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The Ballast 088 Long Sheath is indicated for the introduction of interventional devices into the peripheral, coronary, and neuro vasculature.

The Ballast 088 Long Sheath is a sterile, single lumen, variable stiffness Guide Catheter / Long Sheath that is designed for facilitating the introduction of appropriately sized interventional devices into target blood vessels in the peripheral, coronary, and neuro vasculature. The Ballast 088 Long Sheath consists of a lubricous inner liner made from Polytetrafluoroethylene (PTFE) reinforced by a stainless steel coil over the inner liner and stainless steel braid partially covering the inner coil layer. The outer jacket consists of thermoplastics ranging from thermoplastic polyurethane (TPU), polyether block amide (Pebax), and polyamide 12. The distal section of the Ballast 088 Long Sheath is coated with hydrophilic coating to provide lubricity during use. A Platinum/Iridium marker band is placed at the distal end of the catheter for maximum radiopacity. An introducer sheath, 9F hemostasis valve, 8F hemostasis valve adapter and a dilator are also provided with the device.

This document is a 510(k) summary for the Ballast 088 Long Sheath, a medical device. It aims to demonstrate substantial equivalence to a predicate device, the Neuron MAX™ System. The document focuses on performance and biocompatibility testing.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Important Note: This document describes a medical device (a catheter) and its mechanical, material, and biological performance, not an AI/ML-based device. Therefore, many of the requested categories related to AI/ML (e.g., ground truth, experts, MRMC studies, training set data) are not applicable to this type of device submission. The acceptance criteria here are typically physical and biological performance metrics for a hardware device.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a formal "acceptance criteria" table with pass/fail thresholds for each test. Instead, it describes general test methods and states that "All test samples passed testing" or "The subject device is comparable to the predicate device." For biocompatibility, it states "Non-cytotoxic," "Did not elicit sensitization response," etc.

However, based on the "Performance Bench Testing and Animal Testing Summary" (Table 2) and "Biocompatibility Test Summary" (Table 3), we can infer the acceptance criteria and reported performance for key tests:

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

• Sample Sizes: The document consistently states "All test samples passed testing" for the bench tests. However, it does not specify the exact number of samples used for each bench test. For animal testing, it refers to an "acute porcine model" but does not give the number of animals. For the biocompatibility tests, specific ISO standards are cited, which typically define minimum sample sizes for such tests, but the numbers are not explicitly stated in this summary.
• Data Provenance:
  • Country of Origin: Not specified in the provided text. It's an FDA submission, implying testing acceptable to US regulatory standards, but the physical location of labs is not mentioned.
  • Retrospective or Prospective: These concepts are not applicable to the bench and animal testing described. These are controlled laboratory experiments and prospective animal studies, not data collected from human patients over time.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

Not Applicable. This is a hardware medical device. Ground truth, in the context of AI/ML, refers to human expert annotations or pathology for diagnostic accuracy. For this device, "ground truth" implicitly comes from physical measurements against engineering specifications and biological reactions governed by established scientific principles in highly controlled lab and animal environments (e.g., a specific tensile strength value must be met, a material must not induce a specific biological reaction). There is no "opinion-based" ground truth from human readers/experts in the way there would be for an AI-diagnostic algorithm.

4. Adjudication Method for the Test Set

Not Applicable. As above, this is for a hardware device. Adjudication methods (like 2+1, 3+1) are used to resolve discrepancies in human interpretations or diagnoses within AI/ML performance studies, which is not relevant here.

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 a physical medical device, not an AI/ML diagnostic aid. Therefore, no MRMC study with human readers assisting AI was conducted.

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

Not Applicable. This is a physical medical device. There is no algorithm to test in isolation.

7. The Type of Ground Truth Used

For this device, the "ground truth" is established by:

• Engineering Specifications/Standards: Each mechanical test (e.g., tensile strength, burst pressure, dimensions) has a defined acceptance range based on design specifications and relevant ISO or ASTM standards. "Passing" the test means meeting these predefined quantitative criteria.
• Biological Standards/Regulatory Guidelines: For biocompatibility, the ground truth is whether the device materials elicit specific biological responses (e.g., cytotoxicity, sensitization, irritation, genotoxicity, hemolysis) that fall within acceptable limits defined by ISO 10993 standards. "Non-cytotoxic," "Non-irritant," etc., are the "ground truth" outcomes for these tests.
• Comparative Performance to Predicate: For some tests (e.g., Torque Strength, Kink Resistance, Hemocompatibility), the "ground truth" for substantial equivalence is based on the subject device performing "comparable to" the already-cleared predicate device.
• Validated Protocols: The methods used (e.g., in vitro models, animal models) are validated scientific/engineering protocols providing the "truth" of the device's performance under specific conditions.

8. The Sample Size for the Training Set

Not Applicable. This is a hardware medical device. There is no "training set" in the context of machine learning. The design and manufacturing process are typically developed and refined through engineering iterations, not by "training" an algorithm on data.

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

Not Applicable. As there is no training set for an AI/ML model, this question is irrelevant to this device description.

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