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510(k) Data Aggregation
(55 days)
The FDI Glucose Control Solution is intended for use with Aviva Plus test strip when used on the Accu-Chek® Aviva blood glucose meter. The control solution is used with the Aviva Plus test strips to check that the meter and test strips are working together properly.
The FDI Glucose Control Solution for Aviva consists of a viscosity-adjusted, aqueous liquid control solution containing a known quantity of glucose. The product is packaged in plastic dropper tipped bottles for easy application of the control solutions to the test strips and a red coloration to aid the user to visually confirm application of the control. The product is non-hazardous and contains no human or animal derived materials.
Here's an analysis of the provided text regarding the acceptance criteria and study for the FDI Glucose Control Solution for Accu-Chek Aviva:
Acceptance Criteria and Device Performance Study for FDI Glucose Control Solution for Accu-Chek Aviva
1. Table of Acceptance Criteria and Reported Device Performance
| Performance Characteristic | Acceptance Criteria (Implicit) | Reported Device Performance |
|---|---|---|
| Shelf Life Stability | Not explicitly stated, but expected to meet the claimed shelf life. | Supports a shelf life of 24 months when stored at 15-30°C. |
| Open Vial Stability | Not explicitly stated, but expected to meet the claimed open vial stability. | Supports an open vial stability of 90 days when stored at 15-30°C. |
| Test Precision | Not explicitly stated. The document states "No precision claims will be made for this product." | Studies performed per CLSI document EP5-A2: 2004. No precision claims made. |
| Value Assignment | Acceptable ranges based on "predetermined acceptance criteria established by the manufacturer for glucose recovery for each lot." | The control solutions are analyzed using a single AVIVA monitor using three different lots of test strips, 10 replicates per strip lot, over three days. Glucose control value ranges are lot-dependent and printed on the vial label. |
| Traceability | Not explicitly stated, but expected to be traceable to a recognized standard. | Traceable to an in-house 45 mg/dL Standard, which is traceable to NIST Standard Reference Material 917b. |
| Substantial Equivalence (Overall) | The device's performance characteristics, formulation, and intended use must be substantially equivalent to the predicate device. | Comparison of performance characteristics, formulation, and intended use support the claim of substantial equivalence to the predicate device (ACCU-CHEK Aviva Control Level 1, K043474). |
2. Sample Size Used for the Test Set and Data Provenance
- Real Time Stability: "Samples were periodically removed and tested in triplicate..." The total number of samples or distinct time points is not specified.
- Open Vial Stability: "A number of test and control vials were evaluated for 13 weeks. Each week one test group vial was assayed and one control group vial was assayed in triplicate..." The exact "number of test and control vials" is not specified, but the evaluation spanned 13 weeks with weekly testing of one vial from each group.
- Value Assignment: "using three different lots of test strips, 10 replicates per strip lot, over three days." (This means 3 lots * 10 replicates = 30 measurements for value assignment.)
- Data Provenance: The document does not explicitly state the country of origin for the data or whether the studies were retrospective or prospective. Given that Fujirebio Diagnostics, Inc. is located in Seguin, TX, USA, it's reasonable to infer the studies were conducted in the US. The nature of these stability and performance verification studies suggests they were prospective, carried out specifically for regulatory submission.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
This information is not provided in the document. The studies described are analytical performance studies of a control solution, which typically do not involve human expert interpretation for "ground truth" in the same way an imaging or diagnostic AI would. The "ground truth" here is the measured glucose concentration, either by a clinical chemistry analyzer or the Accu-Chek Aviva meter itself, as calibrated to a reference standard.
4. Adjudication Method for the Test Set
This is not applicable as the studies described are analytical performance tests for a control solution, not studies requiring human expert judgment or adjudication (e.g., for disease diagnosis).
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
There was no MRMC comparative effectiveness study done. This device is a glucose control solution, not an AI-powered diagnostic device that would assist human readers in interpreting cases.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
This question is not directly applicable in the context of this device. The "device" itself is the control solution. The performance studies evaluate the control solution's stability and its interaction with the Accu-Chek Aviva meter. The meter itself operates in a "standalone" fashion (algorithm only) when it measures glucose, but the control solution is a reagent for quality control, not an algorithm.
7. The Type of Ground Truth Used
- For Stability Studies (Real Time & Open Vial): The ground truth was established by testing samples in triplicate on a "commercially available clinical chemistry analyzer." This implies a reference method or gold standard laboratory measurement for glucose concentration.
- For Value Assignment: The control is traceable to an in-house 45 mg/dL Standard, which is traceable to NIST Standard Reference Material 917b. This indicates a chemically certified reference standard as the ultimate ground truth for glucose concentration.
8. The Sample Size for the Training Set
This is not applicable as the FDI Glucose Control Solution is a physical control solution, not a machine learning or AI algorithm that requires a training set.
9. How the Ground Truth for the Training Set Was Established
This is not applicable for the reasons stated in point 8.
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