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The Vigileo™ APCO/Oximetry Monitor is indicated for continuously measuring hemodynamic parameters such as cardiac output and oximetry to assess oxygen delivery and consumption. When connected to an Edwards oximetry catheter, the monitor measures oximetry in adults and pediatrics. The monitor also displays parameters, such as stroke volume and stroke volume variation, used to assess fluid status and vascular resistance. The Vigileo™ APCO/Oximetry Monitor may be used in all settings in which critical care is provided.

The Vigileo™ Arterial Pressure Cardiac Output (APCO)/Oximetry Monitor (Vigileo™ Monitor) is a microprocessor-based instrument. When used with an Edwards FloTrac sensor, the Vigileo™ Monitor is capable of continuously monitoring the following parameters: Cardiac Output (CO); Cardiac Index (CI): Stroke Volume (SV): Stroke Volume Variation (SVV); Systemic Vascular Resistance (SVR); Systemic Vascular Resistance Index (SVRI); Oxygen Delivery (DO2); and Oxygen Delivery Index (DO2I). When used with Edwards Oximetry catheters, the Vigileo™ Monitor is capable of continuously monitoring the following parameters: Central venous oxygen saturation (ScvO2); and, Mixed venous oxygen saturation (SvO2).

The Vigileo™ Monitor, version 3.08, is a microprocessor-based instrument designed for continuous monitoring of hemodynamic parameters and oximetry. The provided text, a 510(k) summary, outlines its acceptance criteria and the studies conducted to demonstrate its safety and effectiveness.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not explicitly state numerical acceptance criteria in a table format for specific performance metrics (e.g., accuracy, precision for CO, CI, etc.). Instead, the acceptance criteria are implicitly stated as demonstrating "substantially equivalent" performance to the predicate device (K103094, Vigileo™ Arterial Pressure Cardiac Output/Oximetry Monitor). The reported device performance is described in terms of successful completion of various tests, confirming this substantial equivalence.

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

The 510(k) summary mentions "pre-clinical animal studies" and "comparative analysis of clinical data" and "comparison testing of clinical cases" for the test set. However, it does not specify the exact sample size (number of patients, animals, or data points) used for these test sets.

The data provenance is stated as including:

• Bench studies: Likely internal laboratory testing.
• Pre-clinical animal studies: Originating from animal subjects. The country of origin is not specified but typically would be internal to the company or contracted research in a regulated environment.
• Clinical data/cases: Originating from human patients. The country of origin and whether the data was retrospective or prospective is not specified.

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

The document does not specify the number of experts used to establish ground truth for the test set, nor does it detail their qualifications. The study relies on "comparative analysis of clinical data" and "comparison testing of clinical cases" against a predicate device, implying that the ground truth for these clinical cases would have been established through a combination of established clinical methods and the predicate device's measurements.

4. Adjudication Method for the Test Set

The 510(k) summary does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the test set. It mentions "comparative analysis" and "comparison testing," which suggests direct comparison of the subject device's outputs with the predicate device's outputs or established clinical reference methods, rather than a human expert adjudication process for image interpretation or diagnosis.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The device in question is a monitor that provides numerical hemodynamic and oximetry parameters, not an imaging device or AI algorithm for diagnosis that would typically involve human readers. The comparative effectiveness focused on the technical performance of the device itself against a predicate, not on how human readers' performance improves with or without AI assistance.

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

While the device is a "Single-Function, Preprogrammed Diagnostic Computer," the term "standalone" in the context of AI often refers to an algorithm making a diagnosis without human input. This device provides physiological measurements to clinicians, rather than a diagnostic interpretation in the way an AI algorithm might. Therefore, the concept of a standalone AI algorithm is not directly applicable here. The device performs its functions (calculating CO, CI, SV, etc.) based on physiological inputs, which is inherently a "standalone" processing of data, but it's not an AI making a medical decision. The primary assessment was device performance and functionality, not an AI algorithm's diagnostic capabilities.

7. The Type of Ground Truth Used

The ground truth for evaluating the Vigileo™ Monitor's performance was established through:

• Comparison to a predicate device (K103094): The primary method of demonstrating substantial equivalence was by comparing the performance and functionality of the subject device against the previously cleared predicate device.
• Established clinical methods/measurements: Implied in the "pre-clinical animal studies" and "comparative analysis of clinical data," the accuracy of the measurements would be referenced against recognized standards or gold standard measurements for cardiac output and oximetry, even if not explicitly detailed in this summary.

8. The Sample Size for the Training Set

The 510(k) summary does not mention a "training set" in the context of machine learning or AI development. This device is a preprogrammed diagnostic computer that likely uses established physiological algorithms, not a machine learning model that requires a training set. Therefore, this question is not applicable in the context of the provided document.

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

As there is no mention of a "training set" for a machine learning model, the question of how its ground truth was established is not applicable based on the provided information.

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The Vigileo APCO/Oximetry Monitor is indicated for continuously measuring hemodynamic parameters such as cardiac output and oximetry to assess oxygen delivery and consumption. When connected to an Edwards oximetry catheter, the monitor measures oximetry in adults and pediatrics.

