(166 days)
A qualitative measure of respiratory airflow for recording onto a data acquisition system.
Target Population: Children and adult patients who are screened during sleep disorder studies
Environment of Use: The majority of the screenings occur at a sleep laboratory although the sensor can also be used in home-studies.
Ultima Airflow Sensor, 1051
This document, a 510(k) clearance letter for the Ultima Airflow Sensor, provides very limited information regarding detailed acceptance criteria or a specific study proving the device meets those criteria. The letter primarily confirms that the device is substantially equivalent to legally marketed predicate devices.
Based on the provided text, the following information can be extracted or inferred:
1. Table of Acceptance Criteria and Reported Device Performance:
The document does not explicitly state specific acceptance criteria in terms of numerical performance metrics (e.g., sensitivity, specificity, accuracy, precision, correlation coefficients) or a table of performance results for the Ultima Airflow Sensor.
However, the "Indications for Use Statement" (page 2) implies its intended performance:
- Acceptance Criterion (Implied): The device must provide "a qualitative measure of respiratory airflow."
- Reported Device Performance (Implied): The device is capable of providing "a qualitative measure of respiratory airflow for recording onto a data acquisition system."
The FDA's determination of "substantial equivalence" (K981445) is the primary "proof" mentioned that the device meets the necessary criteria for its intended use, based on comparison to already approved predicate devices. This typically means the device performs at least as well as, or equivalently to, the predicate device for its intended use, but the specific performance results from comparative testing are not detailed in this letter.
2. Sample size used for the test set and the data provenance:
- Sample size: Not specified in the provided document.
- Data provenance: Not specified in the provided document. The 510(k) process relies on demonstrating equivalence to predicate devices which were previously cleared, rather than necessarily describing new, independent clinical trials with detailed test sets. Therefore, the "data" would likely refer to engineering evaluations, bench testing, and potentially some limited clinical performance data demonstrating equivalence, but no specifics are given here.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Number of experts: Not specified.
- Qualifications of experts: Not specified.
4. Adjudication method for the test set:
- Adjudication method: Not specified.
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: No indication of an MRMC study being performed. This is not an AI device, but a physical sensor. The document does not mention human "readers" or "AI assistance."
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- This is a physical sensor, not an algorithm. Therefore, the concept of "standalone algorithm performance" is not applicable in the context of this document. The device provides raw data ("qualitative measure of respiratory airflow for recording onto a data acquisition system").
7. The type of ground truth used:
- The document implies that the "ground truth" for demonstrating substantial equivalence would be the performance characteristics of its legally marketed predicate device(s). The specific performance data and the methods to establish their "ground truth" are not detailed for either the subject device or the predicate. For a device measuring respiratory airflow, the ground truth would typically be established against highly accurate reference methods (e.g., spirometry, polysomnography channels) in controlled studies.
8. The sample size for the training set:
- Not applicable/Not specified. As this is a sensor (hardware) and not a machine learning algorithm, there isn't a "training set" in the conventional sense.
9. How the ground truth for the training set was established:
- Not applicable/Not specified, for the reasons mentioned above.
Summary of Limitations Based on Document:
This 510(k) clearance letter serves as a regulatory approval notice and does not contain the detailed technical report or study data that would lay out acceptance criteria and performance results directly. The "proof" of meeting criteria primarily stems from demonstrating "substantial equivalence" to a predicate device, meaning its safety and effectiveness are similar to a device already on the market for the same intended use. The specifics of the testing (sample sizes, expert involvement, ground truth methods for the predicate or the new device) are not part of this summary document.
§ 868.2375 Breathing frequency monitor.
(a)
Identification. A breathing (ventilatory) frequency monitor is a device intended to measure or monitor a patient's respiratory rate. The device may provide an audible or visible alarm when the respiratory rate, averaged over time, is outside operator settable alarm limits. This device does not include the apnea monitor classified in § 868.2377.(b)
Classification. Class II (performance standards).