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The Stöckert Centrifugal Pump (SCP) is a cardiopulmonary bypass speed control device (21 CFR 870.4380) indicated for use exclusively with the COBE Revolution Pump Head, for speed-controlled pumping through the cardiopulmonary bypass circuit for typical durations of six hours or less.

The SCP has been qualified only for typical durations of six hours or less, appropriate to cardiopulmonary bypass procedures and has not been qualified through in vitro, in vivo, or clinical studies, for long term use as a bridge to transplant, pending recovery of the natural heart, or extracorporeal membrane oxygenation (ECMO) procedures.

The Stöckert Centrifugal Pump, developed to address the needs of the health care marketplace, consists of the following components:

• SCP Control Panel .
• SCP Drive Unit .
• Flow Probe .
• Emergency Drive Unit .

The SCP is designed to be used exclusively with the COBE® Revolution Disposable Pump Head and in connection with Stöckert S3 and SC Systems. The power supply voltage (24 V DC) as well as the CAN Bus connection is provided through a connection cable. The SCP utilizes the battery backup power from the S3/SC System Uninterruptible Power Supply in case of mains power failure.

1. Acceptance Criteria and Reported Device Performance

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

The provided 510(k) summary does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It generally refers to "functional acceptance testing," "electrical/mechanical safety (IEC60601-1)," "electromagnetic compatibility (IEC60601-1-2)," "software verification and validation," "ship testing," and "battery life." These are typically engineering and bench tests, not clinical studies involving patient data.

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

This information is not provided in the 510(k) summary. The performance testing focuses on technical and safety specifications rather than clinical evaluations requiring expert consensus on ground truth.

4. Adjudication Method for the Test Set

This information is not provided in the 510(k) summary. Given the nature of the tests mentioned (functional, electrical/mechanical safety), an adjudication method as typically understood for clinical data (e.g., 2+1) would not be applicable.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not done. The submission focuses on demonstrating substantial equivalence to predicate devices through technical specifications and performance testing, not on measuring the improvement of human readers with AI assistance.

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

Yes, the performance testing outlined (functional acceptance testing, electrical/mechanical safety, etc.) can be considered standalone testing of the device's capabilities without human intervention beyond operating the device for the tests. The device is a "speed control device" for a pump, implying its core function is autonomous control based on its programming and sensors.

7. The Type of Ground Truth Used

The ground truth for the performance testing would be established by the functional specifications and engineering standards for each test (e.g., expected RPM, current draw, flow rate). For instance, for RPM accuracy, the ground truth would be the actual RPM measured by highly accurate instruments. For safety tests, the ground truth would be compliance with relevant IEC standards.

8. The Sample Size for the Training Set

The concept of a "training set" is not applicable to this device as it is not an AI/machine learning product that learns from data. It is a control device for a mechanical pump, and its functionality is based on fixed algorithms and hardware.

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

As there is no training set in the context of AI/ML, this question is not applicable. The device's operation is predetermined by its design and programming, rather than learned from a dataset.

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