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The Fabius GS/Fabius Tiro is indicated as a continuous flow anesthesia system. The Fabius GS/Fabius Tiro can be used for spontaneous, manually assisted, automatic, pressure support, or synchronized mandatory intermittent ventilation, delivery of gases and anesthetic vapor, and monitoring oxygen concentration, breathing pressure and respiratory volume of patients during anesthesia. Federal law restricts this device to sale by or on the order of a physician.

The Fabius GS/Fabius Tiro is a continuous flow gas anesthesia system.

The provided document is a 510(k) summary for the Fabius GS/Fabius Tiro Anesthesia System, specifically for a software modification to include Synchronized Intermittent Mandatory Ventilation with Pressure Support (SIMV/PS) as an optional ventilation mode.

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

Description of Acceptance Criteria and Reported Device Performance

The document describes the addition of SIMV/PS mode to the existing Fabius GS/Fabius Tiro Anesthesia System. The acceptance criteria are implicitly based on demonstrating substantial equivalence to existing predicate devices (Evita 4 Ventilator K961687 and 7900 Ventilator K023366) with SIMV with Pressure Support ventilation modes. The substantial equivalence is claimed based on functional similarities and that the device "meets" these similarities.

Study Details:

This document describes a 510(k) submission, which primarily relies on demonstrating substantial equivalence to predicate devices rather than a standalone clinical study on the device's performance against specific acceptance criteria. Therefore, many of the requested details typically found in the clinical study report are not explicitly present.

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not applicable in the context of this 510(k) submission. No patient-level test set data is mentioned for performance evaluation in the conventional sense. The "test set" here refers to the parameters and functionalities that were verified against the predicate devices.
   • Data Provenance: Not specified. The submission relies on a comparison of technical specifications and features with previously cleared predicate devices.

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

   • Not applicable as this is a comparison against predicate devices' functionalities, not a diagnostic or clinical performance study requiring expert ground truth establishment. The "ground truth" for substantial equivalence is the functionality of the predicate devices.

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

   • Not applicable. There's no clinical data adjudication described in this type of submission.

4. 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 study was conducted. This device is an anesthesia system, not an AI-powered diagnostic tool for human readers.

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

   • No standalone algorithm performance study was described. The submission focuses on the integration of a new ventilation mode (software change) into an existing, cleared anesthesia system. Performance claims are based on similarity to predicate devices already cleared for use.

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

   • The "ground truth" for this submission is the established functionality and regulatory clearance of the predicate devices (Evita 4 Ventilator and 7900 Ventilator) regarding their SIMV with Pressure Support ventilation modes. The new device's SIMV/PS mode is deemed substantially equivalent based on matching these functionalities and parameters.

7. The sample size for the training set:

   • Not applicable. This is not a machine learning or AI algorithm in the sense that it requires a training set of data. The "training set" would implicitly be the established engineering and design principles used to develop the software, which are not quantified in terms of sample size here.

8. How the ground truth for the training set was established:

   • Not applicable for the same reasons as above. The "ground truth" for the development of the SIMV/PS mode would stem from medical knowledge of ventilation, engineering specifications, and the functionality observed in the predicate devices.

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The Fabius GS and Fabius Tiro are indicated as a continuous flow anesthesia systems. The Fabius GS and Fabius Tiro are indicated for spontaneous, manually assisted, automatic or pressure support ventilation; delivery of gases and anesthetic vapor of patients during anesthesia. The Fabius GS and Fabius Tiro can monitor inspired oxygen concentration, breathing pressure and respiratory volume of patients during anesthesia. The Fabius GS and Fabius Tiro are to be used only in the order of a physician.

The Fabius GS and Fabius Tiro are continuous flow gas anesthesia systems.

The provided document does not contain specific acceptance criteria or a detailed study description for the Fabius GS and Fabius Tiro Anesthesia Systems' Apnea Ventilation feature in the same way one would expect for a machine learning or AI-driven device.

