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510(k) Data Aggregation
(138 days)
The ACCU-CHEK Aviva Expert System is indicated as an aid in the treatment of insulin-requiring diabetes. The ACCU-CHEK Aviva Expert System consists of the ACCU-CHEK Aviva Expert Meter, ACCU-CHEK Aviva Plus test strips, ACCU-CHEK Aviva control solutions, and ACCU-CHEK Bolus Advisor. The ACCU-CHEK Aviva Expert System is intended to facilitate the optimization of glycemic control in multiple daily insulin injection therapy and are under the supervision of healthcare professionals experienced in managing insulin treated patients. The ACCU-CHEK Aviva Expert blood glucose monitoring system is intended to be used for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips. The ACCU-CHEK Aviva Expert blood glucose monitoring system is intended for self-testing outside the body (in vitro diagnostic use) by people with diabetes. The ACCU-CHEK Aviva Expert blood glucose monitoring system is intended to be used by a single person and should not be shared. The ACCU-CHEK Aviva Expert blood glucose monitoring system should not be used for the diagnosis or screening of diabetes or for neonatal use. Alternative site testing should NOT be used with the ACCU-CHEK Aviva Expert blood glucose monitoring system. The ACCU-CHEK Aviva Expert System is intended for prescription home use only.
The ACCU-CHEK Aviva Expert meter is also indicated for the calculation of an insulin dose or carbohydrate intake based on user-entered data. The ACCU-CHEK Bolus Advisor, as a component of the Accu-Chek Aviva Expert meter, is intended for use in providing insulin dose recommendations in response to blood glucose, health events, and carbohydrate input. The ACCU-CHEK Bolus Advisor is intended to provide direction for insulin adjustment within the scope of a preplanned treatment program from a healthcare professional. Before its use, a physician or healthcare professional must prescribe the ACCU-CHEK Aviva Expert System and provide the patient-specific target blood glucose, insulin-to-carbohydrate ratio, and insulin sensitivity parameters to be programmed into the ACCU-CHEK Bolus Advisor. Once programmed, a patient must consult with his/her physician or healthcare professional before making any changes to these ACCU-CHEK Bolus Advisor settings.
The ACCU-CHEK Aviva Expert System consists of the following which was originally cleared under K131366:
- ACCU-CHEK Aviva Expert meter
- ACCU-CHEK Bolus Advisor (a component of the Aviva Expert meter)
- ACCU-CHEK Aviva Plus test strips
- ACCU-CHEK Aviva control solutions
The ACCU-CHEK Aviva Expert system is a blood glucose monitoring system that makes use of the ACCU-CHEK Aviva Plus test strips and the ACCU-CHEK Aviva control solutions.
The ACCU-CHEK Aviva Expert system provides the user with the ability to measure capillary blood glucose levels when a sample of capillary blood is applied to the test strip. The meter also provides an optional insulin bolus calculator (the ACCU-CHEK Bolus Advisor) designed for use by individuals with diabetes who require insulin. This feature is optional in that a user can simply obtain a blood glucose value through capillary blood testing and does not need to use the insulin bolus calculator portion of the system if it is not desired. For the ACCU-CHEK Aviva Expert system, this bolus calculator is meant to be used by patients with diabetes on multiple daily insulin injection (MDI) therapy. In order to calculate the appropriate bolus of insulin, the ACCU-CHEK Bolus Advisor takes the measured bG, the target bG, the carbohydrate intake, the insulin-to-carbohydrate ratio, the insulin sensitivity, health events (such as exercise), the time of day, and the active insulin into account. Before using the ACCU-CHEK Aviva Expert system, a physician or healthcare professional must provide the patient-specific target blood glucose, insulin-to-carbohydrate ration, and insulin sensitivity parameters.
Here's a breakdown of the acceptance criteria and study information based on the provided text, focusing on the performance of the ACCU-CHEK Aviva Expert Blood Glucose Monitoring system:
The document describes the ACCU-CHEK Aviva Expert Blood Glucose Monitoring system and refers to performance testing that was submitted and cleared under a previous 510(k) (K131366). The current submission (K142089) states that "The ACCU-CHEK® Aviva Expert System has not changed since this prior submission and the information submitted here is provided to support the clarification in the intended use statement." Therefore, the performance data presented is for the identical device.
