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THE MEDCOMP 14.5F DOUBLE LUMEN HEMODIALYSIS CATHETER IS INDICATED FOR USE IN ATTAINING LONG-TERM VASCULAR ACCESS FOR HEMODIALYSIS AND APHERESIS. IT MAY BE INSERTED PERCUTANEOUSLY AND IS PRIMARILY PLACED IN THE INTERNAL JUGULAR VEIN. ALTERNATE INSERTION SITES INCLUDE THE SUBCLAVIAN VEIN. CATHETERS GREATER THAN 40CM ARE INTENDED FOR FEMORAL VEIN INSERTION.

The Medcomp 14.5F Double Lumen Hemodialysis Catheter is a polyurethane, double lumen catheter used to remove and return blood through two-segregated lumen passages. Both lumens open at the distal tip, each with a series of side holes. The distal venous lumen is tapered and extends beyond the arterial lumen to reduce recirculation. The lumens are connected to the extensions via a molded hub. The arterial and venous extensions are identified by red and blue luer connectors and clamps. Priming volume information is printed on the clamps for ease in identification. A removable suture wing is provided for securing the catheter after initial placement. The fixed polyester cuff allows for tissue ingrowth for long term placement. The Medcomp 14.5F Double Lumen Hemodialysis Catheter is available in varied implantable lengths, with straight or pre-curved configurations to be determined by the prescribing physician based on insertion site and patient anatomy.

This document describes the Medcomp 14.5F Double Lumen Hemodialysis Catheter and its substantial equivalence to a predicate device (K981994 Bard Opti-Flow).

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:

• The document states that clinical studies were not deemed necessary. All performance data was generated through in vitro testing.
• The specific sample size (number of catheters tested) for each in vitro test is not explicitly stated in the provided document.
• The data provenance is in vitro laboratory testing, directly related to the manufactured device, and therefore not tied to a specific country of origin in terms of patient data. It is inherently prospective in the sense that the device was manufactured and then tested.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• This information is not applicable as clinical studies were not conducted, and the ground truth was not established by human experts for patient outcomes. The acceptance criteria were based on engineering and material science principles, against which the in vitro performance was compared.

4. Adjudication method for the test set:

• This information is not applicable as clinical studies were not conducted, and there was no expert adjudication process for patient cases.

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 performed. This device is a medical catheter and does not involve AI for diagnostic or interpretive purposes.

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

• No, this refers to an AI algorithm's performance, which is not applicable to this medical device.

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

• The "ground truth" for this device, in the context of its substantial equivalence determination, was based on established performance characteristics of legally marketed predicate devices (K981994 Bard Opti-Flow) and industry standards for catheter performance and material properties. This is typically determined through engineering specifications, previous regulatory approvals, and documented performance of equivalent devices.

8. The sample size for the training set:

• This information is not applicable as this device does not involve a machine learning algorithm that requires a training set.

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

• This information is not applicable as this device does not involve a machine learning algorithm that requires a training set.

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