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The FlexCath Cross™ Transseptal Solution is indicated to puncture the interatrial septum to gain access to the left side of the heart whereby various cardiovascular catheters are introduced.

FlexCath Cross™ combines the conventional vessel dilator and transseptal needle into a single device (Figure 1). FlexCath Cross" consists of an elongated shaft with a tapered tip and central lumen to track over a guidewire. The lumen of FlexCath Cross™ is fitted with a hollow stainless steel transseptal needle (Figure 2). Both the shaft and needle are connected to the proximal handle of FlexCath Cross™. The needle is affixed to a springtensioned actuator in the handle of FlexCath Cross™ that prevents needle extension until the operator purposely advances the needle via a slider button located on the outer surface of the handle. The proximal handle is fitted with a Luer connector to gain access to the central lumen of the needle. The handle is also fitted with an electrical connector that allows for monitoring intracardiac electrograms (EGMs) from the needle while in the heart utilizing the EGM adapter cable, and/or allows for the application of radiofrequency (RF) current from an electrosurgical generator to facilitate the septal puncture utilizing the ES adapter cable. FlexCath Cross™ is for single-use only and is provided sterile.

Due to the nature of the device described in the provided text (a catheter introducer - FlexCath Cross™ Transseptal Solution), the acceptance criteria and study detailed are primarily focused on engineering performance and material safety rather than diagnostic accuracy or clinical outcomes in the way often associated with AI/ML-based medical devices. Therefore, many of the typical questions for AI/ML device studies (like those related to expert adjudication of ground truth, effect size with AI assistance, or distinction between training/test sets for algorithm performance) are not applicable here.

The document describes the device, its intended use, and compares it to a predicate device to establish substantial equivalence. The core of the acceptance criteria is demonstrating that the modified device performs as safely and effectively as the predicate device.

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

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

The document does not explicitly present a table of "acceptance criteria" with precise numerical thresholds for each test. Instead, it states that "All necessary bench testing was conducted on the additional models FlexCath Cross™ to support a determination of substantial equivalence to the predicate device" and that the "collective bench testing demonstrates that the proposed device does not raise different questions of safety or effectiveness when compared to the predicate device."

The tests performed and their general outcomes (implied to be successful for showing substantial equivalence) are:

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

The document refers to "bench testing" and "non-clinical testing." This typically involves testing of physical samples of the device in a laboratory setting. No sample sizes for specific tests are provided, nor is information about country of origin or retrospective/prospective nature as this pertains to clinical data, which is not the focus of this submission. The tests are for engineering performance, not clinical performance based on patient data.

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)

Not applicable. This device is not an AI/ML diagnostic or prognostic device that requires expert-established ground truth from clinical cases. The "ground truth" for engineering tests is typically based on predefined specifications and standards.

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

Not applicable for this type of device and testing. Adjudication methods are relevant for subjective interpretations of clinical data, especially in AI/ML performance evaluations.

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

Not applicable. This is not an AI-assisted diagnostic device, and therefore, no MRMC studies or effect size measurements for human readers with AI assistance would be performed or relevant.

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

Not applicable. This is not an algorithm-based device.

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

The "ground truth" for the non-clinical and bench testing is the device's conformance to established engineering specifications, industry standards (e.g., ISO for sterilization, AAMI for biocompatibility, IEC for electrical safety), and its performance being equivalent to the predicate device.

8. The sample size for the training set

Not applicable. There is no "training set" in the context of this device as it is not an AI/ML product.

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

Not applicable for a non-AI/ML device.

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