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510(k) Data Aggregation
(40 days)
The Cohen Crossover Catheter is intended to be used to deliver radiopaque media to selected sites in the vascular system, including the lower extremities using a contralateral approach.
The Cohen Crossover Catheters are angiographic catheters used to aid in percutaneous peripheral procedures. The catheters guide an introducer sheath across the illiac bifurcation and perform a standard angiogram. The catheters are available in three sizes (6 F, 7 F, and 8 F) and taper to a radiopaque, 5 F modified hook configuration on the distal end. Side holes are located at the distal end of the catheters for contrast media injections. The catheters are compatible with 0.035" guidewires.
Here's an analysis of the provided text regarding the Cohen Crossover Catheter, structured according to your request.
1. Table of Acceptance Criteria and Reported Device Performance
The provided text describes verification testing for a medical device (Cohen Crossover Catheter) rather than a software algorithm or AI. Therefore, the "acceptance criteria" are the successful completion of these engineering and safety tests, and the "reported device performance" is that the device met these criteria. There are no quantitative performance metrics (like sensitivity, specificity, etc.) that would typically be associated with AI/software performance.
| Acceptance Criterion (Test Type) | Reported Device Performance |
|---|---|
| Simulated anatomy/concomitant device use | Results of verification testing did not raise new safety or performance questions. |
| Straightener removal force | Results of verification testing did not raise new safety or performance questions. |
| Flow rate | Results of verification testing did not raise new safety or performance questions. |
| Liquid leak | Results of verification testing did not raise new safety or performance questions. |
| Dynamic/static high pressure rating | Results of verification testing did not raise new safety or performance questions. |
| Aspiration | Results of verification testing did not raise new safety or performance questions. |
| Tensile | Results of verification testing did not raise new safety or performance questions. |
| Torque | Results of verification testing did not raise new safety or performance questions. |
| Dimensional Verification | Results of verification testing did not raise new safety or performance questions. |
| Biocompatibility | Biomaterial assessments did not raise new safety or performance questions. |
2. Sample Size Used for the Test Set and Data Provenance
This document describes the premarket notification (510(k)) for a physical medical device (catheter), not an AI algorithm. Therefore, there isn't a "test set" in the context of data used to evaluate an AI model's performance. The "tests" mentioned are engineering and laboratory verification tests conducted on the physical device itself. The document does not specify the number of devices or components tested for each criterion, nor does it refer to data provenance in terms of country of origin or retrospective/prospective data collection as would be relevant for clinical data informing an AI.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable. This is for a physical device, not an AI or diagnostic algorithm, so there is no "ground truth" established by experts in the context of diagnostic accuracy. The "truth" for these engineering tests is defined by the test specifications and whether the device's physical properties meet them.
4. Adjudication Method for the Test Set
Not applicable. As this is not an AI or diagnostic algorithm evaluation requiring expert consensus on outputs, there is no adjudication method described.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
No. An MRMC study is relevant for evaluating the impact of an AI system on human reader performance for diagnostic tasks. This document concerns a physical device, not an AI.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
Not applicable. This is for a physical device. There is no algorithm to run in a standalone fashion.
7. The Type of Ground Truth Used
Not applicable in the context of AI. For the engineering tests, the "ground truth" would be established by the specifications of the device and standard engineering testing protocols (e.g., a specific tensile strength requirement, a specific flow rate, etc.).
8. The Sample Size for the Training Set
Not applicable. This is a physical medical device; there is no "training set" as understood in machine learning. Its design and manufacturing are based on engineering principles and existing predicate devices, not data-driven training.
9. How the Ground Truth for the Training Set Was Established
Not applicable. There is no training set for a physical medical device.
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