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The 840 Ventilator System with Leak Compensation Option is intended to provide continuous ventilation to patients requiring respiratory support. The device is intended for patients with an Ideal Body Weight (IBW) as low as 0.5 kg (with NeoMode option) to adult, and for use in a wide variety of clinical conditions.

The 840 Ventilator System with Leak Compensation Options is intended for a wide range of patients ranging from neonate to adult (V- 5-2500 mL with NeoMode) or from infant to adult (V-25-2500 mL).

The 840 Ventilator System with Leak Compensation is intended for use in hospital and hospital-type facilities. It may be used during hospital and hospital-type facility transport provided that electrical power and compressed gas are supplied.

The Leak Compensation Option enables the 840 Ventilator System to automatically detect and characterize gas flow that leaves the breathing circuit through air leak(s) and does not return to the 840's exhalation valve for inclusion in the displayed exhaled spirometry. LC does not makeup for lost delivered volume but does add the necessary additional flow into the breathing circuit to optimize breath triggering and cycling functions as well as maintain the set PEEP, and display clinically valuable leak-related parameters for consideration by the clinician. The Leak Compensation feature is implemented on the 840 Ventilator through additional functionality in software and by use of the existing User Interface panel. No hardware or firmware changes or additions were required. The 840 Ventilator is a dual-microprocessor controlled, critical care ventilator intended to provide continuous ventilation for neonate to adult (with NeoMode Option) or infant to adult (without NeoMode Option) patients who require either invasive ventilation or non-invasive ventilation (via face mask).

Here's an analysis of the provided 510(k) summary regarding the Puritan-Bennett 840 Ventilator System with Leak Compensation Option, focusing on acceptance criteria and study details.

Important Note: The provided document is a 510(k) summary, which is a high-level overview. It generally does not contain the granular detail of a full study report (like sample sizes for specific tests, expert qualifications, or adjudication methods). Therefore, for several points, the answer will be that the information is not provided in this summary.

Acceptance Criteria and Reported Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not provide a table with specific, quantitative acceptance criteria (e.g., "leak compensation accuracy must be within X%"). Instead, it states that the device "passed all tests" against its stated performance specifications and applicable guidance.

Study Details

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: Not specified in this 510(k) summary. The document mentions "functional testing," "testing confirms," and "all software is tested," implying internal verification and validation activities, but does not quantify the sample sizes for these tests (e.g., number of test cases, number of ventilation cycles simulated).
• Data Provenance: Not specified. The testing described appears to be internal engineering and software verification/validation, not a clinical study with patient data.

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)

• Number of Experts & Qualifications: Not applicable/Not specified. The testing described focuses on functional performance and software compliance, not diagnostic accuracy against expert-established ground truth. These types of tests are typically conducted by engineers and quality assurance personnel against predefined performance specifications.

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

• Adjudication Method: Not applicable/Not specified. Adjudication methods are typically used in clinical studies or studies where multiple human readers interpret data, and their decisions need to be reconciled to establish a ground truth. This document describes functional and software testing.

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

• MRMC Study Done: No, an MRMC comparative effectiveness study was not described in this 510(k) summary.
• Effect Size of Human Improvement: Not applicable, as no MRMC study was described. The device is a ventilator with a leak compensation feature, not an AI-assisted diagnostic tool that humans interpret.

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

• Standalone Performance: Yes, in essence, the "functional testing" and "software testing" described represent the standalone performance of the device's leak compensation algorithm and system. The ventilator itself operates automatically based on its algorithms to detect and compensate for leaks. The summary states: "Functional testing confirms that the 840 Ventilator with Leak Compensation Ventilator is capable of meeting its stated performance specifications." This refers to the device's performance without direct human intervention in the leak compensation process itself.

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

• Type of Ground Truth: For the functional and software testing, the "ground truth" would be the engineering specifications and design requirements for the leak compensation function, as well as the applicable regulatory standards and guidance documents (e.g., "Draft Reviewer Guidance for Ventilators"). The device was tested against these predefined performance targets.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/Not specified. This device is a mechanical ventilator with control algorithms, not a machine learning or AI system that requires a "training set" in the conventional sense of supervised learning. Its algorithms are developed through engineering design, mathematical modeling, and control theory rather than statistical training on a dataset.

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

• Ground Truth for Training Set Establishment: Not applicable, as there is no "training set" in the context of machine learning. The algorithms were designed and verified against established principles of fluid dynamics, respiratory physiology, and control system engineering, coupled with internal performance specifications and regulatory guidance.

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The PB 540 Ventilator is indicated for the continuous or intermittent mechanical ventilatory support of patients weighing at least 5 kg who require mechanical ventilation. The ventilator is a restricted medical device intended for use by qualified, trained personnel under the direction of a physician. Specifically, the ventilator is applicable for adult and pediatric patients who require the following general types of ventilatory support, as prescribed by an attending physician:

• Positive Pressure ventilation
• Assist/Control, SIMV, or CPAP modes of ventilation
• Breath types including Volume, Pressure Control and Pressure Support.
  The ventilator is suitable for use in institutional, home, and transport settings. It is not intended for use as an emergency transport ventilator.

The PB 540 Ventilator gas delivery system is composed of a flow generator capable of supplying a sufficient range of flows and pressures that is then controlled by a three-way valve enabling piloting of the expiration valve. The flow generator is a low-inertia micro-turbine driven by a brushless electric motor and the valve is a proportional electronically driven valve.
These two actuators are controlled according to specific control algorithms by a microprocessor receiving information from the pressure and flow sensors built into the ventilator.

The provided 510(k) summary for the PB 540 Ventilator does not describe an AI/ML device or a study involving acceptance criteria in the typical sense of diagnostic performance metrics (e.g., sensitivity, specificity, AUC). Instead, it's for a traditional medical device (a ventilator) and discusses performance in terms of functional testing and compliance with regulatory guidelines.

Therefore, the requested information elements related to AI device performance evaluation (such as sample size for test sets, ground truth establishment, expert adjudication, MRMC studies, standalone performance, training set details) are not applicable to this document.

Here's an interpretation based on the information available:

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

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 does not specify sample sizes for any "test sets" in the context of diagnostic performance. The testing described is functional and compliance-based for a physical device, not an AI model.

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)

• Not applicable as this is not an AI/ML diagnostic device and no "ground truth" in this context was established. Performance was assessed against predefined functional and regulatory standards.

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

• Not applicable.

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 is not an AI-assisted diagnostic device.

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

• Not applicable. This is not an algorithm-only device. The "standalone" performance implied refers to the ventilator itself meeting its functional specifications.

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

• The concept of "ground truth" as used in AI/ML performance evaluation is not relevant here. The "truth" for this device's performance is its ability to meet engineering specifications, functional requirements, and regulatory standards.

8. The sample size for the training set

• Not applicable. This is not an AI/ML device that requires a training set.

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

• Not applicable.

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