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The device measures the exhaled breath of a patient, specifically FEV1. The results can be used for the diagnosis of COPD in adult smokers.
The device measures expiratory lung function parameters, specifically FEV1 and conforms to ATS standards for diagnostic devices. The results obtained can be used for screening of COPD in adult smokers.

The PulmoLife is a battery operated hand held portable spirometer. It uses a uni-directional, rotating vane, flow sensing turbine to measure lung function parameters, specifically Forced Expiratory Volume in the 1st second of expiration (FEV1) and FEV1 as a percentage of the predicted value (FEV1 %predicted). Lung age estimation is also given. It is supplied with one way valve safety mouthpieces, carrying pouch and instruction manual.

Here's an analysis of the provided 510(k) summary regarding the PulmoLife device, structured to answer your specific questions.

Important Note: The provided document is a 510(k) summary and the FDA's clearance letter. It primarily focuses on demonstrating substantial equivalence to a predicate device and outlining the device's basic information. It does not contain the detailed study results, acceptance criteria, or ground truth establishment that would typically be found in a comprehensive clinical study report. Therefore, many of your questions cannot be answered directly from the provided text.

Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

1. A table of acceptance criteria and the reported device performance

The summary states, "The device measures expiratory lung function parameters, specifically FEV1 and conforms to ATS standards for diagnostic devices." This implies that the device is expected to meet the accuracy and precision requirements set forth by the American Thoracic Society (ATS) standards for spirometry. However, the specific numerical acceptance criteria (e.g., accuracy within X% or Y liters) and the reported device performance against those criteria are not provided in this 510(k) summary.

Without the actual study report, a table cannot be constructed with specific numerical data.

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

This information is not provided in the 510(k) summary. A mention of a clinical study or performance data is absent, other than the general statement about conforming to ATS standards.

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 510(k) summary. For spirometry, "ground truth" often refers to measurements obtained from highly accurate laboratory spirometers.

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

This information is not provided in the 510(k) summary.

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

The PulmoLife is a diagnostic spirometer, not an AI-assisted diagnostic tool for image interpretation or similar. Therefore, an MRMC study as typically understood in AI/imaging is not applicable here, and no such study is mentioned or implied. The device measures lung function directly.

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

The PulmoLife is a standalone device in that it measures FEV1 directly. The "algorithm" here would be the processing of the flow signal to calculate FEV1. There isn't a human interpreting an "output" from the device in the same way an AI system might provide a result that a human then reviews. The performance of the device itself in generating the FEV1 measurement would be the focus. The summary implies that "standalone" performance was the focus, as it claims conformance to ATS standards for "diagnostic devices."

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

For spirometry, ground truth would typically be established by:

• Reference spirometers: Highly calibrated and accurate laboratory spirometers used to validate the measurements of the new device.
• Simulators: Mechanical or electronic simulators that produce known flow and volume signals.

The summary itself does not specify the ground truth mechanism used, but it's implied to be based on physical measurements and comparisons with established methods (ATS standards).

8. The sample size for the training set

This information is not provided in the 510(k) summary. For a hardware device like a spirometer, there might not be a "training set" in the machine learning sense. The device's calibration and validation would be based on engineering principles and comparison to reference standards.

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

As above, a "training set" in the AI sense is unlikely to apply directly to this device. The establishment of ground truth for device calibration and validation would involve highly accurate physical measurements or simulations, conforming to spirometry standards. The specifics are not detailed in the provided document.

Summary of Limitations based on the provided document:

The provided 510(k) summary is very high-level and serves the purpose of demonstrating substantial equivalence to a predicate device. It does not include the detailed clinical or validation study reports that would contain the specific acceptance criteria, performance data, sample sizes, ground truth methodologies, and expert qualifications for a rigorous assessment. To obtain this level of detail, one would typically need to refer to the full 510(k) submission or any referenced performance studies, which are not part of this snippet.

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The Nexsystem ambulatory patient monitoring system is intended for use in the home or alternate care settings. It consists of a patient-worn sensor (Nexisensor), signal transfer unit (Nexender), communications module (Nextation), and a telemonitoring station computer based display and storage system (TMS) located at the health care professional's facility. The device stores and transmits ECG data, respiratory data, skin temperature, systolic and diastolic blood pressure (Non-invasive), and PEF and FEV1.

The Nexsystem is not for use as a critical care monitoring system. The Nexsystem is not an apnea monitor.

