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510(k) Data Aggregation
(336 days)
VIASYS RESPIRATORY CARE INC.
The Micro Diary Spirometer is used in pulmonary function testing to measure the volume of gas moving in or out of a patient's lungs. Specifically, the Micro Diary Spirometer measures the following lung function parameters: FEV1, FVC, FEV6 and PEF. The device also records the test data for later review and has the ability to transfer these test records to a compatible computer.
The Micro Diary Spirometer is a compact, hand held, and battery operated recording spirometer. The device features a five button keypad and an LCD display. The device utilizes a digital volume transducer. As a patient's exhaled breath is passed through the transducer, the vane rotates. The number of rotations is proportional to the volume of air passed through the transducer, and the frequency of rotation is proportional to the flow rate.
Here's an analysis of the acceptance criteria and study information for the Micro Diary Spirometer, based on the provided text:
Acceptance Criteria and Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
FEV1 Accuracy | Passed |
FVC Accuracy | Passed |
FEV6 Accuracy | Passed |
PEF Accuracy | Passed |
Electrical Safety | Passed |
EMC Testing | Passed |
Note: The document states that the device was tested to assure it "accurately measures lung function across the following standard spirometric measures: FEV1, FVC, FEV6, and PEF." It then explicitly states "All tests were passed successfully." No specific numerical thresholds for accuracy are provided in this summary.
Study Information
The provided document describes a non-clinical study to demonstrate substantial equivalence to a predicate device. No clinical studies were conducted or submitted.
2. Sample size used for the test set and the data provenance:
- Sample Size for Test Set: Not explicitly stated. The document mentions "All tests were passed successfully," suggesting an adequate number of tests were performed to demonstrate compliance with accuracy requirements for the measured parameters (FEV1, FVC, FEV6, PEF).
- Data Provenance: Not explicitly stated, though being a non-clinical test, it would have been generated in a laboratory setting, likely in the US where the company is based.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. This was a non-clinical test comparing the device's measurements against established standards for spirometric accuracy, not against expert interpretation of medical images or data.
4. Adjudication method for the test set:
- Not applicable. As a non-clinical test, there was no expert adjudication involved. The device's measurements were compared against objective reference values from a calibrated system.
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:
- No. An MRMC study was not conducted as this is a diagnostic spirometer, not an AI-assisted diagnostic tool for interpretation.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
- Yes, a standalone non-clinical study was performed. The spirometer's accuracy was tested independently in a laboratory setting against objective standards for FEV1, FVC, FEV6, and PEF.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Objective reference values / calibrated standards. For non-clinical spirometry testing, the ground truth is established by highly accurate and calibrated equipment that simulates lung function parameters or directly measures them to a high degree of precision.
8. The sample size for the training set:
- Not applicable. This device is not an AI/ML algorithm that requires a training set in the conventional sense. Its function is based on physical measurement principles (turbine rotation) and engineering design.
9. How the ground truth for the training set was established:
- Not applicable. As mentioned above, there is no "training set" for this device.
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(87 days)
VIASYS RESPIRATORY CARE INC
The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should only be operated by properly trained clinical personnel, under the direction of a physician.
The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its graphical user interface module (UIM) has a flat panel color LCD with real time charting and digital monitoring capabilities, a touch screen for interaction, membrane keys and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. Using internal batteries this provides inter-hospital transport as well as back up capability due to loss of AC power. The AVEA may be configured as a conventional ventilator or non-invasive positive pressure ventilator (NPPV). It has been designed to function using commonly available accessories.
The provided text does not contain the information requested for acceptance criteria and the study that proves the device meets the acceptance criteria.
The document is a 510(k) premarket notification summary for modifications to the AVEA Ventilator, specifically the addition of a capnometry monitor and onboard barometric pressure sensor. While it mentions "Performance testing verified that the AVEA Ventilator meets it's performance requirements," it does not provide any specific criteria, reported performance values, or details about the studies conducted.
Therefore, I cannot populate the table or answer the specific questions about sample size, data provenance, expert involvement, adjudication methods, MRMC studies, standalone performance, ground truth details, or training set information.
