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The CardioQ-EDM series fluid management and cardiac output monitoring systems are designed to provide clinicians with real-time information about patients left ventricular blood flow and key hemodynamic parameters. The CardioQ-EDM series 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 CardioQ-EDM series monitors when used in Flow Monitoring Mode (esophageal Doppler) or Pressure Monitoring Mode (EDM+ only) are intended for use with adult and pediatric patients. When the CardioQ-EDM series monitors are used for High-Definition Impedance CardioGraphy with a PhysioFlow Q-Link module the intended use is for adult patients only.

The CardioQ-EDM series are medical instruments designed to monitor cardiac function and fluid status. The CardioQ-EDM & EDM+ achieve this by combining Doppler measurements of the blood flow (4MHz continuous wave ultrasound) to monitor and quantify the blood flow in the descending thoracic aorta. In addition to this the CardioQ-EDM+ previously introduced as an upgrade to the EDM under (K132139) allows additional Pulse Pressure Waveform Analysis (PPWA) through arterial blood pressure based parameters slaved from a high-end monitor.

The CardioQ-EDM (K111542) & Cardio EDM+ (K132139) have now been introduced with the addition of a USB Hub accessory and software modifications that allow the CardioQ-EDM series monitors to display hemodynamic parameters from the coupled together hemodynamic impedance cardiography device.

The addition of USB Hub accessory and software modifications allow the CardioQ-EDM series monitors to become a non-invasive cardiac output measurement system which displays hemodynamic parameters, allowing analysis of a trans-thoracic impedance cardiography signal & cardiac output monitoring. The only addition to the CardioQ-EDM & CardioQ-EDM+ is the enabling of HD-ICG functionality through the USB Hub accessory introduction and software modifications to display the hemodynamic parameters from the coupled hemodynamic impedance cardiography device.

The provided document is a 510(k) summary for the Deltex Medical CardioQ-EDM and Deltex Medical CardioQ-EDM+ device. It outlines the regulatory submission, device description, intended use, technological features, and a summary of completed testing.

However, the document does not contain any information about acceptance criteria or a study that specifically proves the device meets acceptance criteria related to its performance in terms of accuracy or efficacy, particularly concerning AI or algorithms. The submission focuses on substantial equivalence to predicate devices, primarily due to software modifications and the addition of a USB hub accessory to enable the display of hemodynamic parameters from a coupled impedance cardiography device.

Therefore, I cannot extract the detailed information requested in the prompt. The document explicitly states:

• "No Acoustic Output Testing has been conducted, because the CardioQ-EDM series monitors are only displaying data rather than performing any primary calculations are conducted by the hemodynamic impedance cardiography device coupled to the CardioQ-EDM series monitors."
• "No animal testing was conducted in support of this 510(k)."
• "No clinical testing was conducted in support of this 510(k)."

The "Bench Testing" mentioned is comparative and aims to show that "the software modifications and introduced USB Hub accessory have enabled additional HD-ICG functionality to be introduced to the existing CardioQ-EDM series monitors" and that "the software correctly displays the information received from the coupled hemodynamic impedance cardiography device without any manipulation of the coupled device." This is a functional verification, not a performance study against specific acceptance criteria for a new clinical claim or algorithm.

The document discusses "Letter-to-File Changes" for "Addition of Cardiac Power (CPO) and Cardiac Power Index (CPI)" and "Addition of Elastance (Ea) and Dynamic Arterial Elastance (Eadyn)," citing published papers to validate the use of these formulae. This indicates that the device displays these calculated parameters, but the 510(k) submission itself does not contain a study proving the device's performance in calculating or displaying these against acceptance criteria from a statistical clinical trial.

In summary, based on the provided text, the document does not contain the information required to answer your specific questions regarding acceptance criteria and performance studies for an AI/algorithm-driven device. The device described here is a display unit for parameters calculated by another coupled device and its assessment is for substantial equivalence relating to software and hardware changes enabling this display functionality.