The Nexystem is a non-invasive ambulatory patient monitoring system for recording multiple physiological parameters from patients who may be located at home or in an alternate care setting. The Nexystem continuously gathers physiological data from a sensor band attached to the patient and transmits the data wirelessly via a Signal Transfer Unit (Nexender) to a Base Station Unit (Nextation) where the data are recorded and stored. Additionally, the Base Station Unit has interfaces for auxiliary sensors - spirometer and blood pressure monitor - for recording point in time lung function and blood pressure measurements. A Call Discriminator Unit enables incoming telephone calls to be correctly routed to either the Nextation or a telephone handset.

The data are transmitted for display, monitoring and storage on a computer (Telemonitoring Station TMS running the Nexoft application software) at a distant location (Telemonitoring Centre). This data transfer is under the control of the Health Care Professional (HCP) at the TMS. Data may be transferred in real time to enable the HCP to check on the quality of the physiological data being recorded and/or the status of the patient. Normally data is transferred at a scheduled time after the end of a patient data recording session. Once transferred to the TMS the data can subsequently be displayed for analysis by the HCP. The Nexystem enables the HCP to print reports of raw or summary data.

The Nexystem comprises a number of system components identified below.

Here's a summary of the acceptance criteria and study information for the Nexystem based on the provided K993643 Premarket Notification:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly define acceptance criteria in terms of numerical thresholds for all evaluated parameters. Instead, it relies heavily on compliance with recognized standards (ANSI/AAMI EC12 and EC38) and direct comparison to predicate devices, highlighting where the Nexystem meets or exceeds predicate performance.

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

The submission explicitly states: "14.7 Clinical performance data for equivalence: Not applicable". This indicates that no separate clinical test set of patient data was used to demonstrate the performance of the Nexystem in a clinical setting compared to a ground truth established from patient data. The primary method of demonstrating equivalence was through non-clinical testing against standards and direct comparison of specifications with predicate devices.

Therefore, for a clinical test set:

• Sample size: Not applicable.
• Data Provenance: Not applicable.

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

Since no clinical test set was utilized for demonstrating performance against a "ground truth" derived from patient data, this information is not applicable. The "ground truth" for the non-clinical tests was defined by the requirements of the standards (ANSI/AAMI EC12 and EC38) and the specifications of the predicate devices.

4. Adjudication Method for the Test Set

As no clinical test set with human-established ground truth was used, an adjudication method is not applicable.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. The submission explicitly states "Clinical performance data for equivalence: Not applicable". Therefore, no MRMC study was conducted.

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

Yes, in a way. The performance evaluation primarily involved standalone testing of the device's technical specifications and compliance with recognized standards. This is akin to standalone performance for the various physiological data acquisition and transmission functions. For example:

• Compliance testing of the Nexisensor to ANSI/AAMI EC12 for disposable ECG electrodes.
• Compliance testing of the Nexystem to ANSI/AAMI EC38 for ambulatory electrocardiographs.
• Verification of heart rate range and accuracy, respiration range and accuracy, and temperature range and accuracy based on the device's internal functionality.
• The use of legally marketed and separately cleared devices for blood pressure (Omron IC) and spirometry (Micromedical MicroPlus) implies their standalone performance was relied upon.

However, it's important to note this is not a standalone performance of an AI algorithm, as this device does not appear to incorporate AI for diagnostic purposes. It's a standalone performance demonstration of monitoring hardware and software capabilities.

7. The Type of Ground Truth Used

The ground truth used in this submission is multifaceted and based on:

• Industry Standards: The requirements and specifications outlined in ANSI/AAMI EC12 (Disposable ECG Electrodes) and ANSI/AAMI EC38 (Ambulatory Electrocardiographs).
• Predicate Device Specifications: The technical parameters and performance characteristics of the legally marketed predicate devices (Sabratek PHMS, Propaq Encore, Mortara Ambulatory X-12 Telemetry Module, Reynolds Tracker/Pathfinder, Instromedix Poseidon Cardiac Monitoring System). The Nexystem demonstrated "substantial equivalence" by meeting or exceeding these established benchmarks.
• Functional Testing: The inherent design specifications and measured performance of the Nexystem components (e.g., heart rate accuracy, temperature accuracy) as validated by engineers/testers against known input signals or reference devices.

8. The Sample Size for the Training Set

This information is not applicable. The Nexystem is an ambulatory patient monitoring system that primarily collects and transmits physiological data; it is not described as an AI-powered diagnostic or analysis device that would require a "training set" in the context of machine learning. The focus is on hardware and software functionality and compliance with performance standards for data acquisition and transmission.

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

As no training set (in the context of machine learning/AI) was used, this information is not applicable.

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