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The Accu-Chek Comfort Curve Test Strips are to be used with the Accu-Chek® Advantage® and Accu-Chek® Complete™ Monitors. The Accu-Chek Advantage and Accu-Chek Complete systems are designed for testing glucose in whole blood by persons with diabetes or by health care professionals in the home or in health care facilities.

Professionals may use the test strips to test capillary, venous, arterial and neonate (including cord) blood samples; lay use is limited to capillary blood testing.

The Accu-Chek Comfort Curve Test Strips are to be used with the Accu Chek® Advantage® and Accu-Chek® Complete™ Monitors. The Accu Chek Comfort Curve test strips are designed for convenient, confident, and accurate testing of blood glucose in whole blood samples.

This document describes the Accu-Chek® Comfort Curve™ Test Strip and its substantial equivalence to a previously cleared device. However, it does not contain specific acceptance criteria (e.g., accuracy metrics, specific thresholds) or a detailed study section that proves the device meets such criteria.

The information provided primarily focuses on:

• Introduction and Device Identification: Submitter, device name, predicate device, and device description.
• Intended Use: How and by whom the test strips are to be used (diabetics, healthcare professionals, types of blood samples).
• Comparison to Predicate Device: The key change mentioned is a modification to the reference curve in the code key. This change allows the test strips to report results that reflect plasma glucose values instead of whole blood glucose values, aligning with common laboratory reporting practices. This is presented as a modification to an existing, cleared device, implying that the fundamental performance characteristics (accuracy, precision, etc.) of the strip itself are assumed to be similar, with the primary change being the conversion factor applied to the raw electrical signal.

Therefore, many of the requested items cannot be extracted from the provided text.

Here is a summary of what can be inferred or stated based on the provided text, and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

• Acceptance Criteria: Not explicitly stated in the provided text. For a blood glucose meter, these would typically involve accuracy metrics compared to a laboratory reference method (e.g., ISO 15197 standards for bias, precision, and agreement within certain error bounds).
• Reported Device Performance: Not explicitly stated in terms of specific study results (e.g., mean bias, standard deviation of differences, percentage of values within +/-X% of reference). The document states that Boehringer Mannheim "has modified the reference curve employed within the code key used with the Accu-Chek Comfort Curve test strips to now provide results which reflect the higher laboratory plasma glucose value." This implies a recalibration rather than a new performance study to establish primary accuracy metrics for the device as a whole, given it's a modification to an already cleared device.

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

• Sample Size: Not mentioned.
• Data Provenance: Not mentioned.

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

• Not applicable as no specific test set study is detailed. The ground truth would typically come from a laboratory reference method, not expert consensus in this context.

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

• Not applicable.

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

• Not applicable. This is a medical device for measuring blood glucose, not an AI-powered diagnostic imaging tool.

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

• Not applicable as this is a blood glucose test strip, not an algorithm in the traditional sense of AI. The device itself performs the measurement.

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

• While not explicitly stated, for a blood glucose test strip, the ground truth is universally established by a laboratory reference method (e.g., hexokinase method, glucose oxidase method) using a laboratory analyzer. The document mentions "laboratory plasma value," which strongly infers this type of ground truth.

8. The sample size for the training set

• Not applicable/Not mentioned. The "training set" concept (in the machine learning sense) doesn't directly apply here. The "reference curve" modification represents a recalibration, likely based on data comparing the strip's output to laboratory reference measurements. The size of that calibration data set is not specified.

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

• As mentioned in point 7, the ground truth would have been established using laboratory reference methods to obtain plasma glucose values.

Ask a specific question about this device

The Accu-Chek Comfort Curve Test Strips are to be used with the Accu-Chek® Advantage® and Accu-Chek® Complete™ Monitors. The Accu-Chek Advantage and Accu-Chek Complete systems are designed for testing glucose in whole blood by persons with diabetes or by health care professionals in the home or in health care facilities.

Professionals may use the test strips to test capillary, venous, arterial and neonate (including cord) blood samples; lay use is limited to capillary blood testing.

The Accu-Chek Comfort Curve Test Strips are to be used with the Accu-Chek® Advantage® and Accu-Chek® Complete™ Monitors. The Accu-Chek Comfort Curve test strips are designed for convenient, confident, and accurate testing of blood glucose in whole blood samples.

The provided text is a 510(k) summary for the Accu-Chek® Comfort Curve™ Test Strip. It describes the device, its intended use, and its substantial equivalence to a predicate device, but it does not contain details about specific acceptance criteria or a study proving the device meets those criteria.

Therefore, I cannot provide the requested information based on the input text. The document focuses on regulatory approval and comparison to a predicate device rather than performance study results.

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