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FilterFlo™ filter is a breathing system filter which is designed to reduce possible airborne or liquid-borne cross contamination with micro-organisms and particulate matter via anesthetic or ventilator breathing systems.

The FilterFlo™ filter may either be used on the patient side or on the device side of the ventilator / anesthetic device and is used as a hygienic measure alternatively to decontamination of breathing system and / or breathing gas conveying parts of the ventilator.

The ThermoFlo™ filter/HME is a breathing system filter and a Heat and Moisture Exchanger. The combination of a filter and a Heat and Moisture Exchanger offer the benefit of both product features. Heat and Moisture Exchangers are used as a conditioning system for mechanically ventilated patients whose upper airways are bypassed. In almost all cases of mechanical ventilation they are a fully valid alternative to heated humidifiers. The product is the only conditioning opportunity of breathing gases in cases of emergency ventilation or during transport since heated humidified are almost impossible to use. The ThermoFlo™ filter/HME should be used with patients who have a Tidal Volume between 250 - 1500 ml.

The products mentioned above are designed as disposable single patient use and should be changed at least every 24 hours.

The ARC Medical filter and filter / HME are standard breathing circuit filters or filter / HMEs.

Here's a breakdown of the acceptance criteria and study information for the ARC Medical FilterFlo™ filter and ThermoFlo™ filter/HME, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not explicitly list numerical "acceptance criteria" for the filters in the way one might see for an AI algorithm. Instead, the submission relies on demonstrating substantial equivalence to predicate devices. The performance criteria are implicitly those met by the predicate devices. The study performed was comparative, showing that the proposed devices performed equivalently to these established predicate devices.

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

The document does not specify a separate "test set" in the context of typical AI algorithm evaluation with patient data. Instead, it refers to "comparative performance testing" of the physical devices.

• Sample Size: Not explicitly stated in terms of the number of devices tested for each characteristic. It would typically involve a statistically relevant number of units for each test (e.g., n=3, n=5, or more, depending on the test standard).
• Data Provenance: This is not applicable in the sense of patient data origin. The testing would have been conducted in a laboratory or manufacturing environment. Based on the manufacturer's location (Tucker, GA, USA), the testing was likely done in the USA. This was a prospective test conducted specifically for this 510(k) submission.

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

This is not applicable as the device is a physical medical device (breathing circuit filter/HME), not an AI algorithm requiring human expert ground truth for image interpretation or diagnosis. The "ground truth" here is objective physical performance measurements according to established test methods.

4. Adjudication Method for the Test Set

This is not applicable as the evaluation involves objective physical performance measurements against established standards or predicate device performance, not subjective expert assessment requiring 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 is not applicable. This device is a physical breathing circuit filter/HME, not an AI-powered diagnostic or assistive tool for human readers. Therefore, an MRMC study and effect size in the context of AI assistance are not relevant.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

This is not applicable. This device is a physical filter/HME, not an algorithm.

7. The Type of Ground Truth Used

The "ground truth" for the tested device performance characteristics (BFE/VFE filtration, resistance to flow, internal volume, HME performance) is based on objective physical measurements obtained through standard engineering and laboratory test methods, calibrated against established industry standards or by direct comparison to the performance of the legally marketed predicate devices.

8. The Sample Size for the Training Set

This is not applicable. There is no "training set" in the context of an AI algorithm for this physical device.

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

This is not applicable as there is no training set for an AI algorithm.

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To reduce gross contamination on breathing circuits by covering the patient end of a breathing circuit with a protective sleeve.

The Circuit Guard is a thin-wall sleeve which when attached to a Filter / HME housing, is pulled over the breathing circuit wye and tubing to cover these items to reduce gross contamination of the circuit components. It is viewed to be similar the intended use of a sleeved suction catheter. This is a modification of our ARC Filter and ARC Filter / HME.

The provided document is a 510(k) summary for a medical device called "Circuit Guard." It describes a modification to an existing device and compares it to predicate devices to establish substantial equivalence.

Based on the information provided, here's a breakdown of the requested details:

Acceptance Criteria and Study to Prove Device Meets Criteria

The submission does not contain details about a study to prove the device meets specific performance acceptance criteria in the traditional sense of a clinical trial or performance validation study. Instead, this 510(k) notification focuses on demonstrating substantial equivalence to existing legally marketed predicate devices. The "acceptance criteria" can be inferred from the attributes compared between the proposed device and the predicate devices, aiming to show that the Circuit Guard is equivalent in terms of use, design, and materials.

1. Table of Acceptance Criteria and Reported Device Performance

Interpretation of "Acceptance Criteria": For a 510(k) submission like this, the "acceptance criteria" are not explicit numerical thresholds from a pre-defined performance study. Instead, they are met by demonstrating that the proposed device shares the same fundamental scientific technology, intended use, and similar design and materials as predicate devices. The "performance" of the Circuit Guard is "reported" as being "Yes" for all these attributes, indicating it meets the implicit equivalence criteria by sharing these characteristics with the predicates.

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

• Sample Size for Test Set: Not applicable. This submission does not describe a "test set" in the context of typical algorithm performance evaluation (e.g., a set of images or patient data used to evaluate AI accuracy). The evaluation is based on a comparison of device characteristics to predicate devices, not on a test of its effectiveness with a specific sample of users or operating conditions.
• Data Provenance: Not applicable. There is no "data" in the sense of patient data, images, or outcomes being evaluated. The comparison is based on the design, intended use, and materials of the device itself and its predicates.

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

• Not applicable. No ground truth was established by experts for a test set. The review process by the FDA involves regulatory experts, but that's different from experts establishing "ground truth" for a device's performance data.

4. Adjudication Method for the Test Set

• Not applicable. There was no test set requiring an adjudication method.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• No. This is a mechanical device, not an AI-powered diagnostic or assistive tool. Therefore, an MRMC study is not relevant and was not conducted.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• No. This device is a physical accessory, not an algorithm.

7. The Type of Ground Truth Used

• Not applicable. The concept of "ground truth" as used in AI/performance evaluation (e.g., pathology, outcomes data) is not relevant here. The "truth" in this context is the documented characteristics and intended use of both the proposed device and its legally marketed predicate devices, which the FDA then reviews for substantial equivalence.

8. The Sample Size for the Training Set

• Not applicable. This is a physical medical device, not an AI algorithm that requires a training set.

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

• Not applicable. As there is no training set, there is no ground truth establishment for it.

In summary: The K982239 submission is a traditional 510(k) premarket notification for a physical medical device. It demonstrates substantial equivalence to predicate devices based on intended use, design, and materials, rather than through performance studies involving patient data, AI algorithms, or expert ground truth establishment for a test set. The "study" referenced is the submission itself, which details the comparison between the new device and existing ones.

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