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The Accu-Chek Guide Blood Glucose Monitoring System is comprised of the Accu-Chek Guide meter and the Accu-Chek Guide test strips.

The Accu-Chek Guide Blood Glucose Monitoring System is intended to quantitatively measure glucose in fresh capillary whole blood from the fingertip, palm, and upper arm as an aid in monitoring the effectiveness of glucose control.

The Accu-Chek Guide Blood Glucose Monitoring System is intended for in vitro diagnostic single-patient use by people with diabetes.

The Accu-Chek Guide Blood Glucose Monitoring System is intended to be used by a single person and should not be shared.

This system is not for use in diagnosis or screening of diabetes mellitus, nor for neonatal use.

Alternative site testing should be done only during steady-state times (when glucose is not changing rapidly).

Accu-Chek Guide Control Solutions are for use with the Accu-Chek Guide Blood Glucose Monitoring System to check that the meters and test strips are working together properly and that the test is performing correctly.

The Accu-Chek Guide System consists of the following:

• Accu-Chek Guide meter
• Accu-Chek Guide test strips
• Accu-Chek Guide control solutions (previously cleared in K043474)

The Accu-Chek Guide Blood Glucose Monitoring System makes use of the Accu-Chek Guide test strips, the Accu-Chek Guide meter, and the Accu-Chek Guide control solutions. This system is a single-patient use blood glucose monitoring system intended to be used to quantitatively measure glucose in fresh capillary whole blood from the fingertip, palm, and upper arm as an aid in monitoring the effectiveness of glucose control. It should be noted that the Accu-Chek Guide control solutions are identical to the Accu-Chek Aviva control solutions that were cleared previously in K043474. The name has simply been updated to reflect their use with the Accu-Chek Guide system.

The provided document describes the ACCU-CHEK Guide Blood Glucose Monitoring System and its performance. However, it does not involve an AI or algorithmic device in the context of medical imaging or diagnosis. Instead, it concerns a blood glucose meter, a device that performs a direct chemical measurement. Therefore, many of the requested categories, such as "number of experts used to establish ground truth," "adjudication method," "MRMC comparative effectiveness study," and "sample size for the training set," are not applicable as they relate specifically to AI/ML or image interpretation studies.

Here's a breakdown of the relevant information from the document regarding acceptance criteria and performance:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for blood glucose monitoring systems are generally based on accuracy standards, often requiring a certain percentage of results to fall within specified variations from a reference method. The document provides performance data based on differences from a reference measurement. The provided data is not explicitly labeled as "acceptance criteria," but rather as "user performance data." However, these percentages directly describe how well the device performs against established accuracy thresholds. I will present the provided performance data as demonstrating how the device met implied or industry-standard acceptance levels for accuracy.

Summary of Performance:
The "user performance data" and "repeatability (within lot) precision" data demonstrate that the Accu-Chek Guide Blood Glucose Monitoring System meets strong accuracy and precision standards for measuring glucose in various capillary blood samples and with control solutions. For instance, for glucose levels ≥ 75 mg/dL, the device achieves 99.1% of measurements within ±15% and 100% within ±20% for fingertip samples, which are common accuracy targets in the industry. Precision metrics show low CVs, indicating good consistency.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size:
  • Fingertip: 12 samples for < 75 mg/dL, 108 samples for ≥ 75 mg/dL.
  • Palm AST: 9 samples for < 75 mg/dL, 363 samples for ≥ 75 mg/dL.
  • Upper Arm AST: 10 samples for < 75 mg/dL, 355 samples for ≥ 75 mg/dL.
  • Repeatability (within lot) precision: 300 measurements for each of 5 blood levels.
  • Control solutions precision: 300 measurements for each of 3 control levels.
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. However, "user performance data" typically implies prospective studies where users perform measurements. The precision studies are laboratory-based. Roche Diabetes Care, Inc. is based in Indianapolis, IN, USA, which is the listed address of the submitter.

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)

This question is not applicable to a blood glucose monitoring system. The "ground truth" (reference glucose values) is established by a highly accurate laboratory reference method, not by expert interpretation or consensus. No human experts are used to establish "ground truth" in this context.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This question is not applicable to a blood glucose monitoring system. Adjudication methods are relevant for subjective interpretations of data, such as medical images. For blood glucose measurements, the reference method provides a definitive numerical value.

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

This question is not applicable. The device is a standalone blood glucose meter for direct measurement, not an AI-powered diagnostic tool requiring human-in-the-loop analysis or comparative effectiveness studies with human readers.

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

Yes, the performance data presented (e.g., user performance, repeatability) are for the device operating in a standalone manner (algorithm only, without human-in-the-loop diagnostic interpretation). The system quantitatively measures glucose based on electrochemical reactions and integrated algorithms to convert the signal to a glucose value. The "human-in-the-loop" aspect for a blood glucose meter refers to the user taking the measurement, not a human clinician interpreting an algorithmic output.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

The ground truth for blood glucose monitoring systems is established using a highly accurate laboratory reference method for glucose measurement (e.g., YSI analyzer, hexokinase method). This method provides a precise, quantitative biochemical gold standard for glucose concentration.

8. The sample size for the training set

This question is not applicable. Blood glucose meters like the Accu-Chek Guide are based on electrochemistry and fixed algorithms, not machine learning or AI models that require data "training sets." The device's operational parameters and calibration are determined through extensive analytical studies and manufacturing processes, not through a "training set" in the AI sense.

9. How the ground truth for the training set was established

This question is not applicable for the reasons stated in point 8.

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