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The Accu-Chek Active system is designed to quantitatively measure the concentration of glucose in capillary whole blood. The device is indicated for professional use and over-the-counter sale. The Accu-Chek Active system is indicated for use with capillary whole blood samples drawn from the fingertips, forearm, upper arm, thigh, calf and palm.

Instrument Operating Principle -- photometry
Reagent Test Principle -- glucose dehydrogenase

Here's an analysis of the provided text regarding the Accu-Chek Active System's acceptance criteria and study:

1. Table of Acceptance Criteria and Reported Device Performance

The provided summary focuses on demonstrating that the modified Accu-Chek Active System maintains its accuracy when used with alternate sampling sites (palm) compared to fingertip measurements, and that its performance for existing alternate sites (calf, thigh, upper arm) remains consistent.

Acceptance Criteria for Correlation (Implicit): The study aimed to demonstrate a "substantial correlation (r > 0.90)" between AST (Alternate Site Testing) results and finger results.

Reported Device Performance:

Conclusion regarding acceptance criteria: Both palm sampling sites achieved a correlation coefficient (r) of greater than 0.90 (0.990 and 0.988 respectively), indicating that the device met the implicit acceptance criterion for substantial correlation.

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

• Sample Size: 65 subjects for each palm alternate site (palm near thumb and palm near small finger), for a total of 130 samples specifically reported in the table. The phrase "With these data it is proved that the system accuracy with AST blood from calf, thigh and upper arm is unchanged from forearm" suggests additional data might have been collected for these sites, but the detailed statistics are only provided for the palm.
• Data Provenance: The study was "conducted at our manufacturing facility." This suggests it was an internal company study. There is no explicit mention of the country of origin for the data or whether it was retrospective or prospective. Given it's a study demonstrating consistent performance for a modified device, it is most likely a prospective study where samples were actively collected for the purpose of the study.

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

This information is not provided in the document. For a blood glucose meter, the "ground truth" or reference method is typically an established laboratory reference analyzer, not expert consensus. The document does not specify the reference method used.

4. Adjudication Method for the Test Set

This is not applicable as the ground truth for a blood glucose meter is typically established by a laboratory reference method, not by human expert adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size of how much human readers improve with AI vs without AI assistance

• Not applicable. This document describes a modification to a blood glucose meter, which is an in vitro diagnostic device. It does not involve human readers interpreting images or data, nor does it concern AI assistance.

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

• Yes, implicitly. The data presented are direct comparisons between the Accu-Chek Active System's readings from different sample sites. The performance metrics (slope, intercept, correlation) describe the device's accuracy without human intervention playing a role in interpreting the result displayed by the meter.

7. The Type of Ground Truth Used

The document does not explicitly state the specific reference method used to establish the "ground truth" or comparator for the finger results. However, for blood glucose meters, the standard ground truth is typically:

• A laboratory reference method (e.g., YSI analyzer, hexokinase method) run on a plasma sample, which is then mathematically converted to whole blood equivalent glucose values.
• Alternatively, the "finger results" from the Accu-Chek Active Meter itself could be considered the reference if the goal was solely to prove equivalence of AST to fingertip measurements with the same meter. The text "AST Sample versus Finger (Same Accu-Chek Active Meter)" suggests that the fingertip measurement from the same device served as the comparator for the AST readings.

8. The Sample Size for the Training Set

This information is not provided. The data presented is for the validation/test set. Blood glucose meters typically do not involve "training sets" in the same way machine learning algorithms do. Development and calibration would involve internal studies, but those are distinct from the validation study described here.

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

• Not applicable / Information not provided. As mentioned above, a traditional "training set" with ground truth in the context of machine learning is not directly relevant here. For the internal development and calibration of the glucose meter, ground truth would have been established using highly accurate laboratory reference methods.

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The Accu-Chek Active system is designed to quantitatively measure the concentration of glucose in capillary whole blood. The device is indicated for professional use and over-the-counter sale.

