Search Results

The ACCU-CHEK Aviva Connect Blood Glucose Monitoring System is intended to be used for the quantitative measurement of glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips. The ACCU-CHEK Aviva Connect Blood Glucose Monitoring System is intended to be used by a single person and should not be shared.

The ACCU-CHEK Aviva Connect Blood Glucose Monitoring System is intended for self testing outside the body (in vitro diagnostic use) by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. The ACCU-CHEK Aviva Connect Blood Glucose Monitoring System should not be used for the diagnosis of or screening of diabetes or for neonatal use.

The ACCU-CHEK Aviva Plus Test Strips are for use with the ACCU-CHEK Aviva Connect Blood Glucose Meter to quantitatively measure glucose (sugar) in fresh capillary whole blood samples drawn from the fingertips.

For in vitro diagnostic use

The ACCU-CHEK Aviva Connect blood glucose monitoring system is a blood glucose monitoring system that makes use of the ACCU-CHEK Aviva Connect meter, the ACCU-CHEK Aviva Plus test strips (K101299), and the ACCU-CHEK Aviva control solutions (K101299). The ACCU-CHEK Aviva Connect meter is a modification of the ACCU-CHEK Aviva meter (K133862) with an improved design and the addition of a USB port and the BLE communication capability.

The provided text describes the ACCU-CHEK Aviva Connect Blood Glucose Monitoring System and its substantial equivalence to a predicate device. However, it does not contain a detailed study report with all the requested information for acceptance criteria and device performance.

Based on the available information, here's what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance

The document mentions "Precision: For response targets < 75 mg/dL, the SD is ≤ 5.0 mg/dL, and for response targets ≥ 75 mg/dL, the CV is ≤ 5.0%." This is an acceptance criterion for precision.

The document claims that "Performance testing on the ACCU-CHEK Aviva Connect System demonstrated that the device meets the performance requirements for its intended use" and "The data demonstrates that the ACCU-CHEK Aviva Connect System is substantially equivalent to the predicate device." However, the actual reported device performance data that proves it meets these criteria is not provided in the excerpt.

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

This information is not provided in the excerpt. The document only states "Performance testing on the ACCU-CHEK Aviva Connect System demonstrated that the device meets the performance requirements". It does not specify the sample size, type of study (retrospective/prospective), or data provenance.

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 information is not provided in the excerpt. The document refers to a "Blood Glucose Monitoring System," which typically compares device readings against a laboratory reference method, not necessarily expert consensus in the same way an imaging diagnostic device would.

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

This information is not provided in the excerpt.

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 is not applicable as the device is a Blood Glucose Monitoring System, not an AI-assisted diagnostic tool that involves human readers interpreting cases.

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

The device is a standalone blood glucose monitoring system. The performance testing would inherently be standalone algorithm/device performance, as it quantifies glucose levels directly. The document states it is "intended for self testing outside the body... by people with diabetes at home as an aid to monitor the effectiveness of diabetes control." This implies standalone use.

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

While not explicitly stated, for a Blood Glucose Monitoring System, the "ground truth" (or reference method) for glucose measurements is typically established by laboratory reference methods (e.g., YSI analyzer) which are considered highly accurate.

8. The sample size for the training set

This information is not provided in the excerpt. The document does not mention a "training set" in the context of machine learning. For traditional medical devices like blood glucose meters, "training sets" are not typically discussed in the same way as for AI/ML models. Performance is usually evaluated through analytical and clinical studies.

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

As there is no mention of a "training set" in the context of AI/ML, this information is not provided and likely not relevant to the device as described. If "training set" refers to samples used for initial development or calibration, the ground truth would also be established via laboratory reference methods.

In summary, the provided document focuses on the regulatory submission and claims substantial equivalence based on performance testing but does not provide the detailed study results or methodology that would fully address all the requested points.

Ask a specific question about this device