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510(k) Data Aggregation
(119 days)
GLUCOSE PILOT
The Glucose Pilot Blood Glucose monitoring System is intended for the quantitative measurement of glucose in fresh capillary whole blood from a finger stick, for the lay-user. It is also intended for the professional use, which include fresh capillary whole blood. It is intended for use outside the body (in vitro diagnostic use) by people with diabetes mellitus at home as an aid in monitoring the effectiveness of a diabetes control program. It is not intended for the diagnosis of or screening for diabetes mellitus, and is not intended for use on neonates.
The Glucose Pilot Glucose monitoring System consists of a hand-held blood glucose meter, test strips, lancing device, lancets, and two levels of control solution. The test strip has a lot - specific calibration information and the meter reads automatically. The meter is turned on by inserting the strip, the user supplies the capillary blood or control solution, the meter makes an audible tone and starts the assay, which completes in five seconds. The meter's software converts the results read off the test strip into a plasma glucose concentration and displays the value on the meter's display. The provided instructions and illustrations explaining that the blood drop will be pulled into the strip sample entry by capillary action. Results are stored in the meter's memory for tracking purposes.
Here's an analysis of the provided text to extract the requested information about acceptance criteria and the device study:
Important Note: The provided document is a 510(k) summary for a blood glucose monitoring system. These typically focus on demonstrating substantial equivalence to a predicate device rather than presenting a full clinical study with detailed performance metrics against defined acceptance criteria in the same way a novel high-risk device might. Consequently, some of the requested information (especially regarding expert adjudication, MRMC studies, explicit acceptance criteria with pre-defined ground truth for this specific device's performance claims) is not explicitly stated in this type of document. I will extract what is available and highlight where information is absent due to the nature of the submission.
Acceptance Criteria and Device Performance
The document doesn't explicitly list a table of acceptance criteria with corresponding device performance metrics in the format you requested for a complex diagnostic device. Instead, it focuses on demonstrating substantial equivalence to an existing predicate device (Ultra One Touch Glucose Monitoring System). The "acceptance criteria" in this context are implicitly that the new device performs similarly or equivalently to the predicate device across various characteristics and specifications.
However, based on the Comparison to Predicate Device(s) table, we can infer performance specifications that the new device aims to meet or match.
1. Table of Acceptance Criteria and the Reported Device Performance
Since explicit "acceptance criteria" are not given, I will present the key performance specifications listed for the new device as its "reported device performance" and the corresponding values from the predicate device as an indication of the expected or "accepted" range for similar devices.
| Characteristic / "Acceptance Criteria" (Implied by Predicate) | Reported Device Performance (Glucose Pilot Blood Glucose Monitoring System) |
|---|---|
| Measurement Range | 20-600 mg/dL (1.1 to 33.3 mmol/L) |
| (Predicate Range) | (20 - 600 mg/dL, 1.1 to 33.3 mmol/L) |
| Test Time | 5 seconds |
| (Predicate Test Time) | (5 seconds) |
| Sample Volume | Minimum of 1 micro liter |
| (Predicate Sample Volume) | (Minimum of 1 micro liter) |
| Hematocrit Range | 30 - 55% |
| (Predicate Hematocrit Range) | (30 - 55%) |
| Operating Temperature Range | 10°-40°C (50°-104°F) |
| (Predicate Operating Temperature Range) | (6°-44°C, 43°-111°F) |
| Operating Humidity Range | 25-90% Relative Humidity |
| (Predicate Operating Humidity Range) | (10 – 90% Relative Humidity) |
| Memory Capabilities | Stores 350 of the most recent blood test results. |
| (Predicate Memory Capabilities) | (150 blood glucose and control solution tests) |
| Battery Life | Approximately 1000 glucose tests. |
| (Predicate Battery Life) | (1000 tests) |
Study Details
The document mentions "The information provided in this pre-market notification demonstrates that Glucose Plot [Pilot] Monitoring System is substantially equivalent to Ultra One Touch Blood Glucose Monitoring System. Substantial Equivalence was established through comparison of intended use and physical properties to the commercialized predicate devices. The information supplied in this pro-marketing notification provides reasonable assurance that the Glucose Pilot Blood Glucose Monitoring System is safe and effective for its stated intended use."
This indicates that the "study" demonstrating equivalence primarily involved analytical and performance testing to show the new device meets the same specifications and analytical performance characteristics as the predicate device. Details of a formal "clinical study" in the sense of a large-scale patient trial with statistical comparison of diagnostic accuracy against a gold standard are not provided in this summary.
2. Sample size used for the test set and the data provenance
- Sample size used for the test set: Not explicitly stated in this 510(k) summary. For glucose meters, accuracy studies typically involve a certain number of subjects across different glucose ranges.
- Data provenance: Not explicitly stated. For a 510(k) submission from a Chinese manufacturer, the testing could be done in China (country of origin for manufacturing) or potentially through contract labs elsewhere. The submission itself is dated from China with a US contact. It's safe to assume it's retrospective analytical performance data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- This information is not provided in the 510(k) summary. For blood glucose meters, the "ground truth" for comparative studies is typically established by laboratory reference methods (e.g., YSI analyzer) rather than expert consensus on diagnostic images.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- This information is not applicable/provided as the ground truth for blood glucose measurements is generally established by a laboratory reference instrument, not by human expert adjudication in the context of image interpretation.
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
- This information is not applicable/provided. The device is a Blood Glucose Monitoring System, which is an in vitro diagnostic device for quantitative measurement, not an AI-powered diagnostic imaging system requiring human reader performance analysis.
6. If a standalone (i.e., algorithm only without human-in-the loop performance) was done
- Yes, implicitly. The "standalone" performance for a blood glucose meter refers to its analytical accuracy and precision in measuring glucose concentrations, independent of human interpretation of a complex output. The specifications (measurement range, test time, sample volume, etc.) are characteristics of the device itself.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- For blood glucose monitoring systems, the ground truth is typically established using a laboratory reference method (e.g., a YSI STAT PLUS Glucose & Lactate Analyzer, hexokinase method, or similar highly accurate laboratory method) on plasma or whole blood samples. This document does not explicitly state the specific reference method used, but it would be a standard part of analytical performance testing.
8. The sample size for the training set
- This information is not applicable/provided in the context of this traditional electrochemical biosensor device. Blood glucose meters are not typically "trained" using machine learning algorithms in the same way an AI-driven image analysis system would be. Their performance is based on the chemical and electrochemical properties of the test strips and the meter's calibration algorithms.
9. How the ground truth for the training set was established
- This information is not applicable/provided for the reasons stated in point 8.
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