Instead, the document focuses on demonstrating substantial equivalence to existing predicate devices based on a hazard analysis, system-level qualification, and verification/validation tests. The Apnea Ventilation feature is described as a software change.

Therefore, many of the requested details (like sample sizes for test/training sets, data provenance, expert ground truth, MRMC studies, standalone performance, etc.) are not applicable or not provided in this type of regulatory submission, which predates the common expectations for AI/ML device descriptions.

However, I can extract the available information and indicate where details are not provided.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) submission for a software change to an existing device, the "acceptance criteria" are framed in terms of maintaining safety and effectiveness and demonstrating substantial equivalence to predicates. Performance is described functionally rather than with quantitative metrics typically found in AI/ML performance tables.

• User Alert: All three devices (Fabius GS/Tiro, Evita 4, 7900 Ventilator) alert the user to the apnea condition.
• User Disablement: The Apnea Ventilation feature can be disabled by the user, similar to the predicate devices.
• Backup Function: Intended as a short-term backup to prevent apnea if spontaneous effort fails.
• Core Functionality: Basic infrastructure, operating principle, alarm strategies, fault detection circuitry, and mechanical/pneumatic subassemblies remain unchanged. |
  | Hazard Analysis & Qualification Success | Qualification included hazard analysis, system level qualification, and verification/validation tests were performed, implying successful completion. (Specific results or metrics from these tests are not detailed in this summary). |
  | Functional Equivalence | - All devices are triggered if a user-selected time elapses without a spontaneous breath during pressure support ventilation.
• All prevent an apnea condition.
• All alert the user to the condition.
• All can be disabled by the user. |
  | (Implied) No adverse impact on existing device features | The document states the change is "software only" and that the "basic infrastructure... remain unchanged," implying functionality not related to the Apnea Ventilation feature is unaffected. |

Study Details:

The document describes the evidence for substantial equivalence, which primarily constituted a comparison to predicate devices and internal qualification processes.

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

• Not Applicable / Not Provided: This submission focuses on a software change to an existing device and its substantial equivalence to predicate devices. There is no mention of a "test set" in the context of patient data or algorithm performance used in a way that generates sample sizes for statistical analysis relevant to AI/ML. The "test" here refers to verification and validation of the software feature itself and its integration into the system.

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

• Not Applicable / Not Provided: Ground truth in the context of patient data adjudicated by experts is not described for this type of submission. The "ground truth" here would relate to the functional correctness of the software and its ability to prevent apnea as designed, which is established through engineering and clinical validation rather than expert labeling of empirical patient data.

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

• Not Applicable / Not Provided: As no "test set" requiring expert adjudication is described, no adjudication method is mentioned.

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 / Not Provided: This device is an anesthesia system with an apnea detection and response feature, not a diagnostic AI/ML tool designed to assist human readers (e.g., radiologists). Therefore, an MRMC study is irrelevant to this submission.

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

• Partially Applicable / Implicitly Done: The "Apnea Ventilation feature" is a software algorithm that operates autonomously when triggered by the absence of spontaneous breaths. The "system level qualification, and verification/validation tests" would have evaluated its standalone (algorithm-only) performance within the device infrastructure. However, specific performance metrics or a detailed description of these tests (e.g., how apnea was simulated and how the system responded) are not provided in this summary. The device's overall use involves a human operator, but the apnea feature itself is an automated response.

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

• Functional/Design Specification Ground Truth: The "ground truth" for this device's feature would be its adherence to engineering specifications for detecting apnea conditions and successfully delivering a pressure support breath as designed, and its equivalence to the functional behavior of the predicate devices. This would be established through bench testing, simulated scenarios, and potentially animal or human studies to confirm physiological response, though details are not supplied in this specific summary.

8. The sample size for the training set

• Not Applicable / Not Provided: This is a deterministic software feature, not a machine learning algorithm that requires a "training set" in the conventional sense. The "training" here would refer to software development and debugging.

