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The AFFINITY® 20 µ Arterial Blood Filter with Trillium™ Biosurface is indicated for use in cardiopulmonary bypass procedures up to 6 hours in duration for the removal of particulate and gaseous microemboli.

The AFFINITY® 20 µ Arterial Blood Filter with Trillium™ Biosurface is a single use device designed to filter microemboli from the blood in the extracorporeal circuits during cardiopulmonary bypass surgery. The AFFINITY 20 µ Arterial Blood Filter with Trillium" Biosurface is coated with a nonleaching biosurface.

This document describes a Special 510(k) submission for a modification to the Medtronic AFFINITY® 20 µ Arterial Blood Filter. The modification involves coating the blood contact surfaces of the filter with Trillium™ Biosurface. The submission aims to demonstrate substantial equivalence to legally marketed predicate devices.

Here's the breakdown of the requested information based on the provided text:

1. A table of acceptance criteria and the reported device performance

The provided text does not explicitly state quantitative acceptance criteria or detailed reported device performance in a tabular format as would typically be found in a clinical study report. It states that "biocompatibility and in vitro bench testing demonstrated that when compared to the predicate devices, the AFFINITY® 20 µ Arterial Blood Filter with Trillium™ Biosurface does not significantly affect safety and effectiveness and are substantially equivalent to other commercially distributed arterial filters."

The types of tests performed included:

• Coating Characteristics
• Physical Characteristics
• Performance Characteristics

Without specific numerical targets for these characteristics and the corresponding results, a detailed table cannot be created. The acceptance criteria essentially appear to be "not significantly affecting safety and effectiveness" and "substantially equivalent" to the predicate devices based on these categories of testing.

2. Sample sizes used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document primarily describes in vitro bench testing rather than a study involving human or animal subjects that would typically have a "test set" in the context of device performance in a clinical scenario. Therefore, information about human subject sample size, country of origin, or retrospective/prospective nature of the data is not applicable in the context of this submission. The tests were likely conducted on manufactured device units.

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)

This section is not applicable. The submission relies on "biocompatibility and in vitro bench testing" to demonstrate substantial equivalence. These tests typically involve laboratory measurements and comparisons to engineering specifications or predicate device performance, not expert-established ground truth in the way clinical diagnostic studies would.

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

This section is not applicable. As mentioned above, the evaluation is based on in vitro bench testing, not on clinical assessments that would require adjudication of findings.

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

There is no mention of an MRMC comparative effectiveness study, human readers, or AI assistance in the provided text. This device is a medical filter, not a diagnostic imaging or AI-driven system.

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

This section is not applicable. The device is a physical filter, not an algorithm.

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

The concept of "ground truth" as typically used in diagnostic or AI studies is not directly applicable here. For the in vitro bench testing, the "ground truth" refers to established scientific methods and engineering specifications for device performance, as well as the observed performance of the predicate devices. The aim was to show that the modified device's characteristics (coating, physical, and performance) were comparable to the predicate device and met scientific standards.

8. The sample size for the training set

This section is not applicable. There is no mention of a "training set" as this is not a machine learning or AI-driven device.

9. How the ground truth for the training set was established

This section is not applicable for the same reasons as #8.

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