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The FilterWire EZ™ Embolic Protection System is indicated for use as a guide wire and embolic protection system to contain and remove embolic material (thrombus/debris) while performing angioplasty and stenting procedures in coronary saphenous vein bypass grafts and carotid arteries. The diameter of the vessel at the site of filter loop placement should be between 2.25 mm and 5.5 mm for coronary saphenous vein bypass graft procedures and between 3.5 mm and 5.5 mm for carotid procedures.

The safety and effectiveness of this device as an embolic protection system has not been established in the cerebral vasculature, peripheral vessels other than carotid arteries, or in treating native coronaries, including acute myocardial infarction.

The Boston Scientific FilterWire EZ System (3.5 mm - 5.5 mm) is a temporary intra-vascular 0.014" guide wire filtration system that is placed distal to the vessel lesion to be treated by interventional procedures. The system consists of a Protection Wire, an EZ Delivery Sheath, an EZ Soft Tip Retrieval Sheath and accessories. A separately packaged EZ Bent Tip Retrieval Sheath is also available as an alternate tool for retrieving the FilterWire EZ Protection Wire. The 190 cm wire is compatible with the Boston Scientific extension wire (K970376 cleared June 6, 1997) for overthe-wire catheter exchanges.

The FilterWire EZ System is delivered via the EZ Delivery Sheath. Once the Protection Wire is across the lesion, the filter bag is expanded in the artery lumen by removing the EZ Delivery Sheath. After treating the lesion, all interventional devices are removed, and the EZ Soft Tip Retrieval Sheath or EZ Bent Tip Retrieval Sheath is advanced to the proximal end of the filter and the filter loop is retracted into the EZ Retrieval Sheath, trapping any emboli caught during the procedure. The EZ Retrieval Sheath and Protection Wire are then removed from the patient simultaneously.

This document describes the Boston Scientific FilterWire EZ™ Embolic Protection System.

Acceptance Criteria and Device Performance

Due to the nature of the device (a physical medical device for embolic protection), the provided 510(k) summary does not contain specific quantitative acceptance criteria or detailed device performance metrics in the format of a typical AI/software device. Instead, the document focuses on demonstrating substantial equivalence to predicate devices through various tests and a clinical trial.

Summary of Device Performance and Verification:

Note on Quantitative Data: The provided text does not offer numerical percentages, sensitivities, specificities, or other quantitative performance data often associated with acceptance criteria for AI/software devices. The "acceptance criteria" for this physical device are implicitly met by passing various engineering, biological, and clinical studies to demonstrate substantial equivalence to legally marketed predicate devices.

Study Information

The primary clinical evidence referenced is the CABERNET Trial.

1. Sample Size used for the test set and the data provenance:

   • Test Set Sample Size: Not explicitly stated in this 510(k) summary. The document mentions the CABERNET Trial's results were used to support the NexStent PMA (P050025). To obtain the exact sample size, one would need to refer to the PMA for the NexStent Carotid Stent.
   • Data Provenance: The study was a "carotid artery stenting clinical trial (CABERNET) sponsored by EndoTex™ Interventional Systems, Inc." This indicates it was a prospective clinical trial, likely involving multiple sites (though not explicitly stated) and would have predominantly included data from the country/countries where the trial was conducted. The specific country of origin is not mentioned.

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

   • This information is not provided in the 510(k) summary. For a clinical trial of a physical device, "ground truth" would typically be established through clinical outcomes, imaging assessments adjudicated by a clinical events committee (CEC), or other objective medical findings, rather than expert consensus on interpretive data (as would be common for AI/diagnostic software).

3. Adjudication method for the test set:

   • This information is not explicitly stated in the 510(k) summary. Clinical trials often employ a Clinical Events Committee (CEC) for endpoint adjudication, where multiple experts review cases. However, the specific method (e.g., 2+1, 3+1) is not detailed here.

4. 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 done. This device is a physical medical device (embolic protection system), not an AI-based diagnostic or assistive software. Therefore, the concept of "human readers improve with AI vs. without AI assistance" is not applicable.

5. 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 or software requiring a standalone performance evaluation.

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

   • For a clinical trial evaluating the safety and effectiveness of an embolic protection system, the "ground truth" would primarily be clinical outcomes data, such as rates of stroke, death, myocardial infarction, and other adverse events, as well as procedural success and device-related complications. Imaging data (e.g., degree of revascularization, evidence of embolization) would also contribute.

7. The sample size for the training set:

   • Not applicable. This device is a physical medical device undergoing a clinical trial for safety and effectiveness, not a machine learning model that requires a training set. The "in-vitro testing," "in-vivo testing," and "clinical evaluation" serve to validate the physical device.

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

   • Not applicable. As a physical device, there is no "training set" in the context of machine learning.

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