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The Chartis System is indicated for use by bronchoscopists during a diagnostic bronchoscopy in adult patients in a bronchoscopy suite. The system, composed of the Chartis Catheter and Chartis Console, is designed to measure pressure and flow in order to calculate resistance to airflow and quantify collateral ventilation in isolated lung compartments. The Chartis Catheter is used through the working channel of a bronchoscope and connects to the Chartis Console. The Chartis Console is a re-useable piece of capital equipment that displays the patient information.

The Chartis Console is an integrated, self-contained, 12 VDC powered system designed to be used in the bronchoscopy suite in conjunction with the Chartis Catheter. The proximal end of the Chartis Catheter is attached to a polymer tube with a filter whose opposite end is attached to an input fitting on the Chartis Console. The balloon on the Chartis Cathter isolates the lung compartment of interest. The hardware components of the Console translate air flow and pressure detected through the Chartis Catheter into electrical signals. The Console analyzes and displays airflow and pressure from the isolated lung compartment in real time.

This 510(k) submission (K111764) describes a
Special 510(k) for the Chartis™ Console,
which is a modification of an already cleared device (K083199). As such, the submission focuses on demonstrating substantial equivalence to the predicate device, rather than proving novel clinical effectiveness through extensive new studies.

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

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of a Special 510(k) for device modifications, the "acceptance criteria" here refer to demonstrating that the modified device performs equivalently to the predicate device and that the changes do not raise new questions of safety or effectiveness. The submission explicitly states:

"The modified Chartis Console is substantially equivalent to the predicate Chartis Console with regard to technological characteristics. The minor changes to graphic display, hardware, software, packaging and service/calibration do not raise new types of safety or effectiveness questions."

"Verification and validation test results support the performance characteristics of the modified device and show equivalence to the currently marketed predicate device."

Note: The document does not provide specific numerical performance metrics, such as accuracy, sensitivity, or specificity, as it's a Special 510(k) focused on demonstrating equivalence of modifications to an already cleared device. The "performance data" mentioned are likely internal verification and validation testing to ensure the modified components function as intended and do not degrade the overall system's performance compared to the predicate.

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

The provided document does not specify a sample size for a "test set" or detail data provenance in the context of a clinical study or a comparative study against a ground truth. The submission refers to "verification and validation test results," which are typically internal engineering and performance tests on the device itself, rather than clinical data from patients.

This type of submission often relies on:

• Engineering bench testing: To confirm the functionality and accuracy of the modified hardware and software components.
• Software validation: To ensure the software changes do not introduce errors and perform as designed.
• System integration testing: To verify that all components work together correctly.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The provided document does not mention the use of experts to establish ground truth for a test set. This is consistent with a Special 510(k) for device modification, where the focus is on the device's functional equivalence rather than a new clinical claim requiring expert-adjudicated clinical endpoints.

4. Adjudication Method

The document does not specify an adjudication method. This is because the submission does not detail a clinical study where expert review and adjudication would be necessary to establish a "ground truth" for patient cases.

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

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. Such studies are typically performed for devices that involve human interpretation (e.g., imaging devices) to assess the impact of AI on human reader performance. The Chartis Console is a diagnostic spirometer that measures physical parameters (pressure, flow) and displays them, not a device requiring human interpretation of complex visual data in the same way an imaging AI would.

6. Standalone (Algorithm Only) Performance

The document does not present data on standalone (algorithm only without human-in-the-loop) performance. The Chartis Console is described as displaying real-time data for a bronchoscopist, implying it's always used with a human in the loop for clinical decision-making. The device itself is the "algorithm" that calculates and displays pressure and flow, so its "standalone performance" would be its accuracy in measuring and calculating these physical parameters. The "verification and validation test results" likely cover this aspect without detailing specific metrics in the summary.

7. Type of Ground Truth Used

The type of ground truth used here would be physical and engineering measurements to confirm the accuracy and calibration of the device's sensors and algorithms for measuring pressure and flow, and its ability to quantify collateral ventilation. It would not be expert consensus, pathology, or outcomes data, as these are typically associated with clinical efficacy studies for new devices or new indications.

8. Sample Size for the Training Set

The document does not specify a sample size for a training set. This type of device, which measures physical parameters, typically does not rely on machine learning or AI models that require extensive training data in the same way an image recognition algorithm would. The "algorithm" here is likely based on established physiological principles and formulas for calculating resistance and flow, embedded in the software.

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

Since there is no mention of a training set or specific machine learning algorithms that require one, the document does not describe how ground truth for a training set was established. The "ground truth" for the device's functionality would be established through engineering standards, calibration procedures using known accurate references, and verification against physiological models or simulations.

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