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The Maxtec MaxCap Neo and MaxCap Ped are indicated to provide a semi-quantitative visualization of the CO2 in the patient airvay. It is an adjunct in patient assessment to be used in conjunction with other methods to determine clinical signs and symptoms by or on the order of a physician.

Patient Population: MaxCap Neo is intended for use with neonates and infants 250g to 6kg. MaxCap Ped is intended for use with pediatric patients 1kg to 15kg

Environment of Use: Hospital, sub-acute, pre-hospital, transport.

The proposed MaxCap Neo and MaxCap Ped are comprised of several components:

• . Housing with standard 15 mm / 22 mm fittings to connect to the ventilator assist device and a face mask or endotracheal tube
• . Colorimetric litmus media which has been treated with chemical to detect the presence of CO2 by a change in pH

The Maxtec MaxCap Neo and MaxCap Ped devices are semi-quantitative CO2 detectors that use a colorimetric, pH-sensitive dye to visualize CO2 in a patient's airway. The devices are intended for use as an adjunct in patient assessment.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" for each performance metric, but rather lists specifications and performance data for the proposed devices and compares them to predicate and reference devices. We can infer the acceptance criteria from the context of these comparisons and discussions of "similar" or "lower" than predicates.

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

The document describes non-clinical performance testing that was conducted.

• Sample size: Not explicitly stated for each individual test. The statement "We have performed the following performance data and testing for the new models" implies that a sufficient number of devices were tested to evaluate each listed metric.
• Data provenance: Bench testing results performed by Maxtec, LLC. The document does not specify a country of origin for the data beyond that. It is retrospective in the sense that the testing has already been completed and is being reported as part of the 510(k) submission.

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 provided as the device is a colorimetric CO2 detector. The "ground truth" for the performance tests (e.g., CO2 concentration, flow rate, internal volume) would be measured by laboratory equipment and calibrated standards, not by human experts interpreting clinical cases.

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

This information is not applicable as the described studies are non-clinical performance bench tests, not studies involving human interpretation or clinical adjudication.

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:

This information is not applicable. The device is a physical, colorimetric CO2 indicator, not an AI-powered diagnostic tool. Therefore, no MRMC study or AI assistance evaluation was performed.

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

This information is not applicable. The device is a physical, colorimetric CO2 indicator, not an algorithm. The reported performance is based on the device's physical and chemical properties.

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

The ground truth for the non-clinical performance tests (e.g., color change accuracy, flow resistance, internal volume) was established using calibrated laboratory equipment and controlled gas mixtures to represent specific CO2 concentrations and flow rates. This is an objective measurement rather than expert consensus or pathology.

8. The sample size for the training set:

This information is not applicable. The device is a colorimetric indicator and does not involve machine learning or a training set.

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

This information is not applicable as there is no training set for this type of device.

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