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The AC3™ Intra-Aortic Balloon Pump is clinically indicated for use for the following conditions:

• · Acute Coronary Syndrome
• · Cardiac and Non-Cardiac Surgery
• · Complications of Heart Failure

The AC3™ Series IABP System provides counter-pulsation therapy to adult patients with impaired left ventricular (LV) function. It provides hemodynamic support of blood pressure and reduced cardiac work through volume displacement principles. The IABP is attached to an intra-aortic balloon catheter which is inserted into the femoral artery and positioned in the descending thoracic aorta.
The IABP delivers Helium (HE) into the IAB catheter during diastole to displace blood above and below the IAB, increasing blood pressure and perfusion to organs close to the IAB catheter. The IABP deflates or removes HE from the IAB catheter just prior to or in the early phase of systole, reducing the pressure in the aorta and therefore the pressure the LV must generate to open the aortic valve and eject its contents into the circulatory system. This results in a decrease in work and oxygen demand.
The AC3™ Series IABP System consists of two main components:

• . The pump control/display module which incorporates a touch screen and keypad for system operation, and
• The pneumatic drive module which is incorporated into the body of the device ●
  The AC3™ Series IABP is designed to be used with 30, 35, 40 and 50cc Intra-aortic balloons with the appropriate connectors.

This document is a 510(k) Premarket Notification from the FDA regarding the AC3 Series Intra-Aortic Balloon Pump (IABP). It focuses on demonstrating the substantial equivalence of an updated version of the device (with software V3.11) to its predicate device (with software V3.7).

Based on the provided text, the device is an Intra-Aortic Balloon Pump, and the submission is for a software update (V3.11). The acceptance criteria and study primarily focus on demonstrating that the updated software does not negatively impact the device's performance compared to the predicate.

Here's an analysis of the provided information against your request:

1. A table of acceptance criteria and the reported device performance

The document does not provide a specific table of quantitative acceptance criteria for the device's performance, nor does it present detailed quantitative performance results for the device. Instead, it states that:

• Software Verification testing has been completed to demonstrate that the software requirements have been met. All testing met the expected results.
• System verification and validation testing was also conducted, and all results met the required specifications.
• The results of the testing met the acceptance criteria and performed similar to the predicate device (AutoCAT2 and AC3 Ver.3.7).

The focus is on qualitative statements of compliance and similarity to the predicate, rather than numerical performance benchmarks. The "acceptance criteria" appear to be meeting the functional requirements of the updated software and ensuring it performs comparably to the previous version and predicate.

2. Sample size used for the test set and the data provenance

The document does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). The testing described appears to be internal verification and validation of software and hardware performance, not a clinical study on patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided. The study is a technical verification and validation of a medical device's software update, not a diagnostic AI system requiring expert-established ground truth from clinical images or data.

4. Adjudication method for the test set

This information is not provided, and it's not applicable to this type of device verification. Adjudication methods are typically used in studies involving human interpretation or labeling of data, which is not the primary focus here.

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 MRMC study was done. This medical device is an Intra-Aortic Balloon Pump (IABP), which is a therapeutic device, not a diagnostic AI intended to assist human readers in interpreting medical images or data. Therefore, this type of study is not relevant to this submission.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

This concept is not directly applicable in the sense of a diagnostic algorithm's standalone performance. The device itself (the IABP) operates automatically in "Autopilot Mode" based on its software. The "performance" here refers to the pump's ability to correctly deliver counter-pulsation therapy based on physiological signals, not an algorithm providing diagnostic output for human review. The software's independent functioning was verified: "Autopilot Mode, where most functions are automatically selected and controlled by the IABP."

7. The type of ground truth used

The concept of "ground truth" in the context of this submission refers to the correct expected behavior and output of the IABP system and its software. This would be derived from:

• Device specifications and requirements: The defined functional and performance requirements for the IABP and its software.
• Physiological models/simulations: In laboratory settings, the device would be tested against simulated physiological parameters (e.g., ECG signals, arterial pressure waveforms) to ensure it responds correctly.
• Comparison to predicate device: A key "ground truth" for this submission is that the updated device must perform "similar to the predicate device." The predicate's established safe and effective performance serves as a benchmark.

It's not "expert consensus," "pathology," or "outcomes data" in the typical sense applied to diagnostic tools.

8. The sample size for the training set

This information is not provided and is generally not relevant for 510(k) submissions for iterative software updates on established medical devices. The software development process likely involved internal development and testing cycles, but not typically a "training set" in the machine learning sense for an AI model.

9. How the ground truth for the training set was established

As there's no mention of a "training set" in the machine learning context, this information is not applicable. The "ground truth" for the device's functionality is established through engineering specifications, regulatory standards (like IEC 62304 for medical device software), and the performance of the predicate device.

In summary, this 510(k) submission primarily focuses on demonstrating that a software update to an existing Intra-Aortic Balloon Pump (IABP) system does not introduce new safety or effectiveness concerns and maintains substantial equivalence to its predicate device. The "study" referenced is internal software and system verification and validation testing, which confirmed that the updated device met its functional requirements and performed similarly to the predicate.

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