The Vigileo APCO/Oximetry Monitor is a microprocessor- based instrument which, when connected to a FloTrac sensor, continuously measures arterial pressure cardiac output (APCO). When connected to an Edwards oximetry catheter, the monitor measures oxygen saturation (oximetry) in adults or pediatrics. The monitor also calculates other derived parameters including cardiac index, stroke volume, stroke volume index, stroke volume variation, systemic vascular resistance, systemic vascular resistance, delivered oxygen, delivered oxygen index, and pulse oximetry saturation.

The provided text is a 510(k) summary for the Vigileo APCO/Oximetry Monitor. It is a regulatory document and does not contain detailed information about acceptance criteria or specific study designs to prove device performance against such criteria. The document states that the device has undergone "functional testing demonstrating equivalence to the predicate device" and that a "Comparative Analysis" demonstrated it to be "as safe and effective as the predicate device." However, it does not provide the specifics of these tests or analyses.

Therefore, I cannot extract the requested information from the provided text. The document focuses on regulatory approval based on substantial equivalence to a predicate device rather than a detailed performance study with acceptance criteria.

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The Vigileo APCO/Oximetry monitor is intended to measure arterial pressure cardiac output and oximetry. The monitor also calculates hemodynamic and oxygenation parameters. The DDPT is intended to measure intravascular pressures. It is intended to transmit those pressure readings to both a standard blood pressure monitor and to the Vigileo monitor.

The Vigileo APCO/Oximetry monitor is indicated for use for continuously measuring hemodynamic parameters such as cardiac output and oximetry to assess oxygen delivery and consumption.

The Pressure Monitoring Kit with TruWave dual disposable pressure transducer is indicated for use in intravascular pressure monitoring. It is also indicated for use with Edwards pulse pressure based cardiac output monitoring devices or hardware to measure cardiac output.

The Vigileo APCO/Oximetry monitor is a microprocessor-based instrument which, when connected to a DDPT, continuously measures arterial pressure cardiac output (APCO). When connected to an Edwards oximetry catheter, the monitor measures oxygen saturation (oximetry). The monitor also calculates other derived parameters including cardiac index, stroke volume, stroke volume index, stroke volume variation, system vascular resistance, and systemic vascular resistance index.

The provided 510(k) summary for the Vigileo APCO/Oximetry Monitor and Edwards Dual Disposable Pressure Transducer (DDPT) does not contain the detailed acceptance criteria or the specific study details that prove the device meets these criteria in the way a modern AI/ML device submission would. This submission is from 2005 and for a non-AI/ML medical device, focusing on substantial equivalence to predicate devices based on functional and safety testing.

Therefore, many of the requested fields cannot be directly extracted from the provided text. I will address each point based on the information available and indicate where information is missing or not applicable to this type of submission.

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

The submission does not explicitly state quantitative acceptance criteria in a dedicated table or reported performance metrics from a clinical study for the Vigileo device itself. Instead, the "Comparative Analysis" states: "Both the Vigileo APCO/Oximetry monitor and the DDPT have been demonstrated to be as safe and effective as the predicate devices for their intended uses." This implies that the acceptance criterion was equivalence to the predicate devices in terms of safety and effectiveness, demonstrated through functional and safety testing, not specific performance metrics like sensitivity, specificity, or accuracy compared to a ground truth.

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

Not applicable. This submission describes functional and safety testing, not a clinical study with a test set of patient data in the context of an AI/ML device. There is no mention of patient data being used for a "test set" in the context of performance evaluation. The "functional testing" likely refers to engineering verification and validation without human subjects.

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

Not applicable. As this is not a study involving interpretation of data by experts for a ground truth, this information is not present.

4. Adjudication method for the test set

Not applicable. There is no mention of a test set requiring adjudication.

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

Not applicable. This device is not an AI-assisted diagnostic tool for human readers. It's a monitor measuring physiological parameters.

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

The device itself is a standalone monitor and transducer assembly for measuring physiological parameters. Its performance is inherent in its design and function. The "Functional/Safety Testing" mentioned would assess this standalone performance against engineering specifications and predicate device performance. It's not an "algorithm only" in the sense of an AI/ML model where human-in-the-loop is an option.

7. The type of ground truth used

For the "Functional/Safety Testing," the ground truth would likely be established through:

• Engineering specifications and standards: The device's measurements (e.g., arterial pressure, cardiac output, oxygen saturation) would be compared against known reference values from calibrated equipment.
• Predicate device performance: Performance metrics of the Vigileo were likely compared directly or indirectly to those of the Vigilance monitor and Metracor RODA Monitoring System (predicates for the monitor) and Phoenix Disposable Pressure Transducer (predicate for the transducer). This would involve comparisons of accuracy, precision, and reliability in controlled environments.

8. The sample size for the training set

Not applicable. This device is not an AI/ML device that requires a "training set" of data in the common sense. Its "training" is in its engineering design and manufacturing.

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

Not applicable, as there is no training set in the context of AI/ML. The "ground truth" for its development was based on established principles of physiology, medical device engineering standards, and the performance characteristics of its predicate devices.

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