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510(k) Data Aggregation

    K Number
    K232075
    Date Cleared
    2024-02-23

    (226 days)

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

    The StatStrip Glucose Hospital Meter System is intended for point-of-care, in vitro diagnostic, multiple-patient use for the quantitative determination of glucose in capillary finger stick, venous whole blood, arterial whole blood, neonate arterial whole blood, and neonatal heel stick specimens throughout all hospital and all professional healthcare settings, including patients receiving intensive medical intervention/therapy.

    The system should only be used with single-use, auto-disabling lancing devices when performing a capillary finger stick or neonatal heel stick.

    It is not intended for use with neonate cord blood specimens.

    It is not intended for the screening or diabetes mellitus but is indicated for use in determining dysglycemia.

    The StatStrip Glucose Hospital Meter System includes the following components:

    • StatStrip Glucose Hospital Meter
    • StatStrip Glucose Test Strips
    Device Description

    StatStrip Glucose Hospital Meter System:
    The StatStrip Glucose Hospital Meter System is a hand-held testing device that works in conjunction with the StatStrip Glucose Test Strips to measure glucose in a whole blood sample. Meter operation is selfprompting using an illuminated color touch-screen Graphical User Interface (GUI).

    StatStrip Glucose Test Strips:
    The test strips contain a reaction layer that contains a glucose-enzyme (greater than 1.0 IU) and ferricyanide as a mediator. The test strip is touched to a drop of blood to initiate the test process. The strip is designed such that when a drop of blood is touched to the strip, the blood is drawn into the reaction space via capillary action. A simple one-step provides a blood glucose result. Test strips will be sold in vials of 25 strips.

    StatStrip Glucose Control Solutions:
    The control solutions are aqueous assayed solutions containing buffered D-Glucose, viscosity-adjusting agent, preservatives and other non-reactive ingredients (dye). They contain no products of human origin. There are three levels of controls, (Level 1, Level 2 and Level 3). These solutions will be offered for sale separately from the meter.

    StatStrip Glucose Linearity Solutions:
    There are 5 levels of Linearity solutions containing buffered D-Glucose, viscosity-adjusting agent, preservatives and other non-reactive ingredients (dve). They contain no products of human origin. These solutions are offered separately from the system for users to verify the performance of the system.

    Charging (Docking) Station:
    The meter charging station is a stationary accessory used to recharge the meter. The charging station has one slot for the meter to be placed and charged wirelessly. The charging station should be located central to the patient care area being served by the meter (e.g., a nursing station). The data charging station must remain plugged in to a wall outlet for power.

    The system still allows the charging station to be used to transfer data from the meter to a central workstation and allow meter setup information to be downloaded from the central workstation to the meter.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study details for the StatStrip Glucose Hospital Meter System (K232075), based on the provided text:


    1. Table of Acceptance Criteria and Reported Device Performance

    The acceptance criteria are derived from the FDA Guidance for POC Device Acceptance Criteria (Blood Glucose Monitoring Test Systems for Prescription Point-of-Care Use, September 29, 2020). The performance of both the proposed and predicate devices is reported against these criteria.

    Acceptance Criteria (FDA Guidance)Proposed Device Performance (< 75 mg/dL)Predicate Device Performance (< 75 mg/dL)Proposed Device Performance (≥ 75 mg/dL)Predicate Device Performance (≥ 75 mg/dL)
    Within ± 5 mg/dL33/36 (91.7%)32/36 (88.9%)--
    Within ± 10 mg/dL36/36 (100%)36/36 (100%)--
    Within ± 12 mg/dL36/36 (100%)36/36 (100%)--
    Within ± 15 mg/dL36/36 (100%)36/36 (100%)--
    Within ± 5 %--205/264 (77.3%)211/264 (80.7%)
    Within ± 10 %--263/264 (99.6%)263/264 (99.6%)
    Within ± 12 %--264/264 (100%)264/264 (100%)
    Within ± 15 %--264/264 (100%)264/264 (100%)

    Linear Regression Values:

    ConditionSlopeInterceptCorrelation Coefficient (r)
    Proposed Device vs. YSI0.97995.23570.9977
    Predicate Device vs. YSI0.98485.21640.9984
    Proposed Device vs. Predicate Device0.99350.35710.9978

    Precision Data (Linearity Solutions):

    Linearity LevelsPredicate Device Mean (SD/%CV)Proposed Device Mean (SD/%CV)
    Level 120.2 (0.96)20.4 (0.85)
    Level 262.2 (1.61)62.4 (1.76)
    Level 3116.4 (2.43)116 (2.58)
    Level 4300.8 (2.71)301.5 (2.40)
    Level 5505.6 (3.04)508.1 (3.78)

    Precision Data (Venous Whole Blood Samples):

    YSI MeanPredicate Device Mean (SD/%CV)Proposed Device Mean (SD/%CV)
    4949.7 (2.75)49.1 (2.32)
    8890.1 (2.79)88.8 (3.36)
    156154.4 (3.66)156.1 (3.23)
    263263.9 (3.05)265.9 (3.09)
    371364.0 (3.04)363.0 (2.69)
    458456.5 (2.80)453.0 (2.01)
    531535.9 (2.37)532.1 (1.73)

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

    • Sample Size (Accuracy Study): 100 deidentified venous whole blood samples with glucose concentrations ranging from 20-600 mg/dL.
    • Sample Size (Method Comparison and Precision Study - Venous Whole Blood): Seven (7) levels of deidentified venous whole blood samples, each tested 60 times (implied N for mean calculations in the table).
    • Sample Size (Method Comparison and Precision Study - Linearity Solutions): Five (5) levels of linearity solutions, each tested 60 times.
    • Data Provenance: "deidentified venous whole blood samples from consented donors." The country of origin is not specified, but it implies a prospective collection for the purpose of the study.

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

    The ground truth was established using a "reference analyzer" (YSI), not by human experts for this device. Therefore, this question is not applicable in the context of the provided document.


    4. Adjudication Method for the Test Set

    Not applicable. The ground truth was established by a reference analyzer (YSI), not through human adjudication.


    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 blood glucose meter, not an AI-assisted diagnostic imaging device or a system requiring human interpretation. The study focuses on the accuracy and precision of the meter itself compared to a reference method and a predicate device.


    6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done

    Yes, the studies conducted (Accuracy, Method Comparison, and Precision) are standalone performance evaluations of the device (meter and test strips) and its algorithm. The device measures glucose levels directly from blood samples and outputs a quantitative result without human-in-the-loop interpretation.


    7. The Type of Ground Truth Used

    The ground truth for the glucose measurements was established using a reference analyzer, specifically referred to as "YSI" in the tables and descriptions.


    8. The Sample Size for the Training Set

    The document does not explicitly mention a "training set" in the context of machine learning. The studies described are performance validation studies for a medical device. Therefore, information about a training set for an AI algorithm is not provided as it's not relevant to this type of device submission.


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

    As noted above, a "training set" for an AI algorithm is not discussed in the document for this device. The ground truth for the performance validation studies was established using a reference analyzer (YSI).

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