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NTID is a handheld vital signs monitor that continuously monitors end tidal carbon dioxide (EtCO2), respiratory rate (RR), oxygen saturation (SpO2), and pulse rate. The unit is intended for monitoring only . The unit transfers history data to PC through a USB adapter. It is for use in any environment where continuous, noninvasive monitoring of these parameters is desired, including hospital and hospital-type facilities. The monitor is intended for use on adult and pediatric patients.

NTID Vital Signs Monitor provides:

• SpO2 monitoring
• Pulse rate (PR) monitoring
• End tidal carbon dioxide (EtCO2) monitoring
• Respiration rate (RR) monitoring
• CO2 and SpO2 waveform display
• Audible and visual physiological and technical alarms
• Trend graph and trend table review
• Alarm event records review
• History data storage
• Rechargeable batteries
• External power supply and charger
  The monitor is intended for monitoring adult and pediatric patients in clinical environments where healthcare is provided by healthcare professionals.
  The product is composed of monitor, SpO2 sensor, Mainstream/Sidestream CO2 sensor, charging base, USB Adapter and PC software.

Here's a breakdown of the acceptance criteria and the study details for the NT1D Vital Signs Monitor, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides specific accuracy criteria for SpO2, Pulse Rate, and EtCO2.

Note: For SpO2 accuracy per decade (70-80%, 80-90%, 90-100%), the NT1D also reports +/- 2%, aligning with its overall 70-100% accuracy claim. The predicate devices did not specify accuracy in these discrete ranges.

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

• SpO2 Module (Clinical Trial for T400A sensor):

  • Sample Size: Ten healthy volunteers.
  • Data Provenance: Prospective, from the Hypoxia Research Laboratory, University of California, San Francisco, USA.

• CO2 Module: The document states that the CO2 module has already obtained 510(k) numbers (K042601 for Capnostat 5 CO2 Sensor and K053174 for LoFlo C5 CO2 Sensor). This implies that the performance of the CO2 module within the NT1D is based on the previously established performance and clearance of these predicate CO2 sensors. Therefore, a new specific clinical test set for the CO2 module with the NT1D is not detailed, as its equivalence is established through the cleared components.

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

The document describes an invasive laboratory test for SpO2 accuracy. For such tests, the "ground truth" for oxygen saturation is typically established using a co-oximeter on arterial blood samples, which is considered the gold standard.

• The study was conducted in a "Hypoxia Research Laboratory."
• While the document doesn't explicitly state the number or qualifications of clinicians involved in sample collection or co-oximeter operation, such laboratory tests are generally performed by trained medical and/or laboratory professionals with expertise in invasive blood gas analysis and co-oximetry, ensuring the accurate establishment of the ground truth. The mention of "co-oximeter sample value" confirms this method.

4. Adjudication Method for the Test Set

Not applicable in the traditional sense for these types of clinical performance tests.

• For the SpO2 accuracy study, the performance is assessed by comparing the device readings against the co-oximeter reference values obtained from arterial blood samples. There isn't a subjective interpretation by multiple experts that would require an adjudication method like 2+1 or 3+1. The process is a direct comparison of quantitative measurements.

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 MRMC study was done. This device is a vital signs monitor, not an AI-assisted diagnostic tool that would typically involve human readers interpreting images or data alongside an AI. The evaluation focuses on the device's accuracy in measuring physiological parameters (SpO2, PR, EtCO2, RR).

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

• Yes, a standalone performance evaluation was done. The clinical trial for the SpO2 module (with the T400A sensor) and the reliance on previously cleared CO2 modules represent standalone performance evaluations. The device's measurement outputs are directly compared to a reference standard (co-oximeter for SpO2, and the previously established accuracy of the predicate CO2 sensors). Human interaction is for device operation and data collection, not for interpretation that influences the device's core measurement output.

7. The Type of Ground Truth Used

• For SpO2: Invasive laboratory testing (in vivo) using co-oximetry on arterial blood samples, which is considered the gold standard for SpO2 measurement.
• For CO2: Ground truth for the CO2 sensors (Capnostat 5 and LoFlo C5) would have been established during their initial 510(k) clearances, likely through comparison to reference gas analyzers or other established methods for CO2 measurement in controlled environments.

8. The Sample Size for the Training Set

• The document does not specify a training set size because this device is a vital signs monitor based on established spectrophotometry (SpO2) and infrared absorption (CO2) principles, not a machine learning or AI-driven algorithm that typically requires a large training dataset. The "training" for such devices is inherent in their physics-based design and calibration.

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

• As mentioned above, there is no explicit "training set" in the context of machine learning. The device's design and calibration are based on fundamental scientific principles and established measurement techniques.
  • For SpO2, the underlying principles of differential light absorption by oxyhemoglobin and deoxyhemoglobin are well-understood and form the basis of the device's algorithms. Calibration would involve using known oxygen concentrations.
  • For CO2, the principle of infrared absorption by CO2 molecules is used. Calibration would involve using gas mixtures with known CO2 concentrations.

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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.

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