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510(k) Data Aggregation

    K Number
    K023256
    Date Cleared
    2003-04-23

    (205 days)

    Product Code
    Regulation Number
    862.1345
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Precision Xtra / MediSense Optium / Precision Easy / MediSense Optium Easy Blood Glucose Test Strip is intended for outside-of-the-body (in-vitro diagnostic) use. The system is indicated for the quantitative measurement of glucose in fresh whole blood for self-testing by lay users (e.g., from the finger, forearm, upper arm or base of thumb), or by health care professionals. The test strip is to be used for monitoring blood glucose concentrations in persons with diabetes and other conditions.

    Device Description

    The test strip is for blood glucose testing utilizes amperometric biosensor technology to generate a current. The size of the current is proportional to the amount of glucose present in the sample, providing a quantitative measure of glucose in whole blood and control solutions.

    AI/ML Overview

    Acceptance Criteria and Study for Precision Xtra/MediSense Optium Blood Glucose Test Strip

    The provided document describes the 510(k) premarket notification for the Precision Xtra/MediSense Optium Blood Glucose Test Strip. The acceptance criteria for this device are implicitly tied to demonstrating "substantial equivalence" to a predicate device, the Precision Xtra® Blood Glucose Testing System (K010553). The performance studies conducted aimed to show that the new test strip yields blood glucose results "substantially equivalent" to current methods, including the predicate device, for both laboratory and clinical settings, and that trained operators and lay users obtain equivalent results.

    While specific numerical acceptance criteria are not explicitly stated in a table within the provided text, the core acceptance criterion is that the device's performance is acceptable and comparable to the predicate device and current methods for blood glucose measurement. This includes achieving substantially equivalent results when testing blood glucose concentrations during a steady state, and also when comparing fingertip results to alternative sites (forearm, upper arm, or base of thumb).

    1. Table of Acceptance Criteria and Reported Device Performance

    As specific numerical acceptance criteria were not explicitly quantified in the provided text, the table below reflects the qualitative acceptance criteria implied by the substantial equivalence claim and the reported performance.

    Acceptance Criteria (Implied)Reported Device Performance
    Substantially equivalent to predicate device (K010553)Performance is acceptable and comparable to the predicate device.
    Substantially equivalent to current methods for blood glucose measurementsLay users can obtain blood glucose results substantially equivalent to current methods.
    Equivalent results between trained operators and lay usersClinical performance testing demonstrates trained operators and lay users obtain equivalent whole blood glucose results.
    Equivalent results when testing at alternative sites (forearm, upper arm, base of thumb) compared to fingertipResults of laboratory and clinical testing demonstrated acceptable performance, including alternative sites.

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

    • Sample Size: The document does not explicitly state the specific sample size used for the test set in either the laboratory or clinical studies. It mentions that the performance was studied "in the laboratory and in clinical settings."
    • Data Provenance: The document does not specify the country of origin of the data. The studies were conducted in "laboratory and clinical settings" and involved "healthcare professionals and lay users," suggesting real-world testing. The study is prospective in nature, as it involves testing the new device to demonstrate its performance.

    3. Number of Experts and Qualifications for Ground Truth

    • Number of Experts: Not specified.
    • Qualifications of Experts: The clinical studies involved "healthcare professionals," but their specific qualifications (e.g., years of experience, specialty) are not detailed.

    4. Adjudication Method for the Test Set

    The document does not describe any specific adjudication method (such as 2+1, 3+1, or none) for establishing ground truth for the test set. Given the nature of blood glucose measurement, the "ground truth" would typically be established by a reference laboratory method or the predicate device itself, rather than expert consensus on interpretation.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    No mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance is present. This is not applicable as the device is a blood glucose test strip, not an AI-powered diagnostic imaging tool.

    6. Standalone (Algorithm Only) Performance Study

    The device itself is a test strip, not an algorithm in the traditional sense of AI. Therefore, a "standalone algorithm only" performance study is not applicable. The performance is inherently tied to the chemical reactions on the strip and the accompanying meter. The studies evaluated the performance of the "test strip, when used according to the intended use stated above," which implies the entire system (strip + meter).

    7. Type of Ground Truth Used

    The type of ground truth used is implied to be reference laboratory methods for blood glucose measurement and comparison to the predicate device (Precision Xtra® Blood Glucose Testing System, K010553). The goal was to show "substantial equivalence" to these established methods and devices.

    8. Sample Size for the Training Set

    The document does not mention a "training set" in the context of machine learning, as this device is a chemical biosensor, not an AI algorithm requiring a training phase.

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

    Not applicable, as there is no "training set" in the context of an AI algorithm.

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