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The ASCENSIA BREEZE Blood Glucose Meter is used with the ASCENSIA™ AUTODISC™ Blood Glucose Test Strips and ASCENSIA™ AUTODISCTM CONTROLS for the measurement of glucose in whole blood. The ASCENSIA BREEZE Blood Glucose Meter is an Over-the-Counter (OTC) device used by persons with diabetes and by healthcare professionals in home settings.

The ASCENSIA BREEZE Blood Glucose System is indicated for use with venous, and capillary whole blood samples drawn from the fingertip, palm, forearm, abdomen and thigh.

The frequent monitoring of blood glucose is an adjunct to the care of persons with diabetes.

The ASCENSIA™ BREEZE™ consists of an electrochemical method- based meter and dry reagent sensors (test strips) designed for testing glucose by persons with diabetes.

Here's a breakdown of the acceptance criteria and study information for the Ascensia BREEZE™ Blood Glucose Meter, extracted from the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state numerical "acceptance criteria" in a table format. Instead, it describes equivalence to a predicate device. The performance is assessed against the performance of the predicate device, ASCENSIA™ DEX® 2.

Note: The FDA's 510(k) clearance is based on a determination of "substantial equivalence" to a legally marketed predicate device, rather than meeting pre-defined numerical performance targets in the same way a PMA (Premarket Approval) might require. The "acceptance criteria" here are implicitly tied to demonstrating this substantial equivalence.

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

The document states an "evaluation of the ASCENSIA™ BREEZE™ Blood Glucose Meter was conducted at five clinical sites." However, it does not specify the sample size (number of participants or tests) used in this evaluation.

• Data Provenance: Clinical sites. The specific country of origin is not mentioned, but the submitter is Bayer Corporation, Elkhart, IN, USA, implying the study was likely conducted in the USA.
• Retrospective or Prospective: Not explicitly stated, but clinical evaluations for device clearance are typically prospective in nature, collecting new data specifically for the study.

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method.

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

This is not applicable to a blood glucose meter study. MRMC studies are typically for image interpretation tasks performed by multiple human readers (e.g., radiologists). This device is a direct measurement tool. The study did, however, evaluate the device "in the hands of diabetics," suggesting real-world user performance was considered.

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

Yes, the primary evaluation described is of the device itself (algorithm only, in the sense of the chemical and electronic measurement process) as performed by users. The "Assessment of Performance" section focuses on the meter's equivalence. While users operate the device, the performance being assessed is the meter's ability to accurately measure glucose, not human interpretation skills.

7. The Type of Ground Truth Used

The document states that "Blood glucose results are reference to plasma glucose." This strongly implies that the ground truth was established by a laboratory reference method for plasma glucose.

8. The Sample Size for the Training Set

The document does not provide information on a training set size. For a blood glucose meter, the "training" (calibration) is typically part of the device's design and manufacturing process, not a separate data-driven machine learning training set as might be seen for AI algorithms. The "linear response to glucose from 10-600 mg/dL" implies intrinsic calibration.

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

As no training set is explicitly discussed in the context of a machine learning-style model, this question is not directly applicable. However, the inherent calibration and accuracy of the device (its "training" in a broad sense) would be based on precise chemical standards and reference methods for glucose measurement, ensuring its linear response from 10-600 mg/dL, with plasma glucose as the reference.

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