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The Floppy and Intermediate Extra Long Guide Wires are intended for use to facilitate the placement of PTA and/or PTCA balloon dilatation catheters within the peripheral or coronary vasculature. The guide wires are designed for safe use with appropriately sized balloon catheters. The guide wires are not intended for use in the cerebral vasculature.

The Floppy (2) and Intermediate (2) Extra Long Guide Wires have a nominal outside diameter of 0.014 inch measured at the distal spring coil. The guide wires are 300 cm (nominal) in length and have a distally attached 30 cm composite spring coil. For the 30 cm radiopaque guide wires, the entire spring coil is made of platinum/tungsten alloy. The spring coil of the 4 cm radiopaque guide wires is proximally made of stainless steel wire (26 cm) and has a distal platinum/tungsten alloy spring coil for opacity. For all guide wires, the core wire and spring coils, up to and including the tip, are coated with phosphoryIcholine polymer.

The provided 510(k) summary (K9622dd6) describes the device 0.014" Floppy Extra Long Guide Wire with Phosphorylcholine (PC) Polymer Coating and 30 cm Radiopaque Tip (and variants). However, it explicitly states that "Bench performance and biocompatibity testing was not performed on the subject quide wires". Instead, it refers to prior testing on predicate devices. Therefore, the requested information about the study proving the subject device meets acceptance criteria cannot be fully provided as no such study was conducted for this specific submission.

However, based on the information provided regarding the predicate device, here is a breakdown as requested:

1. Table of Acceptance Criteria and Reported Device Performance (for the Predicate Device):

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

• Sample Size: Not explicitly stated for each test, but implied to be sufficient for a 510(k) submission based on FDA guidelines.
• Data Provenance: Not explicitly stated, but the tests were "conducted according to the guidelines presented in FDA's January 1995 Coronary and Cerebrovascular Guidewire Guidance." This suggests the tests were likely conducted by the manufacturer or a contract lab. The country of origin is not specified but given the FDA guidance, it's presumed to be relevant to US regulatory standards. The testing was retrospective relative to the current submission, as it was performed on "predicate devices."

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

This is not applicable as the testing involves objective physical and biological properties of the device, not expert interpretation of outputs like in image analysis.

4. Adjudication method for the test set:

Not applicable for this type of objective performance and biocompatibility testing. The results are typically quantitative or qualitative (e.g., pass/fail relative to a defined standard).

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 device is a medical guide wire, not an AI-powered diagnostic tool requiring human reader studies.

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

Not applicable. This is a physical medical device.

7. The type of ground truth used:

The ground truth for the predicate device testing was based on:

• Established engineering specifications and test methods for physical properties (e.g., tensile strength, torque).
• Standardized biocompatibility tests with defined pass/fail criteria (e.g., ISO standards for cytotoxicity, pyrogenicity).
• Compliance with "FDA's January 1995 Coronary and Cerebrovascular Guidewire Guidance."

8. The sample size for the training set:

Not applicable. This section relates to the predicate device performance testing, not a machine learning model.

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

Not applicable. This section relates to the predicate device performance testing, not a machine learning model.

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