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PMS8210A Model Code 500 is a multi-parameters monitor used on human patients. The target populations are adult, pediatric and neonatal patients. The PMS8210A Model Code 500 has certain features and functions.

The patient parameters that can be monitored by PMS8210A Model Code 500 are: ECG(3-lead or 5-lead selectable), Heart Rate(HR), Pulse Rate(PR), Respiration Rate(RESP), Non-invasive Blood Pressure (NIBP), Arterial Hemoglobin Oxygen Saturation(SpO2), Temperature (TEMP) and End-tidal CO2 (EtCO2) . Its design allows the operator to adjust the settings of parameter alarms that audibly and visually notify the operator when an excursion occurs.

The PMS8210A Model Code 500 is intended for use in a health care facility setting. It is intended for use by qualified medical personnel trained in the use of the equipment.

The PMS8210A Model Code 500 is not recommended for use in a patient's home or residence, or when it has not been ordered by a physician.

PMS8210A Model Code 500 Patient Monitor is a battery or line-powered patient monitor. The Patient Monitor acquires the physiological signals such as ECG, respiration (RESP), Non-Invasive blood pressure (NIBP), Saturation of pulse oxygen (SPO2), Temperature (TEMP), End-tidal (etCO₂). The signals are converted into digital data and processed, examines the data for alarm conditions and displays the data. The monitor also provides operating control for the user. The submitted device is the same as the predicate with two differences. The new model has added an etCO2 function and the Temp module has been changed. The new Temp module is a previously FDA cleared device.

The provided text describes a 510(k) premarket notification for the PMS8210A (IRIS) Multi-Parameter Patient Monitor Model Code 500. This device is a multi-parameter patient monitor intended for use in healthcare facility settings.

1. Table of Acceptance Criteria and Reported Device Performance:

The document lists various standards and directives to which the device conforms, indicating these as the acceptance criteria. The device's performance is reported as meeting these standards, implying "Same" as the predicate device or conforming to the specified standards without providing specific numerical performance data within the summary itself.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

The provided text does not contain details regarding the sample size used for any test set or the data provenance (country of origin, retrospective/prospective). The document refers to conformance with standards rather than specific clinical trial data with patient samples.

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 in the document. The testing described largely pertains to engineering and performance standards, not clinical validation based on expert-established ground truth.

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

This information is not provided as the document does not describe a setting where expert adjudication of a test set would be applicable (e.g., image interpretation).

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 is not applicable and no such study was reported. The device is a multi-parameter patient monitor, not an AI-assisted diagnostic tool for interpretation by human readers.

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

The document mentions "completed performance testing in accordance with IEC standards" and "Third Party safety testing". This implies standalone testing of the device's functions against recognized standards. However, specific details of "algorithm only" performance for individual parameters are not explicitly detailed beyond conformance. The monitor operates as a standalone device to acquire and display physiological signals.

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

For the performance testing mentioned (e.g., NIBP, SpO2, Temperature, ECG), the ground truth would typically be established by reference measurement devices that are calibrated and traceable to national or international standards. For example, blood pressure measurements would be compared to a validated reference sphygmomanometer, and temperature to a calibrated thermometer. The document does not specify these details but implicitly relies on the methods outlined in the cited IEC and ISO standards.

8. The sample size for the training set:

This information is not provided. The device is a patient monitor, not a machine learning algorithm that typically requires a large training set in the way AI/ML devices do. While it has software of "Moderate concern," this implies software validation, not necessarily a machine learning training dataset.

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

This information is not provided. As noted above, the submission does not detail a machine learning component requiring a training set with established ground truth in the typical sense.

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