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The Breas Vivo 30 is designed for spontaneous breathing patients who require long-term support by mechanical ventilation during night and part of the day.

The Breas Vivo 30 shall only be used by patients with spontaneous breathing.

The Breas Vivo 30 is not intended for life-sustaining applications or for transport of critical care patients.

The Breas Vivo 30 is intended for non-invasive use.

The Breas Vivo 30 is intended for treatment of adult (who weigh more than 30 kg) patients.

The Vivo 30 is a pressure-supported and pressure-controlled ventilator with a CPAP function intended for spontaneous breathing patients who require long-term support by mechanical ventilation during night and part of the day.

The Vivo 30 can be used in clinical settings (e.g., hospitals, sleep laboratories, sub-acute care institutions) and home environments. It must always be prescribed by a licensed physician.

It is not intended for life support applications or for transport of critical care patients.

The therapy delivered by the Breas Vivo 30 System can be either:

• 1. Pressure Controlled Ventilation (PCV) or
• 2. Pressure Support Ventilation (PSV) or
• 3. Constant Positive Airway Pressure (CPAP)

The Vivo 30 airflow is delivered via a single lumen outlet tube that may be connected to various non-invasive patient interfaces, such as nasal masks.

The Vivo 30 has an auto-switching power supply that facilitates use in conjunction with international travel (100 - 240 VAC). It can also be used with an external 12.5/ 24 VDC power source when AC mains line voltage is not available.

The outer dimensions of the Vivo 30 housing are 7.2 × 8.9 inches, and the device weighs 7.3 pounds.

The provided document K060546 describes the Breas Vivo 30 System, a bilevel ventilator. The submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical testing rather than clinical performance studies.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

The document does not provide a specific table of acceptance criteria with corresponding performance metrics. Instead, it outlines a series of tests performed to demonstrate that the device meets its stated performance specifications and is substantially equivalent to a predicate device. The general acceptance criterion implied is the successful completion of all specified tests.

• IEC 601-1 (Electrical Safety)
• ISO 10651-6 (Safety and Essential Performance)
• ISO 17510-1 (Safety and Performance)
• Electromagnetic Compatibility (EMC)
• Mechanical Safety
• Environmental Testing
• Functional Testing
• Particle matter testing
• FDA's "Reviewer Guidance for Premarket Notification Submissions" (Nov 1993 draft)
• FDA's "Draft Reviewer Guidance for Ventilators" (July 1995) |
  | Software Validation | All device software (embedded and Calendar Analysis PC software) documented and tested in accordance with FDA's May 11, 2005 "Guidance for the content of Premarket Submissions for Software Contained in Medical Devices". Device passed all tests. |

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 focuses on non-clinical bench testing. Therefore, there is no patient-based test set or associated sample size in the traditional sense of human subjects. The testing involved the device itself and potentially various scenarios, simulations, and measurement equipment.

Data provenance: The testing was conducted by Breas Medical AB, a Swedish company, but the specific location of the tests is not detailed beyond "non-clinical testing was conducted." The submission is to the FDA in the United States.

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 is not applicable as the submission did not involve a clinical study with a patient test set requiring expert ground truth establishment. The "ground truth" for non-clinical tests would be defined by engineering specifications, regulatory standards, and the performance of the predicate device.

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

This is not applicable due to the absence of a patient-based test set requiring expert 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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The device is a ventilator, not an AI-assisted diagnostic tool for human readers.

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

The device itself is a standalone medical device (ventilator). The "performance" discussed is its mechanical and functional operation as specified by its engineering and regulatory standards, not the performance of an algorithm in a diagnostic context. The document specifies that clinical studies were not required to support substantial equivalence. The standalone performance was evaluated through the non-clinical and comparative bench testing described.

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

For the non-clinical tests, the "ground truth" was established by engineering specifications, recognized regulatory standards (IEC, ISO), and the performance characteristics of the predicate device. For instance, electrical safety compliance is determined by meeting the specified limits in IEC 601-1, which serves as the ground truth for that test.

8. The sample size for the training set

This is not applicable. The device is a hardware system with embedded software for control; it is not a machine learning or AI-based device that undergoes "training" with a dataset in the conventional sense. The software was "documented and tested," which refers to software validation practices, not machine learning model training.

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

This is not applicable as there was no training set for a machine learning model.

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