1. Table of Acceptance Criteria and Reported Device Performance
The provided document presents performance data as evidence of the device meeting requirements for its intended use, but does not explicitly state predetermined "acceptance criteria" as separate rows. However, to construct a table, we can infer the acceptance criteria from the reported results, assuming the reported values met the relevant regulatory standards for blood glucose monitoring systems at the time of clearance.
| Performance Metric | Acceptance Criteria (Inferred from common BGM standards) | Reported Device Performance |
|---|---|---|
| Accuracy (Glucose Concentrations < 75 mg/dL) | Within ±5 mg/dL | 85.4% (41/48 samples) |
| Within ±10 mg/dL | 100% (48/48 samples) | |
| Within ±15 mg/dL | 100% (48/48 samples) | |
| Accuracy (Glucose Concentrations ≥ 75 mg/dL) | Within ±5% | 58.3% (147/252 samples) |
| Within ±10% | 88.1% (222/252 samples) | |
| Within ±15% | 97.6% (246/252 samples) | |
| Within ±20% | 99.2% (250/252 samples) | |
| Repeatability (Within-lot precision) | SD ≤ 5.0 mg/dL for < 75 mg/dL, CV ≤ 5.0% for ≥ 75 mg/dL | Sample-specific: |
| Blood Sample 1 (Mean 42.1 mg/dL) | SD: 1.2 mg/dL, CV: 2.9% | |
| Blood Sample 2 (Mean 84.5 mg/dL) | SD: 2.2 mg/dL, CV: 2.6% | |
| Blood Sample 3 (Mean 137.8 mg/dL) | SD: 3.3 mg/dL, CV: 2.4% | |
| Blood Sample 4 (Mean 208.2 mg/dL) | SD: 5.6 mg/dL, CV: 2.7% | |
| Blood Sample 5 (Mean 345.0 mg/dL) | SD: 7.9 mg/dL, CV: 2.3% | |
| Reproducibility (Intermediate/Day-to-day precision) | SD and CV values likely within established professional standards for BGM systems | Control Solution-specific: |
| Low Control (Mean 45.1 mg/dL) | SD: 1.1 mg/dL, CV: 2.4% | |
| Mid Control (Mean 117.6 mg/dL) | SD: 2.4 mg/dL, CV: 2.0% | |
| High Control (Mean 303.0 mg/dL) | SD: 5.1 mg/dL, CV: 1.7% |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size (Accuracy Test):
- Glucose concentrations < 75 mg/dL: 48 samples
- Glucose concentrations ≥ 75 mg/dL: 252 samples
- Total for accuracy: 300 samples
- Sample Size (Repeatability Test): 100 measurements for each of 5 blood samples (Total 500 measurements)
- Sample Size (Reproducibility Test): 100 measurements for each of 3 control solutions (Total 300 measurements)
- Data Provenance: The document does not specify the country of origin for the data or whether it was retrospective or prospective. It only states that performance testing was done on the system.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not provide any information regarding the number of experts, their qualifications, or their involvement in establishing ground truth for the test set. For blood glucose monitoring systems, ground truth is typically established by a highly accurate laboratory reference method (e.g., YSI analyzer) rather than expert consensus on readings.
4. Adjudication Method for the Test Set
The document does not mention any adjudication method for the test set. Given that it's a quantitative measurement device, adjudication by experts (as in imaging) is typically not applicable; instead, the device's readings are compared directly against a reference method.
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
- MRMC Study: No, the provided document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. This type of study design is not typically applicable to a standalone blood glucose monitoring system, which provides a direct quantitative measurement rather than requiring human interpretation of complex data (like medical images).
- Effect Size of Human Reader Improvement with AI: Not applicable, as no MRMC study or "human reader" component in the traditional sense is described for this device. The "Bolus Advisor" is an automated calculation based on user input and measured BG, not an AI assisting human interpretation.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done
Yes, the performance data presented (accuracy, repeatability, reproducibility) represents the standalone performance of the ACCU-CHEK Aviva Expert Blood Glucose Monitoring system. It measures glucose concentration without human interpretation of the measurement itself, and the bolus advisor function is an algorithm-only component calculating insulin dose based on provided inputs. The performance of the Bolus Advisor algorithm itself would have been validated separately as part of the K131366 clearance (though details are not provided here).
7. The Type of Ground Truth Used
The ground truth used for the accuracy assessment is strongly implied to be a laboratory reference method for glucose measurement, which is standard for blood glucose monitoring systems. The document states "method comparison data," which means the device's readings were compared against another, more accurate method. While not explicitly named, the "method comparison" implies a highly precise and accurate lab instrument (e.g., YSI glucose analyzer) as the ground truth.
8. The Sample Size for the Training Set
The document does not specify any training set sample size. This is because the device described is a blood glucose monitoring system with a fixed algorithm for glucose measurement and a predefined calculation for the bolus advisor. These types of devices generally do not involve machine learning "training" in the way that an AI diagnostic algorithm might. The algorithms are built on known chemical and mathematical principles.
9. How the Ground Truth for the Training Set Was Established
Not applicable. As noted above, the concept of a "training set" with associated ground truth, as used in machine learning, does not align with the description of this device. The underlying principles for glucose measurement and insulin dose calculation are based on established scientific and medical models, rather than learned patterns from a training dataset.
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