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The OxyMinder Pro is an oxygen monitor with integrated pressure monitoring intended for continuous monitoring of the concentration of oxygen and pressure being delivered to patients ranging from newborns to adults. This device can be used in the hospital and subacute settings. The monitor is not intended as a life supporting device or for diagnostics.

The OxyMinder Pro is an oxygen and pressure monitor capable of measuring the oxygen concentration from 18 to 100% (cleared under K213948) and pressure from 0 to 60 cmH2O (subject of this submission) that for convenience can be mechanically mounted on to the cleared blender.

The pressure is measured via a disposable pressure tubing that connects from the monitor to an adapter placed in the patient circuit. This sampling line is identical to that cleared in predicate Maxtec K221734. We have only updated the labeling to reflect the name of the sponsor.

As indicated the oxygen monitoring portion has been previously cleared, K213948. It utilizes a cleared oxygen sensor which outputs a voltage to determine the concentration of oxygen. The OxyMinder Pro calibrates at ambient air (21%) and 100% oxygen. The OxyMinder Pro is software controlled. Again, the oxygen monitoring feature and functions are unchanged and previously cleared under reference K213948.

The new pressure monitoring feature utilizes a pressure sensor which measures the pressure within a patient circuit. There is a disposable pressure tubing that connects between the patient circuit and the pressure sensor.

This document, K251245, describes the 510(k) clearance for the OxyMinder Pro, an oxygen and pressure monitor. It uses the Maxtec MaxO2ME+p (K221734) as its predicate device for the combined oxygen and pressure monitoring features, and the Bio-Med Devices OxyMinder (K213948) as a reference for the oxygen monitoring aspects, which were previously cleared.

The core of the submission focuses on the new pressure monitoring feature, as the oxygen monitoring component is largely based on a previously cleared device (OxyMinder, K213948). The acceptance criteria and testing detailed largely pertain to the performance of the device's measurement capabilities and adherence to relevant safety and performance standards.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document describes the performance specifications of the OxyMinder Pro and compares them to the predicate and reference devices. These specifications serve as the acceptance criteria for the device's functionality. The "Reported Device Performance" is implied by the similarity claims and the statement that "The test results met the applicable standards".

2. Sample Size Used for the Test Set and Data Provenance

The document states that "Bench testing was performed" and lists various types of tests (Shelf-life / Aging, Software Verification and Validation, Safety and ElectroMagnetic Compatibility, etc.). It claims that "The test results met the applicable standards". However, the sample size for the test set is not explicitly stated.

The data provenance is not explicitly mentioned as country of origin, nor is it specified if the testing was retrospective or prospective. Given it's bench testing for a device clearance, it is implicitly prospective testing within a laboratory/manufacturing environment.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This type of information (number of experts, their qualifications, and their role in establishing ground truth) is not applicable or not provided for this specific 510(k) submission. Medical device performance testing, especially for devices like oxygen and pressure monitors, relies on calibrated instruments and established physical and electrical standards to determine "ground truth" (e.g., a calibrated gas mixture for oxygen concentration, a pressure calibrator for pressure measurements), not human expert consensus.

4. Adjudication Method for the Test Set

The concept of an adjudication method (like 2+1 or 3+1 used in clinical trials or image interpretation studies) is not applicable here. The "ground truth" for the device's performance (e.g., accuracy of oxygen or pressure readings) is established through comparison to validated and calibrated measurement standards, not through human interpretation that would require adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

An MRMC study is not applicable and was not done for the OxyMinder Pro. MRMC studies are typically used to assess human reader performance, often in diagnostic imaging, with and without AI assistance. The OxyMinder Pro is a direct measurement device; its performance is based on its physical and electrical accuracy, not on human interpretation or an AI algorithm assisting human interpretation.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

The OxyMinder Pro itself is a standalone device. Its performance, as described by its measurement accuracy, resolution, response time, etc., is its standalone performance without a human-in-the-loop influencing the measurement itself. The device is intended to present data to a human for monitoring, but its core function (measurement) is standalone. The testing conducted, as per the "Non-Clinical Testing Summary," assesses the device's inherent performance.

7. Type of Ground Truth Used

The ground truth for the performance testing is established using calibrated instruments and reference standards. For oxygen measurements, this would involve calibrated gas mixtures with known oxygen concentrations. For pressure measurements, this would involve calibrated pressure sources or transducers with known pressure values. It is based on physical and engineering measurements against established standards, not expert consensus, pathology, or outcomes data.

8. Sample Size for the Training Set

The OxyMinder Pro uses an oxygen sensor (galvanic cell) and a pressure transducer. While it is "software controlled," it does not appear to employ machine learning or AI algorithms that would require a "training set" in the conventional sense (i.e., a dataset used to train a model). The software is likely for control, data processing, display, and alarm functions, using fixed algorithms based on physical principles, not learning from data. Therefore, a training set sample size is not applicable.

9. How the Ground Truth for the Training Set Was Established

As there is no indication of a training set in the context of machine learning, this question is not applicable. The device's operation is based on pre-programmed algorithms and calibrated sensor outputs.

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