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510(k) Data Aggregation
(52 days)
MEG-2B Blood Glucose Monitoring System: The MEG-2B 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. It is indicated for lay use by people with diabetes as an aid to monitoring levels in Diabetes Mellitus and should only be used by a single patient. This system should not be shared. It is not indicated for the diagnosis or screening of diabetes or for neonatal use.
MEG-2B Pro Blood Glucose Monitoring System: The MEG-2B Pro 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. It is indicated to be use for multiple patients in a clinical setting by healthcare professionals, as an aid to monitoring levels in Diabetes Mellitus. This system is only used with single-use, auto-disabling lancing device. It is not indicated for the diagnosis or screening of diabetes or for neonatal use.
MEG-2B Blood Glucose Test Strips: The MEG-2B Blood Glucose Test Strips are to be used with the MEG-2B Blood Glucose Meters to quantitatively measure glucose in capillary whole blood taken from fingertips, palm, or forearm. They are intended for lay use by people with diabetes and should only be used by a single patient. This system should not be shared. They are not indicated for the diagnosis or screening of diabetes or for neonatal use.
MEG-2B Pro Blood Glucose Test Strips: The MEG-2B Pro Blood Glucose Test Strips are to be used with the MEG-2B Pro Blood Glucose Meter; it measures glucose in capillary whole blood taken from a fingertip, palm, or forearm. It is indicated in a clinical setting to be used for multiple patients by healthcare professionals. This system is only used with single-use, auto-disabling lancing device.
MEG-2B Glucose Control Solutions: The purpose of the control solution test is to validate the performance of the Blood Glucose Monitoring System using a testing solution with a known range of glucose. A control test that falls within the acceptable range indicates the user's technique is appropriate and the test strip and meter are functioning properly.
The MEG-2B blood glucose meter and MEG-2B test strips are used for testing of blood glucose by self-testers at home. The MEG-2B Pro meter and strips are identical to the MEG-2B versions except they are sold with labeling oriented toward the professional user, rather than the self-testing home user. MEG-2B Glucose Control Solutions are used for quality control testing of the system.
The provided 510(k) summary for the MEG-2B Blood Glucose Monitoring System indicates that no clinical testing was performed. Therefore, there are no specific acceptance criteria or a study proving the device meets those criteria through clinical performance outlined in this document.
The submission focuses entirely on non-clinical testing to demonstrate substantial equivalence to the predicate device.
Here's a breakdown of the requested information based on the provided text, highlighting the absence of clinical study data:
1. Table of Acceptance Criteria and Reported Device Performance
As no clinical testing was performed, there are no acceptance criteria for clinical performance explicitly stated in this document. The non-clinical testing focused on demonstrating substantial equivalence to the predicate device.
| Acceptance Criteria Category (Non-Clinical) | Reported Device Performance |
|---|---|
| EMC and Electrical Safety | Results demonstrate substantial equivalence to the predicate system. |
| Drop Testing | Results demonstrate substantial equivalence to the predicate system. |
| Test Strip Holder Qualification | Results demonstrate substantial equivalence to the predicate system. |
| Software Verification and Validation (incl. unit integration testing) | Results demonstrate substantial equivalence to the predicate system. |
| Control Solution Qualification | Results demonstrate substantial equivalence to the predicate system. |
| Linearity Testing with Validation of Lo/Hi detection | Results demonstrate substantial equivalence to the predicate system. |
2. Sample size used for the test set and the data provenance
- Sample Size for Test Set: Not applicable, as no clinical test set was used.
- Data Provenance: Not applicable, as no clinical data was used. The non-clinical tests were conducted by the manufacturer.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable, as no clinical test set was used, and thus no expert ground truth was established for clinical performance.
4. Adjudication method for the test set
Not applicable, as no clinical test set was used.
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 device is a blood glucose monitoring system, not an AI-assisted diagnostic imaging device that would typically involve human readers. No MRMC study was performed.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This refers to the intrinsic analytical performance of the device (meter and test strip) in measuring glucose concentrations. The "Non-Clinical Testing" section mentions "linearity testing," which assesses the device's accuracy across a range of glucose concentrations, a form of standalone performance evaluation. The results concluded substantial equivalence to the predicate system.
7. The type of ground truth used
For the non-clinical tests (e.g., linearity), the ground truth would typically be established using a reference laboratory method for glucose measurement (e.g., a YSI analyzer), rather than expert consensus, pathology, or outcomes data. This is implied by "linearity testing with validation of Lo/Hi detection."
8. The sample size for the training set
Not applicable. The description does not indicate the use of machine learning or AI models that would require a training set. The device appears to utilize a fixed algorithm, potentially derived from prior R&D, but not a "training set" in the context of recent AI development.
9. How the ground truth for the training set was established
Not applicable, as no training set was identified.
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