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

• Not Applicable / Not Provided: As there is no "training set" for an AI/ML model, this question is not relevant. The ground truth for the device's design and function comes from established medical standards for ventilation and anesthesia, engineering principles, and the functional behavior of predicate devices.

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The Fabius GS is indicated as a continuous flow anesthesia system. The Fabius GS can be used for spontaneous, manually assisted, automatic or pressure support ventilation, delivery of gases and anesthetic vapor, and monitoring oxygen concentration, breathing pressure and respiratory volume of patients during anesthesia. Federal law restricts this device to sale by or on the order of a physician.

The Fabius Tiro is indicated as a continuous flow anesthesia system. The Fabius Tiro can be used for spontaneous, manually assisted or automatic ventilation, delivery of gases and anesthetic vapor, and monitoring oxygen concentration, breathing pressure and respiratory volume of patients during anesthesia. Federal law restricts this device to sale by or on the order of a physician.

The Fabius GS and Fabius Tiro are continuous flow gas anesthesia systems.

The compact breathing system (COSY) used with the Fabius GS (K030624) and Fabius Tiro (K031400) anesthesia systems is being modified to incorporate a heater plate. The addition of a heater plate is a hardware change only. The basic infrastructure, operating principle, alarm strategies, fault detection circuitry, and mechanical/pneumatic subassemblies within the Fabius GS/Tiro remain unchanged.

The heater consists of a heater foil, metal plate and insulator plate that are mounted between the absorber canister and COSY block. The heater subassembly is connected via a power cable to a separate power supply mounted externally to a Fabius GS/Tiro Anesthesia System.

Like the heater plate utilized in the NM6400's (K033498) Divan ventilator, the heater incorporated into the COSY is intended to warm the breathing system to minimize moisture accumulation in the breathing system components, especially during cases utilizing low flow anesthesia and/or during cases of low ambient environmental temperatures. It is not intended to control or maintain a set temperature of the patient breathing gas or to humidify the gas. Once activated, the heating plate in the COSY, like the NM6400, heats up to a constant temperature. This heat is then transferred, through conduction, to the components of the breathing system above it. In the NM6400, the heater plate is activated when the NM6400 System Power switch is turned to the "On" position and is de-activated when the NM6400 is switched to "Standby." With the COSY, heater activation is independent of the Fabius GS/Tiro. To activate the heater, the user is required to switch the heater power supply to the "On" position. To deactivate, the user is required to switch the power supply to the "Off" position.

This document describes the Fabius GS and Fabius Tiro Anesthesia Systems with a modification: the addition of a heater plate to the compact breathing system (COSY). The acceptance criteria and supporting study details are based on the provided text, which is a 510(k) premarket notification summary.

It's important to note that this document is a 510(k) summary for a hardware modification to an existing medical device, not a new, AI-powered diagnostic device. Therefore, many of the typical elements expected for AI/ML device studies (e.g., sample size for AI, ground truth establishment, MRMC studies) are not applicable here. The evaluation focuses on the safety and effectiveness of the hardware change relative to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a table format with specific performance metrics such as accuracy, sensitivity, or specificity, as would be common for diagnostic AI algorithms. Instead, the "acceptance criteria" are implied by the scope of the premarket notification: to demonstrate that the modified device remains substantially equivalent to legally marketed predicate devices and that the hardware change does not introduce new safety or effectiveness concerns.

The performance is implicitly demonstrated by the comparison to predicate devices and the completion of standard engineering verification and validation.

Study Details:

The supporting "study" is a collection of engineering tests and regulatory analyses performed to ensure the modified device (Fabius GS/Tiro with heater plate) remains safe and effective and continues to be substantially equivalent to its predicate devices.

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

• Sample Size for Test Set: This concept doesn't directly apply in the context of this 510(k) for a hardware modification. There isn't a "test set" of patient data in the way an AI/ML diagnostic algorithm would have. Instead, the "testing" involves internal engineering verification and validation of the physical device, including hazard analysis and system-level qualification. The sample size for these engineering tests would likely refer to the number of units tested, the number of test cycles, or the specific operating conditions evaluated, which are not detailed in this summary.
• Data Provenance: Not applicable in the context of patient data for an algorithm. The "data" comes from internal engineering and manufacturing records, hazard analyses, and performance testing of the device hardware.

