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510(k) Data Aggregation
(64 days)
CAPNO MODULE, 92517
The Capno Module, 92517 (92517) is intended to provide a means of monitoring carbon dioxide and respiration rate and alert clinical personnel when the concentration moves outside of user-defined limits.
The 92517 is intended to be used with and controlled by a Spacelabs Healthcare monitors. The 92517 is intended to be used for monitoring adult, pediatric and neonate patients, under the direction of qualified medical personnel.
The Spacelabs Healthcare Capno Module, 92517 (92517) is an easy-to-use modular unit used with Spacelabs Healthcare Ultraview SL or XPREZZON monitors. The 92517 is inserted into the bay within the monitors. which is then used to control the 92517, and provide the user interface for the 92517.
The 92517 is a sidestream or mainstream analyzer intended to provide a measurement of the following parameters: carbon dioxide (CO2); and respiratory rate.
The monitor provides a number display for CO2 and respiratory rate, and a capnograph waveform. The 92517 is intended to be used primarily in the operating room environment.
The provided 510(k) summary for the Spacelabs Healthcare Capno Module, 92517, describes the device, its intended use, and technology comparison to a predicate device. However, it does not contain detailed information about specific acceptance criteria for performance metrics (like accuracy, sensitivity, specificity) or the specific results of performance testing in a tabular format as requested.
Instead, the document focuses on compliance with electrical safety, electromagnetic compatibility (EMC), general requirements for safety, alarm systems, and software development standards. It confirms that the device complies with its predetermined specifications and applicable standards, but does not present the raw performance data or detailed acceptance criteria for clinical efficacy.
Therefore, much of the requested information cannot be extracted from this document.
Here's an attempt to answer based on the available information, noting where data is missing:
1. A table of acceptance criteria and the reported device performance
The document states that "Test results indicated that the 92517 complies with its predetermined specification and with the applicable Standards" for various technical and safety aspects. However, specific performance acceptance criteria for parameters like CO2 measurement accuracy or respiratory rate accuracy, and the actual numerical performance results against those criteria, are not provided in the given text. The relevant sections (Summary of Performance Testing, Performance Testing) mention compliance with standards but do not detail clinical performance metrics.
Acceptance Criteria (e.g., Accuracy, Range) | Reported Device Performance |
---|---|
Specific clinical performance criteria for CO2 and respiratory rate are not detailed in the provided text. | The device "complies with its predetermined specification and the Standards." |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
This information is not available in the provided 510(k) summary. The document mentions "Performance Testing" and "Verification and validation activities," implying tests were conducted, but it does not specify the sample size of patients or data points, nor the provenance (country, retrospective/prospective nature) of any data used for these tests.
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)
This information is not available in the provided 510(k) summary. The document focuses on technical and safety compliance rather than clinical study protocols involving expert review for ground truth establishment.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
This information is not available in the provided 510(k) summary. Given the focus on technical compliance, a clinical adjudication method is unlikely to be discussed unless specific clinical trials were performed and detailed in the summary (which they are not in this case).
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
An MRMC comparative effectiveness study is not mentioned in the provided 510(k) summary. This device is a CO2 and respiratory rate monitor, not an AI diagnostic tool designed to assist human readers in interpreting images or complex data, so such a study would not be relevant in this context.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This device is a physical medical device (CO2 monitor) that provides measurements. Its performance is inherently "standalone" in generating these measurements, though a human interprets and acts on the displayed data. The document confirms that the device "complies with its predetermined specification and the Standards" through performance testing, which is essentially standalone validation of the device's measurement capabilities.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The document does not explicitly state the "ground truth" method for its performance testing related to CO2 and respiratory rate measurements. For physiological parameters like these, typically, the ground truth would be established using a highly accurate reference standard (e.g., a calibrated gas analyzer for CO2 concentration, or manual counting of respirations by a trained clinician for respiratory rate, or a verified simulator). The 510(k) summary implicitly suggests that the device's measurements were compared against accepted "standards" and "specifications."
8. The sample size for the training set
This information is not available in the provided 510(k) summary. The device is not described as involving machine learning or AI models that require a "training set" in the conventional sense. Software modifications are mentioned, but their development and validation adhere to software life cycle processes and guidance documents, not AI training methodologies.
9. How the ground truth for the training set was established
As there is no mention of a "training set" in the context of machine learning, this information is not applicable and therefore not provided in the 510(k) summary.
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