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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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The 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 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 CardioQ-EDM+, is a medical instrument designed to monitor cardiac function and fluid status, providing clinicians with real-time information about a patient's left ventricular blood flow and key hemodynamic parameters in anesthetized, sedated or conscious patients in the operating room, intensive care unit, emergency room or ward. In addition, the CardioQ-EDM+ includes a function to calculate arterial blood pressure based parameters from an output slaved from a vital signs monitor. The CardioQ-EDM+ combines Doppler measurement of blood flow with Pulse Pressure Waveform Analysis (PPWA). In "Flow Monitoring Mode" the system employs esophageal Doppler monitoring (EDM) techniques using 4 MHz continuous wave ultrasound to monitor and quantify the blood flow in the descending thoracic aorta. displaying this data as a maximum velocity curve, a velocity spectrum and derived measurements. Real-time information about cardiac function, in particular left ventricular flow, is displayed continuously. For the CardioQ-EDM+, the newly added "Pressure Monitoring Mode" the system slaves the arterial blood pressure signal supplied by the hospital patient monitoring system to provide systolic and diastolic pressures and derived parameters. The CardioQ-EDM+ uses these classical blood pressure measurements to calculate Stroke Volume (SV), Cardiac Output (CO), Stroke Volume Variation (SVV). Pulse Pressure Variation (PPV) and a small number of derived parameters. The pressure derived stroke volume is calibrated from the CardioQ-EDM+'s Doppler ultrasound measurement of stroke volume ensuring consistency and allowing frequent recalibration.

Here's a breakdown of the acceptance criteria and study information for the Deltex Medical CardioQ-EDM+ device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not contain a specific table detailing acceptance criteria for the CardioQ-EDM+ device's performance in terms of accuracy, precision, sensitivity, or specificity. Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (CardioQ-EDM) by highlighting that the new device is a modification incorporating additional functionalities for blood pressure-based parameter calculation.

The performance comparison is implicitly made through the "Comparison technological features" table, which shows that the CardioQ-EDM+ offers the same ranges for directly measured Doppler parameters as the predicate device. The new "Pressure Monitoring Mode" adds new calculated parameters with specified ranges, but these are not compared against a defined acceptance criterion from a performance study within this document.

Implicit Performance Claim (based on substantial equivalence):
The CardioQ-EDM+ is designed to provide real-time information about a patient's left ventricular blood flow and key hemodynamic parameters, similar to the predicate device. The new pressure-based calculations are presented as additions to the existing capabilities, relying on the established performance of the Doppler measurement and the slaved arterial blood pressure signal.

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

• Sample Size:
  • Clinical Testing: The document explicitly states: "No clinical testing was conducted in support of this 510(k)." Therefore, there is no clinical test set sample size.
  • Bench Testing: The document mentions "Comparative bench testing," but does not provide details on the number of samples, cases, or specific data points used in this testing.
• Data Provenance: Not applicable for clinical data. For bench testing, the provenance is internal to Deltex Medical, as it was "Comparative bench testing... using a CardioQ-EDM Cardiac Output and Fluid Status Monitoring System (K111542)."

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

Since "No clinical testing was conducted in support of this 510(k)," there was no ground truth established by experts for a clinical test set.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set was used to establish performance against a ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Was it done? No. The document explicitly states: "No clinical testing was conducted in support of this 510(k)."
• Effect size of human readers improvement: Not applicable, as no such study was performed.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

While the device operates as a standalone monitoring system, the term "standalone study" typically refers to an evaluation of an AI algorithm's performance independent of human input or review. As this device is a medical instrument (hardware and integrated software) designed to measure physiological parameters rather than an AI-driven diagnostic or interpretative tool, this concept doesn't directly apply in the same way it would for, for instance, an AI-powered image analysis product.

The device's performance is demonstrated through its ability to measure and calculate parameters, and the validation of these calculations would be part of the bench testing. The 510(k) focuses on the "substantial equivalence" of the device, particularly the new features, to the predicate. The "Bench Testing" section confirms that the new device's performance is substantially equivalent to the predicate, implying that the algorithm's calculations were validated against expected outputs or the predicate's known outputs, but specific details of this validation are not provided.

7. Type of Ground Truth Used

• Clinical: Not applicable, as no clinical testing was performed.
• Bench Testing: The implicit ground truth for the bench testing would have been reference measurements or known values used to compare the CardioQ-EDM+ against the predicate CardioQ-EDM. The document states: "Comparative bench testing... supports the conclusion that the two systems have substantially equivalent performance." This implies that the CardioQ-EDM+'s outputs for new and existing parameters were compared against established and accepted values (e.g., from the predicate device or simulated physiological signals) during bench testing.

