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The Prefense EDNS-9000 Series Central Nurse Station is intended for use by medical professionals to provide cardiac and vital signs monitoring for multiple patients within a medical facility.

The Prefense EDNS-9000 Series Central Nurse Station will display and record physiological data from up to forty telemetry receivers/transmitters and generates an alarm when a measured parameter falls outside a preset limit or when an arrhythmia is detected. Arrhythmia detection is a function of the telemetry receivers/transmitters. Alarm determination can be configured to be performed by either the Prefense EDNS-9000 Series Central Nurse Station or by the telemetry receivers/transmitters.

The device is intended for use by medical professionals to provide cardiac and vital signs monitoring for multiple patients within a medical facility. The Prefense EDNS-9000 Series Central Nurse Station will display and record physiological data from up to forty telemetry receiver/transmitters and generates an alarm when a measured parameter falls outside a pre-set limit or when life threatening arrhythmia is detected. Arrhythmia detection is a function of the telemetry receivers (Model ORG-9700 Multiple Patient Receiver, per 510k K071058 Commercial distribution certification dated June 29, 2007) transmitter (Model ZS-940PA, per 510(k) K043517 Commercial Distribution certification dated February 3, 2005). Alarm determination can be configured to be performed by either the Prefense EDNS-9000 Series Central Nurse Station or by the telemetry receivers (Model ORG-9700 Multiple Patient Receiver, per 510k K071058 Commercial distribution certification dated June 29, 2007) transmitter (Model ZS-940PA, per 510(k) K043517 Commercial Distribution certification dated February 3, 2005).

The provided text is a 510(k) summary for the Nihon Kohden Prefense EDNS-9000 Series Central Nurse Station. The submission is for a software change to an existing predicate device, primarily allowing the central nurse station itself to perform alarm determination, which was previously handled exclusively by the telemetry receivers/transmitters.

Based on the provided text, here's an analysis of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy for arrhythmia detection, or alarm-related performance). Instead, it relies on substantial equivalence to a predicate device and general validation of software and hardware operation.

The "device performance" reported is largely that the device operates according to design specifications and that the non-clinical tests (electromagnetic, environmental, safety, and performance) verified proper operation.

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

The document does not specify a sample size for a "test set" in the context of clinical performance evaluation (e.g., patient data for arrhythmia detection). The studies mentioned are primarily non-clinical: electromagnetic, environmental, safety, and general performance testing, as well as design validation. Therefore, there is no mention of data provenance (e.g., country of origin, retrospective/prospective).

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. Since the submission focuses on a software change to an existing device and relies on non-clinical testing and substantial equivalence, there is no indication of expert-established ground truth for a clinical test set.

4. Adjudication method for the test set

This information is not provided. No clinical test set with human-adjudicated ground truth is described.

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

There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study. This device is a central nurse station for physiological monitoring and alarm generation, not an AI-assisted diagnostic tool that would typically involve human readers interpreting output.

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

The document states that "Arrhythmia detection is a function of the telemetry receivers/transmitters" and "Alarm determination can be configured to be performed by either the Prefense EDNS-9000 Series Central Nurse Station or by the telemetry receivers/transmitters." This implies that the device (or its components) performs alarm determination algorithmically and automatically. However, the exact performance metrics of this standalone algorithmic detection (e.g., sensitivity, specificity, positive predictive value for arrhythmia detection) are not provided in this summary. The summary focuses on the functionality of the alarm determination rather than its specific performance characteristics.

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

Given the nature of the device (physiological monitor and alarm system) and the type of information provided, the ground truth for "performance testing" and "design validation" would likely involve:

• Engineering specifications and test protocols: Verification that the device functions as designed against pre-defined engineering requirements (e.g., alarm triggers correctly at set limits, data display is accurate).
• Simulated physiological signals: For testing arrhythmia detection and alarm generation, simulated ECG signals with known arrhythmias would likely be used.

However, the document does not explicitly detail the type of ground truth used beyond "design specifications" and verification of "proper operation." There is no mention of ground truth established through expert consensus on clinical data, pathology, or outcomes data.

8. The sample size for the training set

This information is not provided. The document describes a software change to an existing device, not the development of a new algorithm that would typically involve a separate training set.

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

This information is not provided, as there is no mention of a training set.

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