The Accu-Chek Active system is indicated for use with capillary whole blood samples drawn from the fingertips, forearm, upper arm, thigh, and calf

Instrument Operating Principle -- photometry
Reagent Test Principle -- glucose dehydrogenase

Here's a breakdown of the acceptance criteria and study information for the Accu-Chek Active System (K021448):

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as numerical targets for accuracy (e.g., % within a certain range). Instead, the study aims to demonstrate "good correlation (r > 0.90)" between results from different Alternative Site Testing (AST) locations and finger results.

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

The data is prospective as it was generated from a study specifically conducted to demonstrate the device's performance with additional AST sites.

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 given document. The study compares AST results to "finger results," which is implicitly considered the reference measure. It doesn't mention a separate expert review process for the glucose readings themselves.

4. Adjudication Method for the Test Set

This information is not provided in the given document. Since the study compares the device's readings from different sites to readings from a fingerstick (likely the same device's reading from a fingerstick or a laboratory reference), a traditional adjudication method for subjective medical imaging interpretation wouldn't apply here.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This type of study focuses on human reader performance, often in conjunction with AI. The provided study is a device performance study, not one evaluating human readers.

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

Yes, a standalone study was done. The study assesses the accuracy of the Accu-Chek Active System when using blood samples from various alternative sites. It evaluates the device's performance independently, without human interpretation as a primary factor in the measured outcome. The device itself is the "algorithm" in this context (photometric measurement).

7. The Type of Ground Truth Used

The ground truth used is fingerstick blood glucose readings. The study implicitly uses finger results (from the same device or a similar, validated method) as the reference standard to which the Alternative Site Test (AST) results are compared. The phrase "AST location versus Finger" directly indicates this comparison.

8. The Sample Size for the Training Set

The document does not provide information on the sample size for a training set. This is a device performance study, not specifically an AI/machine learning model where a training set distinct from a test set would typically be described. The "study" mentioned likely refers to the overall validation of the modified device rather than the training of a computational algorithm.

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

Since there is no mention of a separate training set or an AI/machine learning model being developed, this information is not applicable/provided in the document. The device's underlying technology (photometry and glucose dehydrogenase chemical reaction) is well-established, and its "training" would have occurred during the initial development and validation of the predicate device (K012324), not detailed here.

Ask a specific question about this device

The Accu-Chek Active system is designed to quantitatively measure the concentration of glucose in capillary whole blood. The device is indicated for professional use and over-the-counter sale.

Instrument Operating Principle -- photometry
Reagent Test Principle -- glucose dehydrogenase

The provided text describes the Accu-Chek Active System and its substantial equivalence to a predicate device, focusing on features and intended use. However, it does not contain detailed information regarding the specific acceptance criteria, a comprehensive study report, sample sizes, expert qualifications, or ground truth establishment methods in the depth requested. The document is primarily a 510(k) summary and an FDA clearance letter.

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

Acceptance Criteria and Device Performance Study

1. Table of Acceptance Criteria and Reported Device Performance

The document states that "All predetermined acceptance criteria were satisfied" but does not provide specific numerical acceptance criteria nor detailed reported device performance values. It only generally states that "The clinical data demonstrates that the performance of the Accu-Chek Active correlates well with the laboratory plasma glucose reference test method."

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not specified in the provided text.
• Data Provenance: A "multi-center performance study" was conducted. The country of origin is not explicitly stated. It is a prospective study, as it was conducted to "evaluate the accuracy and precision of the modified device."

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not specified in the provided text.

4. Adjudication method for the test set

Not specified in the provided text.

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: Not applicable. This device is a glucose meter, an automated system, not an AI-assisted diagnostic imaging or interpretation system requiring human readers. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not relevant to this device.
• Effect size of human reader improvement: Not applicable.

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

Yes, a "multi-center performance study" was conducted to evaluate the accuracy and precision of the device itself, which operates as an algorithm-only system without human-in-the-loop performance for glucose measurement. The device's performance was compared to "the laboratory plasma glucose reference test method."

7. The type of ground truth used

The ground truth was established by a "laboratory plasma glucose reference test method." This implies comparison to a validated and accurate laboratory-based glucose measurement method.

8. The sample size for the training set

Not specified in the provided text. (For this type of device, what might be referred to as "training" would be part of the R&D and calibration process, not distinct in the same way as an AI model's training set).

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

Not specified in the provided text. (As above, this information is not typically presented for a glucose meter in this format).

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