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The Cordis Nitinol Stent and Delivery System is intended for palliation of malignant neoplasms in the biliary tree.

The subject device is a system consisting of a self-expanding, open mesh, nitinol stent preloaded onto a sheathed delivery catheter. The delivery system is designed to deliver the stent to the stricture site via transhepatic access. Once positioned at the sheath is withdrawn and the stent is released. Upon release, the stent expands and conforms to the inner lumen of the biliary duct. The stent is designed to maintain patency of biliary ducts which have been obstructed by malignant neoplasms.

Device Acceptance Criteria and Study Details

1. Acceptance Criteria and Reported Device Performance

The provided document is a 510(k) Premarket Notification for the Cordis Nitinol Stent & Delivery System. A 510(k) submission primarily demonstrates substantial equivalence to a predicate device, rather than establishing specific performance acceptance criteria and then proving the device meets them through a direct clinical trial with predefined endpoints.

Therefore, the "acceptance criteria" here are implicitly centered around demonstrating that the Cordis Nitinol Stent & Delivery System is as safe and effective as legally marketed predicate devices for the palliation of malignant neoplasms in the biliary tree.

Table of Implicit Acceptance Criteria and Reported Performance:

• Comparable design and function.
• Comparable intended use.
• Comparable technological characteristics.
• Comparable safety profile.
• Comparable effectiveness for palliation of malignant neoplasms in the biliary tree. | Demonstrated Substantial Equivalence:
• The device has a tubular, open mesh design, similar to predicates.
• Intended for palliation of malignant neoplasms in the biliary tree, matching predicate indication.
• Self-expandable stent, while predicates are self- or balloon-expandable; both types "have been shown to be safe and effective."
• Constructed of biocompatible materials, as are predicates.
• Delivered percutaneously via a sheathed delivery system, similar to predicate delivery systems (sheathed or balloon). "Both system types have been shown to be safe and effective."
• Range of lengths and diameters comparable to predicates, intended to meet clinical need.
• "Preclinical testing was conducted which confirmed the safe and effective performance and biocompatibility of the subject device."
• "Comparative testing was also conducted which confirmed substantial equivalence of the subject and predicate devices." |
  | 2. Safety Profile (Preclinical):
• Biocompatibility.
• Mechanical integrity.
• Delivery system performance. | Confirmed Safety:
• Biocompatibility testing was conducted.
• "Preclinical testing was conducted which confirmed the safe and effective performance..." |
  | 3. Effectiveness (Preclinical):
• Ability to expand and maintain patency in simulated biliary ducts. | Confirmed Effectiveness:
• "...preclinical testing was conducted which confirmed the safe and effective performance..." |

Key takeaway: This 510(k) relies on demonstrating similarity to existing, legally marketed devices (predicates) rather than presenting new clinical acceptance criteria and data for novel claims.

2. Sample Size for Test Set and Data Provenance

The document does not describe a clinical study in human subjects for the Cordis Nitinol Stent & Delivery System that would involve a "test set" of patients.

Instead, the submission relies on:

• Preclinical testing: "Preclinical testing was conducted which confirmed the safe and effective performance and biocompatibility of the subject device."
• Comparative testing: "Comparative testing was also conducted which confirmed substantial equivalence of the subject and predicate devices."

The specifics of these preclinical and comparative tests (e.g., number of stents tested, type of models used) are not detailed in the provided summary. Therefore, there is no information on:

• Sample size used for a clinical test set.
• Data provenance (country of origin, retrospective/prospective), as no human clinical data is presented.

3. Number of Experts and Qualifications for Ground Truth

Since no human clinical study or data review (e.g., image interpretation) is described for the purpose of establishing a "ground truth" for a test set, there is no information about:

• The number of experts used.
• Their qualifications.

4. Adjudication Method for the Test Set

As no clinical test set or review requiring expert consensus is described, there is no information on an adjudication method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

An MRMC study is typically performed to evaluate the performance of human readers, often with and without AI assistance, on a set of cases.

The document does not describe an MRMC comparative effectiveness study. Its purpose is to demonstrate substantial equivalence based on device characteristics and preclinical performance, not to evaluate human reader performance or the impact of AI assistance.

6. Standalone Algorithm Performance

This device is a physical medical device (a stent and delivery system), not an algorithm or AI software. Therefore, the concept of "standalone (i.e., algorithm only without human-in-the-loop performance)" is not applicable to this submission.

7. Type of Ground Truth Used

Given that the submission focuses on preclinical testing and substantial equivalence, the "ground truth" refers to:

• Preclinical Test Results: Objective measurements of device performance, biocompatibility, and mechanical properties against engineering specifications or established standards.
• Predicate Device Performance: The established safety and effectiveness of the legally marketed predicate devices, inferred from their regulatory clearance and clinical use.

There is no mention of "expert consensus, pathology, or outcomes data" being directly used to establish ground truth for this device's performance in humans within this 510(k) summary.

8. Sample Size for the Training Set

The concept of a "training set" applies to machine learning algorithms. As this is a physical medical device, there is no training set in the context of an AI algorithm. The device development would involve engineering design, prototyping, and iterative testing, but not an algorithmic training set.

9. How Ground Truth for the Training Set Was Established

Again, since there is no AI algorithm or training set, this question is not applicable.

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