Search Results

The BD Logic™ Blood Glucose Monitor is intended to be used for the quantitative measurement of glucose in whole blood. It is intended for use by people with diabetes mellitus in the home as an aid to monitor the effectiveness of diabetes control. It is not intended for use in the diagnosis of or screening for diabetes mellitus and is not intended for use on neonates.

The BD Logic™ Blood Glucose Monitor is specifically indicated for the quantitative measurement of glucose in whole blood samples obtained from the fingertip.

The BD Logic™ Blood Glucose Monitor is intended for use in the quantitative measurement of glucose in capillary blood collected from fingertips.

The BD Logic™ Blood Glucose Monitor is designed to be simple and easy to use. It provides accurate blood glucose test results in 5 seconds using a small (0.3 µL) sample volume. The system also offers the convenience of containing all of the daily needs for diabetes care in one place as the System includes the blood glucose meter and conveniently holds the test strip vial, lancing device, spare lancets, insulin pen and additional insulin pen needles.

The BD Logic™ Blood Glucose Monitor is based on biosensor technology. When blood is applied to the Blood Glucose Test Strip, reagents on the test strip react with the blood and a current is generated. The BD Logic™ Blood Glucose Monitor employs amperometric technology to measure the glucose concentrations in the blood sample by measuring the amount of current that is generated and flows through the electrodes on the test strip.

Here's an analysis of the provided text regarding the BD Logic™ Blood Glucose Monitor, focusing on acceptance criteria and the study particulars:

The provided document is a 510(k) summary for the BD Logic™ Blood Glucose Monitor. As such, it establishes "substantial equivalence" to a predicate device rather than detailing specific, pre-defined acceptance criteria and their direct fulfillment through a dedicated study. The document states that "Laboratory and clinical studies demonstrate that the BD Logic™ Blood Glucose Monitor performed in an equivalent manner to the predicate and is suitable for its intended use" (See Section 7). However, it does not explicitly list numerical acceptance criteria or the full details of the studies conducted to prove these criteria.

Based on the information provided, here's what can be extracted and inferred:

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

The document does not provide a table of explicit acceptance criteria with specific numerical targets. It relies on the concept of "substantial equivalence" to a predicate device (Glucometer DEX Test Sensor). The implicit acceptance criterion is that the new device performs "in an equivalent manner" to the predicate.

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

The document mentions "Laboratory and clinical studies" but does not specify:

• The sample size used for the test set.
• The country of origin of the data.
• Whether the data were retrospective or prospective.

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 document. For blood glucose monitors, ground truth is typically established using a laboratory reference method, not by human experts interpreting results.

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

This information is not provided in the document. Adjudication methods like '2+1' or '3+1' are typically used when human interpretation of data (e.g., medical images) is involved to establish ground truth, which is not the case for a blood glucose monitor's performance evaluation 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

An MRMC study is relevant for devices involving human interpretation of complex medical data (e.g., AI in radiology). This device is a blood glucose monitor, and thus an MRMC study is not applicable and was not conducted for this type of device.

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

The device itself is a standalone system for measuring glucose. Its "performance" refers to the accuracy of its direct measurements. The 510(k) summary implies that the evaluation focused on the device's ability to accurately measure glucose, which can be considered its standalone performance. The document states it "performed in an equivalent manner to the predicate," which indicates a standalone performance comparison.

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

For blood glucose monitors, the ground truth is typically established by laboratory reference methods (e.g., using a YSI glucose analyzer), which are considered highly accurate. While not explicitly stated, this is the standard practice for evaluating such devices. The document implies comparison to a "predicate" device, which would also have been validated against such reference methods.

8. The sample size for the training set

The document does not mention a "training set" in the context of device development or performance evaluation. Blood glucose monitors are typically characterized for their analytical performance directly through clinical and laboratory studies, not through machine learning training sets in the same way an AI algorithm would be.

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

As there is no mention of a "training set" in the context of machine learning, this question is not applicable based on the provided document. The device operates based on biosensor technology and amperometric measurements, not a machine learning model that requires a labeled training set.

Ask a specific question about this device