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The GlucoSure ADVANCE Link Blood Glucose Monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood samples drawn from the fingertips, forearm, or palm. Alternative site testing should be performed only during steady-state (when glucose is not changing rapidly). Testing is done outside the body (In Vitro diagnostic use). It is intended for self-testing by people with diabetes at home as an aid to monitor the effectiveness of diabetes control. It should only be used by a single patient and should not be shared. It is not indicated for the diagnosis of or screening for diabetes or for neonatal use.

The GlucoSure ADVANCE Link Blood Glucose Monitoring System consists of the GlucoSure ADVANCE Link Blood Glucose Meter, GlucoSure ADVANCE Link Blood Glucose Test Strips and Contrex Plus 5 glucose control solution. It is used for testing of blood glucose by self-testers at home. The GlucoSure ADVANCE Link Blood Glucose Test Strips and Contrex Plus 5 glucose control solution are purchased separately. The modified device of GlucoSure ADVANCE Link Blood Glucose Meter is derived from the existing device of BGM014 Blood Glucose Meter and the modified device contain the Bluetooth function to transfer glucose results to the mobile device. The blood glucose test strips and glucose control solution utilized in the GlucoSure ADVANCE Link Blood Glucose Monitoring System are the same as the BGM014 Blood Glucose Test Strips and Contrex Plus 5 Glucose Control Solution, previously cleared in K161299. The meter materials of GlucoSure ADVANCE Link Blood Glucose Meter are the same as the BGM014 Blood Glucose Meter. Therefore, the disinfection performance (robustness of meter to multiple cleanings and disinfections) was previously cleared in K161299.

Here's an analysis of the provided text, focusing on the acceptance criteria and study information:

Acceptance Criteria and Device Performance

The provided 510(k) summary does not explicitly list a table of quantifiable acceptance criteria with corresponding reported device performance, which is typical for performance claims related to new algorithms or AI-driven devices. Instead, it describes various tests performed to demonstrate substantial equivalence to a predicate device.

However, based on the nature of a Blood Glucose Monitoring System, the primary performance characteristic is system accuracy. While not explicitly a table, the document mentions that a "Usability study confirmed the system accuracy." The common standards for blood glucose meter accuracy are from ISO 15197. Without the full test reports, the specific numerical acceptance criteria and reported performance values related to accuracy are not present in this document.

The document also implies acceptance criteria for:

• Battery Life: The new device must meet an acceptable battery life, which is noted to be decreased from 1000 tests to 750 tests compared to the predicate. The implicit acceptance criterion here would be that 750 tests is still considered acceptable for the intended use.
• Memory Capacity: The new device's memory capacity of 700 test results is a decrease from 1000. Similar to battery life, the implicit acceptance criterion is that 700 results are sufficient.
• EMC and Electrical Safety: The device must comply with relevant Electromagnetic Compatibility and Electrical Safety standards.
• Software Verification and Validation (including cybersecurity): The software must be verified and validated to ensure proper function and data security, especially with the added Bluetooth functionality.
• Disinfection Performance: The meter materials are the same as the predicate, so the disinfection performance must remain acceptable, as it was previously cleared.

Table of Implicit Acceptance Criteria and Reported Performance (based on available information):

Study Details from the Provided Text:

1. Sample size used for the test set and the data provenance:

   • The document mentions "A Usability study" and "Testing," but does not provide specific sample sizes (e.g., number of patients, number of blood samples) for any test set.
   • The provenance of the data (e.g., country of origin, retrospective or prospective) is not explicitly stated. It's likely prospective for a usability/clinical accuracy study, but this is an inference.

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

   • The document states "A Usability study confirmed the system accuracy." For blood glucose monitoring systems, ground truth is typically established by comparing capillary whole blood glucose readings to laboratory reference methods (e.g., YSI blood glucose analyzer) using venous plasma or serum.
   • The number of experts and their qualifications used to establish ground truth are not specified in this summary.

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

   • No adjudication method is mentioned in the provided text. For blood glucose accuracy studies, adjudication methods (like expert consensus) are typically not directly applicable as the ground truth is established by a quantitative laboratory reference method.

4. 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:

   • No MRMC study was mentioned or performed. This device is a Blood Glucose Monitoring System, which does not involve "human readers" or "AI assistance" in the typical diagnostic imaging sense. It's a direct measurement device.

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

   • Yes, implicitly. The "system accuracy" would be a standalone performance measure of the device itself (meter + test strips + algorithm) without requiring a human interpretation component, beyond the user following instructions. The device provides a quantitative number.

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

   • The specific type of ground truth is not explicitly stated in this summary but, in the context of blood glucose monitoring systems, ground truth for accuracy studies is typically established using a laboratory reference method (e.g., YSI 2300 STAT Plus Glucose & Lactate Analyzer measuring plasma glucose).

7. The sample size for the training set:

   • This device is a measurement system, not a machine learning/AI diagnostic system in the sense that would require a distinct "training set" for an algorithm to learn from data in a traditional ML paradigm. The "algorithm" here refers to the embedded logic for glucose measurement. Therefore, no training set in the typical AI sense is mentioned or implied.

8. How the ground truth for the training set was established:

   • As there's no mention of a traditional "training set" for an AI algorithm, this point is not applicable based on the provided text. The device's measurement algorithm is likely developed through engineering principles and calibration rather than data-driven machine learning training.

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