or performance metrics (like accuracy, precision, linearity, etc.) for the autoHDL™ Cholesterol Reagent Set.
    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

      • Not Available. The letter does not describe any specific test sets or studies performed with the autoHDL™ Cholesterol Reagent Set.
    3. 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 Applicable / Not Available. This device is an in vitro diagnostic reagent set. Ground truth for such devices is typically established through reference methods or certified control materials, not expert consensus on images. The document does not provide details on how the performance was verified.
    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

      • Not Applicable / Not Available. Adjudication methods like 2+1 or 3+1 are typically used in imaging studies where subjective interpretation is involved. This is not relevant for an in vitro diagnostic device.
    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 / Not Available. MRMC studies are not relevant for this type of in vitro diagnostic reagent. This device is not an AI-assisted diagnostic tool that would involve human reader improvement.
    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

      • Not Applicable. This is an in vitro diagnostic reagent, not an algorithm.
    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

      • Not Available from the document. For an in vitro diagnostic, ground truth for performance studies typically involves comparison to a recognized reference method or use of certified reference materials with known analyte concentrations. The letter does not specify this.
    8. The sample size for the training set:

      • Not Applicable. This is a chemical reagent set, not a machine learning algorithm that requires a "training set."
    9. How the ground truth for the training set was established:

      • Not Applicable. As above, no training set for a machine learning model is involved.
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