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510(k) Data Aggregation
(93 days)
The St. Jude Medical Slittable Direct™ Outer Guide Catheter is designed for intracardiac access of the venous system of the heart and to serve as a conduit during implantation for the delivery of contrast medium and St. Jude Medical devices, including implantable left heart leads and delivery tools, and support of fluids where minimizing blood loss is essential. In addition, the outer guide catheters can work with inner catheters as a system.
The outer catheter is an introducer that is used to cannulate the Coronary Sinus (CS). The CPS Direct SL outer catheter will have 7 different curves configurations. The catheters will be available in 47 and 54cm working lengths and are radiopaque. The inner diameter of the sheath shall be PTFE lined and to ensure additional lubricity the inside and outside surfaces of the sheath shall be coated with a lubricious coating. The sheath shall have a proximal split away hub which interfaces with the hemostasis valve. The catheter functions as an outer catheter and can work with an inner guide catheter as a system.
This document describes a 510(k) premarket notification for the "CPS Direct™ SL Outer Catheter," a medical device. The submission focuses on demonstrating substantial equivalence to predicate devices rather than proving novel clinical effectiveness through extensive studies involving human subjects or AI algorithms. As such, many of the requested categories for AI-driven device studies are not applicable or cannot be extracted from the provided text.
Here is the information that can be extracted, acknowledging the limitations for a traditional medical device submission:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria (Implicit for Substantial Equivalence) | Reported Device Performance |
|---|---|
| Mechanical Performance: Similar to predicate devices. | Verification performed, meeting "all specified design and performance specifications." (Specific metrics or thresholds are not detailed in the provided text.) |
| Functional Performance: Similar to predicate devices. | Verification performed, meeting "all specified design and performance specifications." (Specific metrics or thresholds are not detailed in the provided text.) |
| Biocompatibility: Safe for patient tissue contact. | Biocompatibility testing performed on patient tissue-contacting materials, which "have been found to be biocompatible." (Specific test results or standards are not detailed in the provided text.) |
| Sterilization: Sterile for medical use. | Sterilized using a "validated Ethylene Oxide (EtO) sterilization process." (Validation results or specific methods are not detailed in the provided text.) |
| Materials: Similar to predicate devices. | Uses "similar technology,...materials" as predicate devices. (Specific materials are not itemized or compared in detail.) |
| Intended Use: Achieve stated purpose. | Designed for "intracardiac access of the venous system of the heart and to serve as a conduit during implantation for the delivery of contrast medium and St. Jude Medical devices, including implantable left heart leads and delivery tools, and support of fluids where minimizing blood loss is essential." Performance is deemed "substantially equivalent" to predicate devices, implying it achieves its intended use safely and effectively within that context. |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document mentions "Device comparison testing" and "Verification... which included mechanical, functional and biocompatibility testing." However, it does not specify sample sizes for these tests, nor the data provenance (e.g., country of origin, retrospective/prospective). These would typically be detailed in underlying test reports, not usually summarized in a 510(k) notification in this manner unless it's a clinical study. This is a medical device, not an AI or software device that processes data in the typical sense this question implies.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This question is not applicable to the type of device and submission described. The "ground truth" for this catheter's performance is established through engineering and biological testing (mechanical, functional, biocompatibility) against established specifications and predicate device equivalence, not through expert review of data like in an AI diagnostic device.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This question is not applicable. Adjudication methods like 2+1 or 3+1 are used in clinical studies or evaluations where expert consensus on outcomes or interpretations is required, typically for diagnostic or prognostic devices. This submission involves technical device testing.
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
This question is not applicable. This is a physical medical device (catheter), not an AI system. Therefore, no MRMC study, human reader improvement with AI, or effect size related to AI assistance would be performed or reported for this device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This question is not applicable. This is a physical medical device, not an algorithm or AI system.
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
The "ground truth" for this device's performance, in the context of the described studies, would be based on:
- Engineering specifications and standards: For mechanical and functional testing.
- Established biocompatibility standards: For biocompatibility testing.
- Validated sterilization methods: For sterilization validation.
- Performance of predicate devices: As a benchmark for "substantial equivalence."
It is not based on expert consensus, pathology, or outcomes data in the way these terms are typically used for diagnostic or AI-driven devices.
8. The sample size for the training set
This question is not applicable. This device does not involve a "training set" as it is not an AI or machine learning model.
9. How the ground truth for the training set was established
This question is not applicable. As there is no training set, there is no ground truth, as defined for AI models, that needs to be established for it.
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