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(58 days)
VIASYS RESPIRATORY CARE INC
The AVEA is intended to provide continuous respiratory support in an institutional health care environment (e.g. hospitals). It may be used on adult, pediatric, and neonatal patients. It should only be operated by properly trained clinical personnel, under the direction of a physician.
The AVEA is a servo-controlled, software-driven ventilator. It has a dynamic range of breathing gas delivery that provides for neonatal through adult patients. Its graphical user interface module (UIM) has a flat panel color LCD with real time charting and digital monitoring capabilities, a touch screen for interaction, membrane keys and a dial for changing settings and operating parameters. It also has an internal gas delivery system with servo controlled active inhalation and exhalation functions. Using internal batteries this provides inter-hospital transport as well as back up capability due to loss of AC power. The AVEA may be configured as a conventional ventilator or non-invasive positive pressure ventilator (NPPV). It has been designed to function using commonly available accessories.
This is a 510(k) premarket notification for a medical device (AVEA Ventilator), not a study report. Therefore, much of the requested information regarding acceptance criteria and study details for device performance, ground truth, and expert evaluation is not available in the provided text.
However, based on the submission, here's what can be extracted and inferred:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state quantitative acceptance criteria or detailed reported device performance in a table format. It states generally that: "Performance testing verified that the AVEA Ventilator meets it's performance requirements and that this device is substantially equivalent to medical devices currently legally marketed in the United States."
The focus of this 510(k) is on modifications to an already cleared device, asserting substantial equivalence. The specific modifications are:
- "Software update encompasses a modification to the current . Non-Invasive ventilation previously cleared under K013642 of which allows for a particular mode on the Infant Flow Plus, Nasal CPAP."
- "This Nasal CPAP mode that is emulated is accomplished by a software modification only, utilizing existing AVEA hardware."
- "This Nasal CPAP mode is only for single level continuous positive airway pressure to nasal pronos."
Therefore, the "acceptance criteria" for this specific submission would implicitly be:
- The modified AVEA Ventilator, with the new Nasal CPAP software, performs equivalently to the predicate devices for continuous ventilation and specifically for the Nasal CPAP mode.
- The software modification does not adversely affect existing validated functionalities.
Given the nature of a ventilator, performance metrics would likely revolve around aspects such as:
- Accuracy of delivered pressure/volume/flow.
- Response time to patient effort.
- Safety alarms and limits.
- Biocompatibility (not directly covered by the software modification but generally a part of medical device approval).
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document does not provide details on the sample size for any specific test set, nor the data provenance (country of origin, retrospective/prospective). The submission refers to "Performance testing" in a general sense.
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. Since this is an engineering performance assertion for a ventilator and not a diagnostic device relying on expert interpretation of images or signals, the concept of "ground truth" derived from expert consensus in that context is unlikely to apply directly in the same way. Performance testing for a ventilator typically involves engineering and physiological measurements rather than expert clinical consensus on data.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable/not provided. Adjudication methods like 2+1 or 3+1 are typically used in studies involving expert interpretation where disagreements need to be resolved for ground truth establishment. This is not the type of study described.
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
Not applicable. This is a ventilator device, not an AI-assisted diagnostic tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
This concept doesn't directly apply as the AVEA Ventilator is a device that interacts with a patient and is operated by trained clinical personnel. The "performance testing" mentioned would be for the device's functionality, which by its nature is "standalone" in terms of its mechanical and software operation, but in a real-world scenario, it's always "with human-in-the-loop" for patient management. The document states it is "servo-controlled, software-driven."
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Given the device type, "ground truth" would likely be derived from:
- Engineering specifications and validated measurement equipment (e.g., flow sensors, pressure transducers) for evaluating the ventilator's physical outputs.
- Physiological models or test lung systems to simulate patient conditions.
- Compliance with recognized standards (e.g., ISO standards for ventilators).
However, the specific methods are not detailed in the provided text.
8. The sample size for the training set
Not applicable. This device is not described as utilizing a machine learning model that requires a distinct "training set" in the context of AI/ML. The software modification is likely a rule-based or control-algorithm update, not an autonomously learning system.
9. How the ground truth for the training set was established
Not applicable, as there is no mention of a machine learning training set.
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