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The FloTrac sensor is indicated for use in intravascular pressure monitoring. It is also indicated for use with the Edwards arterial pressure based cardiac output monitoring devices or hardware to measure cardiac output. They are intended to be used in adult patients.

The FloTrac Jr sensor is indicated for use in intravascular pressure monitoring. It is also indicated for use with the Edwards arterial pressure based cardiac output monitoring devices or hardware to measure cardiac output. The FloTrac Jr sensor is indicated for use in pediatric patients ≥ 12 years of age.

The Acumen IQ sensor is indicated for use in intravascular pressure monitoring. It is also indicated for use with the Edwards arterial pressure based cardiac output monitoring devices or hardware to measure cardiac output. They are intended to be used in adult patients.

The VolumeView sensor is indicated for use in intravascular pressure monitoring. It is also indicated for use with the Edwards arterial pressure based cardiac output monitoring devices or hardware to measure cardiac output.

The FloTrac, Acumen IQ, and VolumeView sensors are constructed from two disposable pressure transducers that convert a physiological signal (or mechanical pressure) to an electrical signal that is transmitted through the cable to the patient monitor. The sensors have a straight, flow-through design in which fluid is passed directly across the pressure sensor. The sensors are comprised of a pressure sensitive silicon chip with two electrodes for excitation voltage and two electrodes for signal output. A polycarbonate housing with an integral stopcock at one end, and an integral flush device at the other end, encloses the sensors.

The provided FDA 510(k) summary for the FloTrac, FloTrac Jr, Acumen IQ, and VolumeView sensors does not contain detailed information about specific acceptance criteria and the study that proves the device meets those criteria in the typical format requested for an AI/ML-based medical device.

This document describes a substantial equivalence determination for extravascular blood pressure transducers (sensors) manufactured by Edwards Lifesciences, LLC. The core of the submission is that the subject devices are identical to the predicate devices in terms of intended use, indications for use, and technological characteristics, EXCEPT for changed pressure tubing and IV set component materials.

Therefore, the "study" described here is primarily focused on demonstrating that these material changes do not introduce new safety or effectiveness concerns, rather than validating an AI/ML algorithm's diagnostic performance against established ground truth.

Here's a breakdown based on the provided text, addressing your questions where possible, and noting where the information is not applicable or not present:

Overview of the Device and Study's Focus:

The devices in question are FloTrac, FloTrac Jr, Acumen IQ, and VolumeView sensors, which are intravascular pressure monitoring devices that also work with Edwards' arterial pressure-based cardiac output monitoring hardware. The 510(k) submission (K242909) is for modifications to these existing devices, specifically changes to the pressure tubing and IV set component materials. The premise of the submission is that these material changes do not alter the fundamental performance or safety in a way that would require new clinical performance studies typical for an AI/ML device.

1. Table of Acceptance Criteria and Reported Device Performance

The document states:

• "All testing met the existing predetermined acceptance criteria."
• "Based on the performance testing and the technological characteristics, the FloTrac sensors, Acumen IQ sensors, and VolumeView sensors meet the established performance criteria and are substantially equivalent to the predicate."

However, the specific quantitative acceptance criteria (e.g., accuracy +/- X mmHg, drift < Y%, etc.) and the reported numerical performance data from these tests are not provided in this summary document. The summary indicates that "Performance verification was performed in accordance with Edwards' current design requirements," and "shelf-life and sterilization validations have been performed to existing specifications." These are standard engineering and manufacturing performance metrics, not AI/ML performance metrics.

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

The document refers to "Performance verification" and "Biocompatibility testing." These generally involve lab-based testing (e.g., mechanical stress tests, pressure accuracy checks on a test bench, chemical compatibility tests) rather than patient data test sets in the context of an AI/ML algorithm.

• Sample Size for Test Set: Not specified. The nature of the "test set" here refers to physical product samples for engineering verification and manufacturing quality, not a dataset of patient measurements for AI validation.
• Data Provenance: The studies mentioned (biocompatibility, performance verification, shelf-life, sterilization) are likely conducted in a controlled lab/manufacturing environment, not directly on patient data. Therefore, questions of "country of origin" or "retrospective/prospective" data collection are not applicable in the context of this type of submission.

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

Given this is not an AI/ML submission related to diagnostic imaging or interpretation, the concept of "experts establishing ground truth" in the clinical sense is not applicable. The ground truth for this device's performance would be established through:

• Engineering specifications and testing standards for pressure transducers.
• Biocompatibility standards (e.g., ISO 10993-1).
• Sterilization validation protocols.
• Comparison to the performance of the predicate device.

4. Adjudication Method for the Test Set

Not Applicable. Adjudication methods like "2+1" or "3+1" are used in clinical studies, particularly for diagnostic interpretations where multiple readers assess cases. This submission is about the physical device's material change and its impact on manufacturing and engineering performance, not clinical interpretation.

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

No. An MRMC study is designed to assess the impact of an AI algorithm on human reader performance for tasks like diagnosis or detection. Since this submission is for material changes to a physical sensor, and there is no AI component described, an MRMC study was not performed and is not relevant.

6. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done

No. This question pertains to AI/ML algorithms. The device described (pressure sensor) does not have a standalone AI algorithm that would produce a diagnostic output. Its function is to convert a physiological signal to an electrical signal for a monitor.

7. The Type of Ground Truth Used

The "ground truth" for this submission is based on:

• Engineering specifications and validated testing methods: For pressure accuracy, electrical signal output fidelity, and physical integrity.
• International standards (e.g., ISO 10993-1): For biocompatibility.
• Defined shelf-life and sterilization parameters: Within established industry and regulatory norms.
• Comparison to the performance characteristics of the legally marketed predicate devices.

This is not ground truth derived from expert consensus, pathology, or outcomes data in the clinical sense, as it's not a diagnostic AI device.

8. The Sample Size for the Training Set

Not Applicable. Training sets are relevant for machine learning algorithms. This submission does not describe an AI/ML component or the training of such a component.

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

Not Applicable. As there's no mention of an AI/ML training set, the establishment of ground truth for it is irrelevant to this 510(k) summary.

In summary: This FDA 510(k) summary provides a high-level overview of a submission for material changes to existing medical devices (intravascular pressure sensors). The document focuses on demonstrating substantial equivalence by confirming that these material changes do not negatively impact the device's established performance, biocompatibility, shelf-life, or sterilization. It does not contain the detailed information typically associated with the validation of an AI/ML diagnostic algorithm, such as specific performance metrics, test set sizes for clinical data, expert panel ground truth adjudication, or MRMC study results.

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