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The REMstar Auto CPAP System delivers positive airway pressure therapy for the treatment of Obstructive Sleep Apnea.

The REMstar Auto Continuous Positive Airway Pressure (CPAP) System is a microprocessorcontrolled, blower-based system that generates positive airway pressures from 4 to 20 cmH₂O. The device is intended for use with a patient circuit that is used to connect the device to the patient interface (mask). The Respironics Model 7410 Voyager has been modified to detect hypopneas and to add an interface to adjust the settings of the Remstar Heated Humidifier. The design implementation of the humidifier and the humidifier interface is the same as the Remstar Plus CPAP System (K010263). The basic functional and performance characteristic of the REMstar Auto CPAP System is unchanged from the predicate device (Model 7410 Voyager K974879).

This document is a 510(k) premarket notification for the Respironics REMstar Auto CPAP System. It focuses on demonstrating substantial equivalence to predicate devices and does not contain detailed information about specific acceptance criteria, performance studies, or clinical trial data as typically found in a comprehensive clinical study report. Therefore, I can only extract limited information based on the provided text.

Here's an attempt to answer your questions based on the available text:

1. Table of acceptance criteria and the reported device performance

   The document states: "Design verification tests were performed on the REMstar Auto CPAP System because of the risk analysis and product requirements. All tests were verified to meet the required acceptance criteria."

   However, no specific acceptance criteria or detailed reported device performance metrics are provided in the text. The submission highlights that the device has the "Same operating principle," "Same technology," and "Same manufacturing process" as the predicate, and that its "basic functional and performance characteristic... is unchanged from the predicate device (Model 7410 Voyager K974879)." The key modification is the ability to "detect hypopneas."

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

   The document mentions "design verification tests" but does not specify the sample size used for any test set or the data provenance. There is no indication of clinical trial data or patient data being used for these tests; "design verification tests" typically refer to engineering and bench testing.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

   This information is not provided in the document. The submission focuses on substantial equivalence based on design and technical characteristics rather than a clinical evaluation requiring expert ground truth assessment.

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

   This information is not provided in the document.

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 MRMC comparative effectiveness study is mentioned. The device is a CPAP system, not an AI-assisted diagnostic tool that would typically involve human readers. The new feature is the automatic detection of hypopneas, which is an algorithmic function, but its comparative effectiveness in improving human reader performance is not discussed.

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

   The document mentions that the modified device "has been modified to detect hypopneas." This suggests an algorithm for detecting hypopneas is part of the device's functionality. The "design verification tests" would have evaluated the performance of this detection algorithm in a standalone manner (without a human in the loop for the detection itself), but details of these tests and their results are not provided.

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

   The document refers to "design verification tests" but does not specify the type of ground truth used for evaluating the hypopnea detection, or any other functional aspect. For a feature like hypopnea detection, ground truth would typically come from polysomnography data scored by sleep experts, but this is not mentioned.

8. The sample size for the training set

   The document does not mention any training set or machine learning development process, nor a sample size for it. While the device detects hypopneas, it doesn't explicitly state that this detection uses a modern machine learning model that would require a distinct training set. It could be based on rule-based algorithms.

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

   Since no training set is mentioned, information on how its ground truth was established is not provided.

In summary, this 510(k) submission is a regulatory document focused on demonstrating substantial equivalence, not a detailed technical or clinical study report. It states that design verification tests were conducted and met acceptance criteria, but it does not provide the specifics of these tests, including sample sizes, ground truth establishment, or detailed performance metrics.

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