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510(k) Data Aggregation
(185 days)
The device is used for the kinetic quantitative determination of Urea Nitrogen (BUN) in serum.
The device test kit is comprised of two reagents, BUN Buffer (R1) and BUN Enzyme (R2), and a standard, 30mg/dL. A working reagent is prepared by combining five parts BUN Buffer (R1) to one part BUN Enzyme (R2).
This 510(k) summary describes an in-vitro diagnostic device (Urea Nitrogen (BUN) Liqui-UV®) and not an AI/ML powered medical device. Therefore, many of the requested categories (e.g., sample size for test set, number of experts, adjudication method, MRMC study, training set information) are not applicable.
Here's an interpretation of the available information in the context of your request:
Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance | Comments |
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Strong correlation with a predicate device. | Correlation coefficient of 0.999 when compared to Boehringer Mannheim's BUN, UV Urease/GLDH (Kinetic) method. | This indicates a very high degree of agreement between the new device and the established predicate device. |
Acceptable precision. | Results were found to be "acceptable." | Specific metrics for precision (e.g., CV%, standard deviation) are not provided, but the manufacturer deemed them acceptable. |
Acceptable linearity. | Results were found to be "acceptable." | Specific range and deviation from linearity are not provided, but the manufacturer deemed them acceptable. |
Acceptable sensitivity. | Results were found to be "acceptable." | Specific sensitivity values (e.g., limit of detection, lowest reportable value) are not provided, but the manufacturer deemed them acceptable. |
Acceptable stability. | Results were found to be "acceptable." | Specific stability period and conditions are not provided, but the manufacturer deemed them acceptable. |
Acceptable interference. | Results were found to be "acceptable." | Specific interferents and their impact are not provided, but the manufacturer deemed them acceptable. |
Regression equation close to ideal (y=x). | Regression equation of y = 1.012x - 0.47. | This equation is very close to the ideal y=x, indicating a strong linear relationship with a slight positive bias and a minor negative intercept. |
Study Details (as inferable from the given text)
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Sample size used for the test set and the data provenance: Not explicitly stated. The document mentions "method comparison" between the two test kits, implying a set of patient samples were tested concurrently on both devices. The country of origin and whether the data was retrospective or prospective are not mentioned.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. For IVD devices, "ground truth" is typically established by comparative analysis against a well-characterized predicate device or reference method, not by expert human interpretation in the same way as imaging or clinical decision support AI.
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Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable. Adjudication is generally not relevant for quantitative IVD device performance studies.
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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 is for an IVD device, not an AI/ML driven diagnostic.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: The device itself is a standalone in-vitro diagnostic reagent kit. Its performance is measured directly, not as an algorithm separate from human interaction in a diagnostic workflow.
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The type of ground truth used:
- Predicate Device Comparison: The primary "ground truth" or reference standard for demonstrating substantial equivalence was the performance of a legally marketed predicate device: Boehringer Mannheim's BUN, UV Urease/GLDH (Kinetic) method.
- Analytical Performance Standards: For precision, linearity, sensitivity, stability, and interference, the "ground truth" would be established analytical performance specifications or industry-accepted limits for these parameters.
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The sample size for the training set: Not applicable. Since this is an IVD reagent kit and not an AI/ML algorithm, there is no "training set" in the machine learning sense. The development of the kit involves formulation and optimization, not algorithmic training.
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How the ground truth for the training set was established: Not applicable, as there is no training set for an IVD reagent kit.
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