Search Results

The St. Jude Medical CPS Aim ™ Universal slittable inner catheter (subselector/cannulator) is designed for intracardiac access of the coronary sinus and subselection of the venous system of the heart, and to serve as a conduit during implantation for delivery of contrast medium and St. Jude Medical devices (such as guidewires and implantable left heart leads). In addition the CPS Aim Universal slittable inner catheter (subselector/cannulator) can work with outer guide catheters as a system

The CPS Aim™ Universal slittable inner catheter (subselector/cannulator) is used to facilitate left heart lead delivery procedures. It is an introducer that is used to cannulate the coronary venous system and act as a conduit for contrast medium. implantable coronary leads, or other devices. The CPS Aim™ Universal slittable inner catheters will be available in the same working lengths, 59 and 65 cm as the predicate and will be available with the same number of curves as the predicate. The key design features of the CPS Aim™ Universal slittable inner catheters have only undergone a minimal change to increase the inner diameter of the catheter and the accessories have not changed. The key design features are listed below:

• . Braid reinforced, varying durometer PEBAX shaft with molded proximal hub.
• Inner diameter of the catheter is PTFE lined .
• Atraumatic distal soft tip.
• . Embedded marker band on soft tip for fluoroscopic visibility.
• Outside surface of the catheter shaft is coated with a hydrophilic coating to . provide lubricity during use.
• . Hub of the catheter is fitted with a retention cap and an integrated valve that includes a sideport assembly with 3-way stopcock.
• The catheters are available in cannulator and sub-selector models. .
• Accessories such as VBT used to assist the insertion of SJM Devices . (leads, guidewires, inner catheters, etc)

The provided text describes a 510(k) submission for a medical device, the CPS Aim™ Universal slittable inner catheter. This is a traditional 510(k) for a device with only minimal changes compared to a predicate device. Therefore, the information provided focuses on demonstrating substantial equivalence rather than a detailed performance study as would be expected for a novel AI/ML device.

Based on the provided text, here's a breakdown of the requested information, noting where specific details are not available due to the nature of this type of submission:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not detail specific quantitative acceptance criteria or a comprehensive table of performance metrics. Instead, it indicates that "completion of all verification and validation activities demonstrated that the candidate devices meet their predetermined design and performance specifications." The primary performance "acceptance criterion" appears to be demonstrating substantial equivalence to the predicate device.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a "test set" in the context of an AI/ML study, but rather refers to non-clinical verification and validation activities. The non-clinical tests would have involved various physical and mechanical tests on samples of the device. The specific sample sizes for these individual tests are not provided. Data provenance (country of origin, retrospective/prospective) is not applicable in this context as it refers to data used for AI/ML model training or evaluation, not for physical device testing.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This is not applicable. The "ground truth" for this medical device submission would be the device itself conforming to its design specifications and performing as intended in non-clinical tests. There are no "experts" in the AI/ML sense establishing ground truth for image interpretation or diagnosis.

4. Adjudication Method for the Test Set

This is not applicable for a physical device submission. Adjudication methods are typically used in AI/ML performance studies to resolve discrepancies in expert annotations or interpretations.

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

No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI/ML diagnostic or prognostic devices where human readers (e.g., radiologists, pathologists) interpret cases with and without AI assistance. This submission is for a physical medical catheter.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

No, a standalone algorithm performance study was not done. This is not an AI/ML device.

7. The Type of Ground Truth Used

For this device, the "ground truth" is defined by the device's adherence to its engineering specifications and its performance in various non-clinical (e.g., mechanical, material, functional) tests. This is not pathology, outcomes data, or expert consensus in the diagnostic sense.

8. The Sample Size for the Training Set

This is not applicable. There is no AI/ML model for which a "training set" would be used. The "training" for a physical medical device involves its design, manufacturing, and internal quality control processes.

9. How the Ground Truth for the Training Set Was Established

This is not applicable. As there is no AI/ML model, there is no training set or ground truth established in that context.

Ask a specific question about this device