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510(k) Data Aggregation
(15 days)
PRECISION XTRA PLUS OR OPTIUM PLUS, BLOOD GLUCOSE TEST STRIPS
The Precision Xtra / MediSense Optium / Precision Easy / MediSense Optium Easy Blood Glucose Test Strip is intended for outside-of-the-body (in-vitro diagnostic) use. The strip is indicated for the quantitative measurement of glucose in fresh capillary blood for self-testing by lay users (e.g., from the finger, forearm, upper arm or base of thumb), or by health care professionals. The test strip is to be used for monitoring blood glucose concentrations in persons with diabetes and other conditions.
The Precision Xtra™ Advanced Diabetes Management System utilizes amperometric biosensor technology to generate a current. The size of the current is proportional to the amount of glucose present in the sample, providing a quantitative measure of glucose in whole blood and control solutions.
The provided text describes a 510(k) submission for Abbott Laboratories' MediSense Optium Plus and Precision Xtra Plus Blood Glucose Test Strips. This submission is for a modified version of an already cleared device, indicating an updated or improved iteration. Therefore, the "acceptance criteria" and "device performance" are framed in the context of demonstrating substantial equivalence to the predicate device rather than meeting specific performance thresholds against a gold standard in a standalone study.
Here's an analysis of the requested information based on the provided text:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Substantial equivalence to the predicate device for blood glucose measurement. | "The studies demonstrated that lay users can obtain blood glucose results that are substantially equivalent to the current methods for blood glucose measurements." |
| "Results of laboratory testing demonstrate that the performance of the Precision Xtra Diabetes Test Strip is acceptable and comparable to the performance of the predicate device for blood glucose testing, when used according to its intended use." | |
| Performance for quantitative measurement of glucose in fresh whole blood. | The performance studies were conducted to evaluate the device's ability to quantitatively measure glucose in whole blood. The conclusion states performance is "acceptable and comparable to the predicate device." |
| Capability for self-testing by lay users. | "The studies demonstrated that lay users can obtain blood glucose results that are substantially equivalent to the current methods for blood glucose measurements." The intended use explicitly states for "self-testing by lay users." |
| Capability for use by health care professionals. | The intended use explicitly states for "health care professionals." Although the specific statement above focuses on lay users, the overall conclusion of "acceptable and comparable" performance under intended use implies this capability. |
| Consistency with existing methods for blood glucose measurement. | "The studies demonstrated that lay users can obtain blood glucose results that are substantially equivalent to the current methods for blood glucose measurements." This refers to established clinical blood glucose testing methods, which would serve as the comparator for assessing equivalence. |
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: The document does not specify the sample size used for the performance studies.
- Data Provenance: The document does not specify the country of origin of the data or whether the studies were retrospective or prospective. It only states that "The performance of the strips using various diabetes monitoring systems was studied in the laboratory."
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)
- The concept of "experts" establishing ground truth in the context of blood glucose test strips is not directly applicable in the same way it would be for image analysis or diagnostic decision-making. For these devices, the "ground truth" would typically refer to a reference method of blood glucose measurement (e.g., a laboratory analyzer).
- The document does not provide details on specific experts or their qualifications for establishing any reference values. It refers to "current methods for blood glucose measurements" as the comparator.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Adjudication methods like 2+1 or 3+1 are typically used when multiple human readers are interpreting data, and their decisions need to be reconciled. This is not mentioned or relevant for the type of performance study described for blood glucose test strips, which would likely involve direct comparison to a reference analytical 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
- No, an MRMC comparative effectiveness study was not done. This type of study is not relevant for blood glucose test strips, which do not involve human "readers" interpreting results in the same way as, for example, radiologists interpreting medical images. The device itself performs the measurement.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Yes, performance studies are inherently "standalone" in the sense that the device's analytical performance (how well it measures glucose) is evaluated. The phrase "algorithm only without human-in-the-loop performance" isn't directly applicable as there isn't a complex diagnostic "algorithm" in the AI sense, but rather a chemical reaction and electrochemical detection process. The studies focused on the performance of the strips when used with various diabetes monitoring systems. The core evaluation is the device's ability to produce accurate glucose readings.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- The ground truth would be established by a reference method for blood glucose measurement, typically a laboratory-grade analyzer (e.g., a YSI analyzer or similar hospital-grade instrument) that is considered highly accurate and precise. The document refers to "current methods for blood glucose measurements" as the benchmark for substantial equivalence.
8. The sample size for the training set
- The document does not mention a "training set" in the context of a statistical model or algorithm development. For a chemical-based test strip, "training" isn't typically applicable in this way. The performance studies evaluate the final product.
9. How the ground truth for the training set was established
- As there is no mention of a "training set" in the context of algorithm development, this question is not applicable. For quality control and manufacturing, the strips would be tested against known glucose concentrations to ensure they meet specifications, but this is part of manufacturing quality, not a "training set" for an AI algorithm.
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