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Glucose Meter-Check Solution Roche ACCU-CHEK is intended to assess the performance of the following Roche ACCU-CHEK blood glucose monitoring systems:

· Roche ACCU-CHEK Nano SmartView® using SmartView® test strips

• · Roche ACCU-CHEK Aviva® and ACCU-CHEK Aviva Combo® using Aviva Plus® test strips
• · Roche ACCU-CHEK Active® using ACCU-CHEK Active test strips
• Roche ACCU-CHEK Compact® and ACCU-CHEK Compact Plus® using ACCU-CHEK Compact test strips.
• · Roche ACCU-CHEK Advantage using Comfort Curve® test strips

Glucose Meter-Check Solution for Roche ACCU-CHEK is intended for use by healthcare professionals and people with diabetes mellitus at home.

For In Vitro Diagnostic Use

Glucose Meter-Check Solution is a single-level, viscosity-adjusted, aqueous liquid glucose control solution. Glucose Meter-Check Solution is intended for use to verify the performance of the Roche ACCU-CHEK brand BGM Systems listed in the package insert at recover glucose values within the interval recommended in ISO 15197:2003 In vitro diagnostic test systems - Requirements for blood glucose monitoring systems for selftesting in managing diabetes mellitus for most meter types (Aviva, SmartView, Advantage), and comparable to solutions currently marketed by Roche for others (Active, Compact). This mid-level glucose concentration will complement the current, low and high glucose concentrations available in quality control products distributed by Roche Diagnostics for these same BGM systems. The product is packaged in plastic bottles with dropper tips for application of the solution to test strips. The control has a red color to help users see the solution while dispensing onto a test strip.

Glucose Meter-Check Solution is a non-hazardous aqueous solution glucose control solution containing no ingredients of biological origin, or in concentrations qualifying as a controlled product under the Controlled Products Regulation.

This material consists of viscosity-adjusted, aqueous glucose control solution prepared with a single concentration of D-glucose and has been optimized to simulate the response of whole blood on the Roche ACCU-CHEK blood glucose monitoring systems. The solution contains no hazardous, human or animal derived components. The solution is recognized as a control automatically by the ACCU-CHEK Aviva and Nano SmartView meters to perform consistently as the Roche branded products.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Device: Glucose Meter-Check® Solution for Roche ACCU-CHEK

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Value Assignment: For each meter type and for 3 lots of test strips, 8 measurements of the Glucose Meter-Check Solution were taken on each of 5 meters. This results in 120 measurements per meter type (3 lots x 8 measurements/lot x 5 meters/lot).
• Sample Size for Method Comparison/Equivalence: For each meter brand, 2 meters were used. For each meter, 1 measurement of Glucose Meter Check Solution was followed by 1 measurement of the Roche ACCU-CHEK branded control solution. This sequence was repeated until 25 sets of pairs were recorded per meter (25 measurements for the candidate device and 25 for the predicate device, per meter).
• Data Provenance: Not explicitly stated (e.g., country of origin). The testing described appears to be retrospective laboratory testing performed by Bionostics, Inc. to characterize the new control solution and compare it to existing solutions. It is not patient-derived data.

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

N/A. This device is a quality control solution for glucose meters, not a diagnostic algorithm that interprets medical images or patient data. The "ground truth" here refers to the known glucose concentration of the control solution itself and the expected performance ranges of the glucose meters. The "ground truth" for the control solution's glucose level is established through analytical methods (YSI 2300 calibrated with N.I.S.T. 917 traceable materials), not expert interpretation.

4. Adjudication Method for the Test Set

N/A. No human interpretation (such as radiological reads) is involved, so no adjudication method is applicable. The evaluation criteria are quantitative and based on instrument readings.

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

No, an MRMC study was not done. This study pertains to the performance of a quality control solution with blood glucose meters, not an AI-assisted diagnostic tool that involves human readers.

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

Yes, in a sense. The core "performance" of the Glucose Meter-Check Solution is its ability to produce expected glucose values when tested on various blood glucose monitoring systems. The studies evaluated the control solution itself and its interaction with the meters, which are standalone devices. There is no human-in-the-loop aspect for the control solution's function or the meter's measurement.

7. The Type of Ground Truth Used

The ground truth used is analytical measurement of glucose concentration traceable to a recognized standard:

• N.I.S.T. SRM 917 (National Institute of Standards and Technology Standard Reference Material 917)
• Measurements were performed using a YSI 2300 (a glucose analyzer).
• For the meter performance, the "ground truth" is defined by the expected assay ranges provided by Roche for their test strips and the assigned range for the Glucose Meter-Check solution (±15% of the grand mean).

8. The Sample Size for the Training Set

N/A. This is a quality control solution, not a machine learning algorithm. Therefore, there is no "training set" in the context of AI/ML. The device's formulation and performance specifications are developed through R&D and analytical testing, not supervised learning.

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

N/A. As there is no training set for a machine learning model, this question is not applicable.

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