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The PROTEK Duo 31 Fr. Veno-Venous Cannula Set is intended for use as a single cannula for both venous drainage and reinfusion of blood via an internal jugular vein during extracorporeal life support procedures.

The PROTEK Duo 31 Fr. Veno-Venous Cannula Set consists of two components: a 31 Fr. Dual lumen Veno-Venous Cannula and a 15.5 Fr. Introducer. The Introducer is designed to accept a standard 0.038 inch guidewire. The PROTEK Duo 31 Fr. Veno-Venous Cannula Set is intended as a single patient, single use, sterile device.

The PROTEK Duo 31 Fr. Veno-Venous Cannula (Figure 1) consists of two distinct lumens with a wire-reinforced cannula body. The inner lumen is located entirely within the outer lumen forming two concentric channels. The distal section (inner lumen/cannula) of the cannula body has six side holes near the distal tip opening. The proximal sections of each lumen are clear and not wire-reinforced to allow visualization of blood and to enable clamping to prevent blood flow during setup and removal of the cannula from the extracorporeal circulatory support equipment (see Figure 1). A non-vented barbed connector is affixed to both proximal ends (inner/distal and outer/proximal lumens) of the cannula and allow for connection of standard 3/8 inch blood circuit tubing for subsequent connection to extracorporeal circulatory support equipment.

The introducer (Figure 2) consists of a tube with a luer hub. The introducer fits inside the inner lumen of the cannula during insertion of the cannula/introducer assembly. The introducer is used to advance the cannula over a guidewire and facilitate cannula placement within the target vessel. The introducer has a luer hub at its proximal end to manage introducer insertion and removal from the cannula. The luer hub can also enable contrast injection, if necessary, to facilitate placement and final positioning of the cannula within the target vessel. The hemostasis cap minimizes blood loss when the cannula/introducer assembly is inserted into the target vessel. The introducer body is constructed from radiopaque material for visualization under fluoroscopy.

The provided document is a 510(k) premarket notification for a medical device called the PROTEK Duo 31 Fr. Veno-Venous Cannula Set. This document is a regulatory submission to the FDA, not a study publication. Therefore, it does not describe an AI/ML-based device or a clinical study in the typical sense for AI/ML validation.

The "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context refer to non-clinical (benchtop) tests performed to demonstrate the substantial equivalence of the new device (PROTEK Duo 31 Fr.) to a legally marketed predicate device (PROTEK Duo 29 Fr.). The device in question is a physical medical device (cannula), not a software algorithm.

Based on the provided information, here's a breakdown of the requested points as they apply to this document:

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

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 exact sample sizes for each benchtop test (e.g., how many cannulas were tested for hemolysis, tensile strength, etc.). It generally refers to "tests" and "all tests," implying multiple units were tested.
• Data Provenance: The tests are non-clinical, benchtop tests performed by the manufacturer, CardiacAssist, Inc., located in Pittsburgh, PA, USA. Therefore, the data provenance is from internal laboratory testing within the USA. It is prospective in the sense that the tests were performed specifically for this 510(k) submission.

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 question is not applicable to this document. The "ground truth" for these non-clinical tests is based on objective physical measurements and engineering specifications, not expert interpretation or clinical outcomes. For example, hemolysis is measured by laboratory methods, tensile strength by force gauges, and flow rates by fluid dynamics measurements. No human experts are used to establish ground truth in this context.

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

This question is not applicable to this document. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies where multiple human readers or experts interpret data (e.g., medical images) and their disagreements need resolution. For physical benchtop tests, results are typically quantitative and objectively measured, not subject to subjective adjudication.

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 question is not applicable to this document. This submission is for a physical medical device (cannula), not an AI/ML-based medical device. Therefore, no MRMC study, human readers, or AI assistance is involved.

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

This question is not applicable to this document. As stated, this is a physical medical device, not an algorithm.

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

As explained in point 3, the "ground truth" here refers to the actual physical properties and performance of the device under controlled benchtop conditions, measured objectively using established engineering and laboratory test methods. It's based on physical measurements against established specifications and comparison to a predicate device's performance, not clinical ground truth types like pathology or outcomes data.

8. The sample size for the training set

This question is not applicable. This submission is for a physical medical device. There is no AI/ML algorithm that requires a "training set." The device itself is manufactured; it does not "learn."

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

This question is not applicable. As there is no training set for an AI/ML algorithm, there is no ground truth to be established for it.

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