8. Sample Size for the Training Set

This device does not appear to use machine learning or AI in a way that requires a "training set" in the conventional sense (e.g., for image recognition or predictive modeling from a large dataset). Its algorithms are based on established physiological principles and signal processing for Doppler measurements and arterial pressure analysis. Therefore, there's no reported training set sample size.

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

Not applicable, as there is no apparent training set for machine learning. The algorithms are likely deterministic and based on validated mathematical models and physiological relationships.

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The 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 CardioQ-EDM's 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 sedated patients in the operating room, intensive care unit, emergency room or ward.

The CardioQ-EDM system employs esophageal Doppler techniques using 4 MHz continuous thoracic aorta, displaying this data as a maximum velocity curve, a descriting there is derived measurements. Thus, real-time information about verouty opesant in particular left ventricular flow, is displayed continuously. This is the same as for the predicate CardioQ K031706. CardioQ-EDM is an enhancement of the CardioQ K031706; obsolete The components have been replaced and an improved/modernized user interface created, by utilizing the full capabilities of the new components.

The provided text describes a 510(k) premarket notification for the Deltex Medical CardioQ-EDM Esophageal Doppler Monitor, aiming to demonstrate substantial equivalence to a predicate device (Deltex Medical CardioQ - K031706). Since it's a 510(k) submission, the "acceptance criteria" are implied by comparing the new device's performance to the legally marketed predicate device, rather than explicit numerical thresholds typically seen in AI/ML performance studies. The study described is a comparative bench study.

Here's the breakdown of the information requested based on the provided text, recognizing that it's a medical device submission focused on substantial equivalence rather than AI/ML performance:

1. Table of Acceptance Criteria and Reported Device Performance

For a 510(k) submission, acceptance criteria are generally to demonstrate that the new device (CardioQ-EDM) performs as safely and effectively as the predicate device (CardioQ K031706). The "reported device performance" is the conclusion of the bench testing that substantial equivalence was achieved. Specific numerical acceptance criteria are not explicitly stated for individual device parameters, but rather the overall conclusion of comparable performance across all listed characteristics.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: This was a bench testing study, therefore there is no "patient test set" in the traditional sense. The comparison was between the physical device (CardioQ-EDM) and the predicate device (CardioQ K031706) within a laboratory/bench environment. The text does not specify a "sample size" of individual tests or measurements, but rather refers to "Comparative bench testing of the CardioQ-EDM... using a CardioQ..."
• Data Provenance: The study was conducted by Deltex Medical Limited, based in the United Kingdom. The data is from bench testing, implicitly a prospective assessment of the new device's engineering and performance characteristics against the predicate's known specifications and performance.

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

• Not applicable. For this type of device (a diagnostic/monitoring tool) and study (bench testing for substantial equivalence), "expert ground truth" as understood in clinical evaluation or AI/ML performance is not relevant. The "ground truth" for the bench testing would be the engineering specifications and validated performance characteristics of the predicate device, against which the new device's measurements and functionalities were compared. This would be established by experts in device design, testing, and regulatory compliance, but not in the sense of clinical annotation.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• None. This concept is not applicable to bench testing where objective measurements and engineering specifications are compared. Discrepancies would be resolved through engineering re-evaluation or re-testing, not through a consensus-based adjudication of clinical observations.

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 study was NOT done. This submission is for a medical device (Esophageal Doppler Monitor), not an AI/ML diagnostic tool. Therefore, the concept of "human readers improve with AI vs without AI assistance" is not relevant. The device measures physiological parameters, and its performance is assessed against directly measured physical quantities or established specifications.

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

• Not applicable in the context of AI/ML algorithms. The CardioQ-EDM is a standalone medical device that provides direct measurements and calculated parameters. Its "standalone performance" refers to its ability to accurately measure and compute these physiological metrics as designed, compared to the predicate device. It's not an AI algorithm that processes data to generate a finding that a human then interprets or acts upon.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

• The "ground truth" for the bench testing was primarily the engineering specifications and established performance characteristics of the predicate device (CardioQ K031706), along with compliance with recognized consensus standards (IEC 60601 series for electrical safety/EMC, NEMA UD 2 for acoustic output). The goal was to show that the new device met or exceeded these, demonstrating substantial equivalence.

8. The sample size for the training set

• Not applicable. This submission is for a medical device that does not use machine learning algorithms, thus there is no "training set."

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

• Not applicable. As there is no training set, there is no ground truth established for it.

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