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The EmmeBi Lepstep Central Venous Catheter Kit is intended for short term use as an aid for catheterization of the superior vena cava for the purpose of performing parenteral infusional or nutritional therapy and to withdraw blood samples where the peripheral venous system is not practicable.

The Emmebi Central Venous Catheter Kit is comprised of three main components; 1. Syringe/inserter 2. guidewire (J-Wire) and 3. central venous catheter. The catheter kit is intended for short term use as an aid to access the central venous system.

The Syringe/inserter simplifies the technique used by physicians to perform catheterization.

The guidewire (J-Wire) is designed to fit inside a percutaneous catheter for the purpose of directing the catheter being introduced.

The catheters are available in 14-> 22 gauge sizes with 4, 5, 6 and 7 Fr. diameter sizes in either a single, double or triple lumen. Circular lumen passages have soft flexible tapered tips. Lumens are connected to extensions containing hubs and catheter fixing wings. The lumens are printed with depth markings. Catheter markings are radiopaque.

The document describes a 510(k) premarket notification for the "Emmebi Central Venous Catheter Kit with Lestep® Introducer", and its substantial equivalence to predicate devices, rather than a study with specific acceptance criteria and detailed performance data often seen for AI/software devices. This device is a traditional medical device (catheter kit), not an AI/ML product.

Therefore, many of the requested elements for an AI/ML product study (like sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone performance for an algorithm, etc.) are not applicable to this submission.

However, I can extract the information relevant to the performance claims and the general nature of the "study" conducted for this type of device.

1. Table of Acceptance Criteria and Reported Device Performance

For this type of traditional medical device, "acceptance criteria" are typically defined by engineering specifications and standards to demonstrate equivalence to predicate devices. The document does not explicitly list numerical acceptance criteria in a table. Instead, it makes a general statement about performance.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a sample size for any clinical or in-vitro tests conducted. The "performance data" mentioned would typically involve in-vitro (benchtop) testing, not human subject data in the way an AI/ML product would have a "test set." No country of origin for data is provided, nor is it specified if data is retrospective or prospective, as these are not relevant for this type of device submission.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not Applicable. This is a hardware medical device. There is no concept of "ground truth" derived from expert consensus in the context of diagnostic accuracy for this device. Performance tests (e.g., tensile strength, leakage) are typically measured objectively against engineering specifications.

4. Adjudication Method

Not Applicable. No expert adjudication is described or required for this type of device submission.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. This is not an imaging or diagnostic AI/ML device, so an MRMC study is not relevant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Not Applicable. This is a physical medical device, not an algorithm.

7. The Type of Ground Truth Used

The concept of "ground truth" as used in AI/ML performance evaluation (e.g., pathology, clinical outcomes, expert consensus) is not applicable to this device. The "ground truth" for the performance of this device would be objective measurements against engineering specifications and comparisons to the known performance of predicate devices.

8. The Sample Size for the Training Set

Not Applicable. There is no "training set" as this is a physical device, not an AI/ML algorithm.

9. How the Ground Truth for the Training Set Was Established

Not Applicable. As there is no training set, this question is not relevant.

Summary of the "Study" for this Device:

The "study" referenced in this 510(k) submission is a demonstration of substantial equivalence to legally marketed predicate devices. This typically involves:

• Comparison of technological characteristics: Showing that the materials, design, intended use, manufacturing process, and sterilization method are similar to predicate devices.
• Performance Testing (Benchtop): Conducting a series of engineering/in-vitro tests (e.g., tensile strength, bond strength, bend test, air and liquid leakage) to ensure the device meets established safety and performance standards, and that these results are comparable to or better than predicate devices. These tests are conducted in a laboratory setting, not on human subjects.

The core argument is that, because the device's characteristics and performance are substantially equivalent to devices already on the market, it is equally safe and effective. The submission does not disclose raw data, specific test methodologies, or sample sizes for these internal engineering tests, but rather asserts that the data supports the claim of substantial equivalence.

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