Search Results

The intended use of the Mercury module with Capnostat 5 is to provide: spirometric, and carbon dioxide monitoring in neonatal, pediatric and adult patients during general anesthesia and in the intensive care unit (ICU) and the emergency department (ED). Separate combination CO2/flow sensors are provided for adult, pediatric and neonatal use.

The Mercury module with Capnostat 5 is intended for non-invasive monitoring of the inspired and expired airflow and airway pressure of intention norminds for monthsive non emergency room (ER) patients, as well as capnography in all of these clinical settings. It is intended to serve the same purposes as the flow and carbon dioxide monitoring component of the NICO monitor. The Mercury module with Capnostat 5 is the flow and carbon dioxide monitoring component of the presently 510(k) cleared NICO with MARS monitor. It has been designed to include all of the functionality of the flow and carbon dioxide monitoring components of NCO with CO massurament workilling for a with Capnostat 5 is intended to provide all of the existing flow and CO2 measurement capabilities of the NICO Model 7300 of continuous monitoring of respiratory flow and pressure, and CO2 during anesthesia and intensive care and in the emergency department. The flow sensors connect to a patient airway circuit and provide physiological information to the Mercury module. The parameters directly measured and computed by the module (when connected to a Capnostal 5 sensor) include airway flow and pressure, volume, and CO2. The monitor calculates flow by measuring the pressure drop across a known resistance placed in the breathing circuit. CO2 is measured as the absorption of a known intensity of infrared light by CO2 molecules in the airway.

The provided document does not contain information about the acceptance criteria or a study proving the device meets acceptance criteria. The document is a 510(k) summary for the Mercury Module with Capnostat 5 CO2 Sensor, primarily focusing on its substantial equivalence to a predicate device and its intended use. It does not detail specific performance studies, acceptance criteria, or the methodology for proving these criteria.

Therefore, I cannot populate the requested table or answer the specific questions about sample size, data provenance, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, or training set details based on the provided text.

Ask a specific question about this device

The intended use of the Capnostat 5 CO2 sensor is to provide carbon dioxide monitoring to a host monitoring system during anesthesia / recovery, in the intensive care unit (ICU), and in Emergency Medicine/Transport or Respiratory care.

The Capnostat 5 CO2 sensor is designed for continuous, non-invasive monitoring of carbon dioxide .. Carbon dioxide is measured on-airway using an infrared absorption (IR) technique. The airway adapters are already legally marketed as accessories to the predicate device. The Capnostat 5 CO2 sensor is an integrated microprocessor based data acquisition system consisting of CO2 measurement, control circuitry and a high speed serial interface. The Capnostat 5 CO2 sensor uses SRAM for data storage and an EEPROM to store system parameters. The firmware resides in a PROM. The operations performed by the Capnostat 5 CO2 sensor include data acquisition, parameter calculation, zeroing, heater control and corrections to the CO2 signal for NoO. O2 and barometric pressure.

This 510(k) submission describes a device modification to an existing CO2 sensor, the Capnostat 5 CO2 sensor. The core of the submission is about demonstrating that the modified device is substantially equivalent to its predicate device (Capnostat III sensor in Tidal Wave Sp. Model 710/715 [510(k) K032971]). For devices like this, the "acceptance criteria" are typically related to performance specifications that show the device functions as intended and is safe and effective, similar to the predicate. The study would then demonstrate that these specifications are met.

However, the provided text does not contain a detailed study section with explicit acceptance criteria, results, and specific study parameters like sample sizes, expert qualifications, or ground truth methods. The document is primarily a 510(k) summary, which focuses on describing the device, its intended use, and its technological characteristics in comparison to a predicate device. It confirms substantial equivalence based on these characteristics and mentions that the device is a modification.

Therefore, many of the requested fields cannot be filled directly from the provided text. I will explain why each field cannot be filled, or what can be inferred if any information is available.

1. Table of Acceptance Criteria and Reported Device Performance

Explanation: The document describes the technical function of the CO2 sensor (infrared absorption, beam splitter, pulsed IR source, calibration to a known CO2 concentration) but does not provide specific numerical acceptance criteria or performance results from any studies. For a device modification of this nature, performance data (e.g., accuracy against a known gas mixture, stability over time, performance in different temperature/humidity conditions) would typically be part of the supporting documentation but is not included in this 510(k) summary.

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

• Sample Size: Not provided.
• Data Provenance: Not provided.

Explanation: No specific performance study data, including sample sizes or data provenance (e.g., whether a clinical study was performed, and if so, where and when), is mentioned in this summary.

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)

• Number of Experts: Not applicable/Not provided.
• Qualifications of Experts: Not applicable/Not provided.

Explanation: This type of device (CO2 sensor) typically doesn't use human expert ground truth in the way an imaging AI algorithm would. Its ground truth would be established by reference gas mixtures or another highly accurate, independently verified CO2 measurement device. No details on such a "ground truth" establishment are provided, nor are human experts relevant for this specific device.

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

• Adjudication Method: Not applicable/Not provided.

Explanation: As explained above, human expert adjudication methods are not typically used for establishing ground truth for a CO2 sensor's performance.

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

• MRMC Study: No.

Explanation: This is a hardware CO2 sensor, not an AI diagnostic tool that assists human readers with interpretation. Therefore, a MRMC study is not applicable.

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

• Standalone Performance: Partially yes, but specific study details are not provided.

Explanation: The device itself is designed for standalone measurement of CO2. The description focuses on its technical mechanism (IR absorption, calibration) which implies standalone performance is its primary function. However, no specific "standalone study" with quantifiable results is documented in this summary. The device's performance is inherently "standalone" in how it acquires and processes CO2 data.

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

• Type of Ground Truth: Not explicitly stated, but for this type of device, it would typically be a reference gas analyzer or known, certified CO2 gas mixtures.

Explanation: The text mentions "To calibrate, the photodetector's response to a known concentration of CO2 is stored in the monitor at the factory." This implies that the ground truth for calibration (and by extension, for evaluating accuracy) comes from controlled, known CO2 concentrations.

8. The sample size for the training set

• Sample Size: Not applicable/Not provided.

Explanation: This device is a hardware sensor with embedded firmware, not a machine learning model that undergoes a "training set" in the conventional AI sense. Its calibration is done at the factory with known CO2 concentrations, but this is a calibration process, not a machine learning training process.

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

• Ground Truth Establishment: Not applicable/Not provided in terms of a "training set" for AI.

Explanation: As mentioned above, the concept of a "training set" and associated ground truth establishment for AI models does not directly apply to this device. Its accuracy is established through calibration against known CO2 concentrations, which serves a similar function to ground truth validation in a traditional engineering context.

Ask a specific question about this device