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

Not applicable. There is no "ground truth" established by experts for a test set of data in this context. The evaluation is of hardware performance and substantial equivalence to predicate devices, not of diagnostic accuracy.

4. Adjudication Method for the Test Set

Not applicable. No expert adjudication method (like 2+1, 3+1 consensus) would be used for this type of hardware modification submission.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No. An MRMC study is relevant for evaluating the impact of an AI algorithm on human reader performance (e.g., radiologists interpreting images). This submission is for a hardware modification to an anesthesia machine and does not involve human readers analyzing medical cases with or without AI assistance.

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

No. This device is a piece of hardware, an anesthesia machine, which inherently requires human operation and interaction for its intended use. There is no standalone "algorithm only" performance to evaluate.

7. The Type of Ground Truth Used

Not applicable in the typical sense of diagnostic algorithms (e.g., pathology, outcomes data). The "ground truth" for this submission revolves around:

• Engineering specifications and design requirements.
• Performance against established safety standards.
• Functional equivalence compared to predicate devices (e.g., the NM6400's heater plate).
• Results from internal verification and validation testing, which confirm the device meets its design intent.

8. The Sample Size for the Training Set

Not applicable. There is no AI/ML algorithm involved, and therefore no "training set" of data.

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

Not applicable, as there is no training set for an AI/ML algorithm.

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The Fabius Tiro is indicated as a continuous flow anesthesia system. The Fabius Tiro can be used for spontaneous, manually assisted or automatic ventilation, delivery of gases and anesthetic vapor, and monitoring oxygen concentration, breathing pressure and respiratory volume of patients during anesthesia. Federal law restricts this device to sale by or on the order of a physician.

The modified Fabius GS (Fabius Tiro) is a continuous flow gas anesthesia system.

The provided document, K031400, is a 510(k) premarket notification for the Fabius Tiro Anesthesia System. This type of submission is for demonstrating substantial equivalence to a legally marketed predicate device, rather than proving that a device meets specific performance criteria through a detailed clinical study with acceptance criteria.

Therefore, the document does not contain the information requested in your prompt regarding acceptance criteria, study details, sample sizes, expert ground truth, or MRMC studies. The content focuses on describing the device, its intended use, and the modifications from its predicate device (Fabius GS Anesthesia System) to establish substantial equivalence.

Here's why the requested information is absent based on the provided text:

• Acceptance Criteria and Device Performance: The document does not list specific measurable acceptance criteria that the Fabius Tiro had to meet. Instead, it describes modifications from a predicate device and states that "Qualification of the Fabius Tiro included a hazard analysis, system level qualification, and verification tests." However, the results or specific acceptance criteria for these tests are not detailed.
• Sample Size, Data Provenance, Ground Truth, Experts, Adjudication, MRMC, Standalone Performance: These are elements typically found in clinical studies or performance validation studies designed to establish efficacy or diagnostic accuracy. A 510(k) submission primarily relies on demonstrating that a new device is "substantially equivalent" to an existing legally marketed device, often through engineering modifications, bench testing, and comparisons to the predicate's specifications, rather than new clinical trials with human subjects.
• Training Set Size and Ground Truth for Training Set: Since this is a hardware device (anesthesia system), it doesn't involve machine learning or AI in the sense of a "training set" for an algorithm. The software changes mentioned are purely cosmetic (changing "Fabius GS" to "Fabius Tiro").

In summary, the provided document does not contain the information necessary to complete the table and answer the specific questions about acceptance criteria and a study design as typically associated with performance validation studies for AI/ML devices or novel therapies. The FDA's letter explicitly states that they have reviewed the 510(k) and determined the device is "substantially equivalent," which is the core of this type of submission.

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