(28 days)
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.
| Acceptance Criterion (Implicit/Derived) | Reported Device Performance |
|---|---|
| Safety & Efficacy equivalent to predicates (Evita 4 and 7900 Ventilator) | - Apnea Condition Prevention: The Fabius GS/Tiro Apnea Ventilation feature, like its predicates, triggers (delivers a Pressure Support breath) when a patient's spontaneous breathing rate falls below a minimum frequency setting, thereby preventing an apnea condition. |
- 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.
§ 868.5160 Gas machine for anesthesia or analgesia.
(a)
Gas machine for anesthesia —(1)Identification. A gas machine for anesthesia is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation. The device may include a gas flowmeter, vaporizer, ventilator, breathing circuit with bag, and emergency air supply.(2)
Classification. Class II (performance standards).(b)
Gas machine for analgesia —(1)Identification. A gas machine for analgesia is a device used to administer to a patient an analgesic agent, such as a nitrous oxide-oxygen mixture (maximum concentration of 70 percent nitrous oxide).(2)
Classification. Class II (performance standards).