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510(k) Data Aggregation
(310 days)
The Humid-Heat™ is an active respiratory humidifier that heats and humidifies dry breathing gases supplied from a ventilator via an endotracheal tube, a tracheostomy tube, or a face mask to adult patients under intensive care or anesthesia.
Humid-Heat™ is an active respiratory humidifier that heats and humidifies dry breathing gases supplied from a ventilator via an endotracheal, tracheostomy tube or a face mask to adult patients under intensive care or anesthesia. Patients receive physiologically optimized breathing gases (37°C, 100% relative humidity) instead of the dry and cool breathing gases they would otherwise receive if connected directly to a ventilator. They thus avoid the thickened mucus, decreased cilia activity and secretions trapped in the lower airways that are often caused by exposure to cool, dry gases. By heating and humidifying the supplied breathing gases, Humid-Heat™ overcomes these problems and provides optimal conditions for long-term ventilation.
Heating and humidification take place close to the patient, thereby eliminating the problem of condensation in ventilator circuits. Humid-Heat™ does not require any water traps or heated wires and water consumption is reduced. Humid-Heat™ is suitable for use with volume and pressure-controlled ventilators.
The Humid-Heat™ consists of five main components: the supply unit, the heater, the temperature probe, the HME and the water feed set. It is controlled with a keypad consisting of four buttons; up/down buttons to set the minute volume of the ventilator, start button, stop button, and the power switch.
The provided text describes a medical device, the Humid-Heat™, which is an active respiratory humidifier. The document focuses on its substantial equivalence to a predicate device and its intended use, rather than presenting a study with specific acceptance criteria and detailed performance data typically associated with studies proving a device meets those criteria for AI/diagnostic devices.
Therefore, many of the requested sections (sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, training set details) are not applicable as this is not a study assessing AI or diagnostic performance.
However, based on the information available, I can extract the following:
1. A table of acceptance criteria and the reported device performance
The acceptance criteria are implicitly derived from the performance characteristics of the predicate device. The reported device performance for the Humid-Heat™ is presented in comparison to these.
| Characteristic | Acceptance Criteria (Predicate device: Fisher & Paykel MR850, K983112) | Reported Device Performance (Humid-Heat ™) |
|---|---|---|
| Intended Use | To warm and add humidity to gases delivered to patients requiring mechanical ventilation. | Same |
| Airway Temperature output | 30-39° C marked, 29-40° C actual | 37° C ± 2° C |
| Moisture output | At least 30 mg H2O/L if temperature of gas delivered to patient is set to 37° C for a given continuous flow. | 44 mg H2O/L active VT 800 ml |
| 30 mg H2O/L passive VT 800 ml | ||
| Passive humidification | Maximal 20 mg H2O/L | 30 mg H2O/L passive VT 800 ml |
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. The document describes a medical device's technical specifications and comparison to a predicate device, not a study involving a "test set" in the context of AI/diagnostic device evaluation.
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. This is not a study involving ground truth establishment by experts for a test set.
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 a study on AI assistance for human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not applicable. Performance is based on technical specifications and measurements of output (temperature, moisture).
8. The sample size for the training set
Not applicable. This is not a machine learning or AI device that would have a "training set."
9. How the ground truth for the training set was established
Not applicable.
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