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The Deltex Medical CardioQ-EDM+ / Deltex Medical CardioQ-EDM cardiac function and fluid status monitoring system is designed to provide clinicians with real-time information about a patient's left ventricular blood flow and key hemodynamic parameters. The Deltex Medical CardioQ-EDM+ / Deltex Medical CardioQ-EDM beat-to-beat data on cardiovascular status can be used by the managing clinician to evaluate and optimize hemodynamic performance in anesthetized, sedated or conscious patients in the operating room, intensive care unit, emergency room or ward.

The Deltex Medical CardioQ-EDM series are medical instruments designed to monitor cardiac function and fluid status. The CardioQ-EDM series combine Doppler measurement of the blood flow (4MHz continuous wave ultrasound) with Pulse Pressure Waveform Analysis (PPWA) to monitor and quantify the blood flow in the descending thoracic aorta and hence calculate other clinically significant information.

The provided document is a 510(k) premarket notification for the Deltex Medical CardioQ-EDM+ / Deltex Medical CardioQ-EDM, a cardiovascular blood flowmeter. The submission focuses on minor software changes to extend the range of volumetric calculations and accommodate future probe additions, rather than a new standalone device with extensive clinical studies. Therefore, much of the requested information regarding detailed acceptance criteria, specific study designs, or human-in-the-loop performance is not present.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a "test set" in the context of patient data. The testing described focuses on software functionality.

• Test Set Sample Size: Not applicable in the context of patient data. Testing involved simulation and code examination concerning formula selection and calculation.
• Data Provenance: Not applicable, as no patient data was used for this particular submission. The changes are software-based.

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

Not applicable. Ground truth for the software's functionality was established through internal code examination and "by hand" calculations during simulation testing. No external experts or medical professionals were mentioned for this specific validation.

4. Adjudication method for the test set

Not applicable. The validation was based on software implementation and calculation accuracy, not on expert adjudication of clinical data.

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. This document describes a software update for a medical device that measures hemodynamic parameters, not an AI or imaging diagnostic tool. Therefore, an MRMC study comparing human reader performance with and without AI assistance is not relevant or mentioned.

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

Yes, in a way. The testing focused on the algorithm's functional correctness ("software correctly implements the formula and algorithms" and "values displayed match those calculated 'by hand'"). However, this is distinct from "standalone performance" in the context of a diagnostic AI algorithm. Here, it refers to the accuracy of the device's internal calculations.

7. The type of ground truth used

For the specific software changes:

• Ground Truth: Verification that the software's internal logic for probe identification, formula selection, and volumetric calculations aligns with predefined expectations and manual calculations. This assumes the underlying physiological models and formulas used are already well-established.

8. The sample size for the training set

Not applicable. This submission concerns a software update to an existing device, primarily focused on formula selection and calculation. It does not involve machine learning or AI models that require a "training set" of data.

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

Not applicable, as there was no training set for a machine learning algorithm.

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