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The VitalSense XHR can be used in any setting where physiological body core temperature, skin temperature, or heart rate are used to further the understanding of body function or where quantifiable analysis of temperature or heart rate data is desirable.

The subject device can be classified as physiological signal conditioner as described in 21 CFR 882.1845. VitalSense XHR is a physiological data recorder that is worn on the body. Up to ten sensor inputs may be used with a single VitalSense XHR recorder. The device senses and records physiological data and displays the data on its LCD screen. Recorded data may be transferred later to a PC for display and conversion for export to other programs.

Here's an analysis of the provided text regarding the acceptance criteria and study for the device:

Summary of Device Performance Study

The provided documentation for the Respironics VitalSense XHR (K061870) describes a submission for a modified device based on a predicate device (VitalSense K033534). The core of the study is a demonstration of substantial equivalence to this predicate device by showing that modifications (primarily to software for heart rate display) have no impact on safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly list quantitative acceptance criteria for specific physiological parameters (e.g., accuracy ranges for temperature or heart rate) alongside reported device performance. Instead, it states:

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

• Test Set Sample Size: Not specified. The document refers to "performance tests, risk analysis, and verification and validation testing" but does not give a sample size for human subjects or data points used in these tests.
• Data Provenance: Not specified. The document does not indicate the country of origin of any data or whether the data was retrospective or prospective.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

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, an MRMC comparative effectiveness study was not conducted or described. This device is a physiological data recorder, 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

• Standalone Performance: The documentation implies that "performance tests, risk analysis, and verification and validation testing" were done on the device itself. Given that it's a physiological recorder, its performance fundamentally relates to its ability to accurately sense and record data in a standalone manner. However, specific details of these "performance tests" are not provided. The focus is on the substantial equivalence of the modified software within the existing hardware.

7. The Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated. The document implies that the device's measurements (body core temperature, skin temperature, heart rate) are considered the "truth" for the purpose of demonstrating substantial equivalence to the predicate device. It's likely that established measurement standards or comparison to other validated devices would serve as the ground truth during performance testing, though this is not detailed.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is a physiological recorder, not an AI/ML device that requires a training set in the typical sense. The software modification described (allowing heart rate display) would not involve a machine learning training set.

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

• Ground Truth for Training Set: Not applicable, as no training set for AI/ML is described.

In summary:

The submission for the VitalSense XHR focuses on demonstrating substantial equivalence to an existing predicate device after a software modification. The provided text outlines a regulatory submission process rather than a detailed scientific study with quantitative performance metrics, test set descriptions, or expert involvement in establishing ground truth for a diagnostic algorithm. The "acceptance criteria" are broad statements about meeting regulatory requirements and ensuring the modifications have no impact on safety and effectiveness, rather than specific numerical targets for physiological measurement accuracy.

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VitalSense® can be used in any setting where physiological body core and skin temperature are used to further the understanding of body function. The device can be used for simultaneous assessment of core body temperature, dermal surface temperature, or ambient temperatures that require logging of data over time with subsequent conversion, display, and analysis of the aforementioned parameters. VitalSense® may be used in any instance where quantifiable analysis of temperature data is desirable.

VitalSense® is a compact, ambulatory, physiological data logger. VitalSense® may be attached to the belt or worn in a shirt pocket. Up to ten telemetric sensors may be used with a single VitalSense® logger. The device senses and records physiological data and displays the data on its LCD screen. Recorded data may be transferred later to a PC for display and conversion for export to other programs.

Here's a breakdown of the acceptance criteria and study information for the VitalSense® device based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document does not explicitly state a "test set" in the context of clinical data or human subjects. The tests described are laboratory-based performance verification tests.

• Sample Size: For the stability test, the image description mentions "Two Temperature Probes" (Sensor 24 and Sensor 26). For linearity, the image description also refers to "Sensor_24" and "Sensor_26". The total number of devices or sensors tested across the entire evaluation is not specified beyond these two examples for illustration.
• Data Provenance: The data is generated from internal laboratory testing by Mini Mitter Co., Inc. (as indicated by "Mini Mitter Company has established criteria..." and "Mini Mitter has tested the accuracy..."). The country of origin is the USA, specifically Bend, Oregon, where the company is located. The study is prospective in nature, as it involves active testing of the device's performance.

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

Not applicable. The ground truth for these performance tests is based on objective, quantifiable measurements against established standards and calibration, not expert consensus or interpretation of medical data. The "ground truth" for temperature linearity is a "standard, NIST-traceable digital RTD thermometer."

4. Adjudication Method for the Test Set:

Not applicable. There is no adjudication method used for these non-clinical performance tests, as the results are numerical comparisons against defined thresholds.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

No, an MRMC comparative effectiveness study was not done. The document describes engineering performance tests of the device itself (accuracy and stability of temperature measurement), not studies involving human readers or assessment of human interpretation with or without AI assistance.

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

Yes, the studies described are standalone performance evaluations of the device's ability to accurately and stably measure temperature. The device functions as a data logger, and the tests evaluate its fundamental measurement capabilities directly, without human intervention in the data acquisition or algorithmic processing during the test.

7. The Type of Ground Truth Used:

The ground truth used for these performance tests is based on:

• Reference Standards: For temperature linearity, a "standard, NIST-traceable digital RTD thermometer" served as the reference for ground truth.
• Defined Baseline/Stability: For temperature stability, the expectation of minimal drift over time (e.g., in a stable bath) serves as the ground truth.

8. The Sample Size for the Training Set:

Not applicable. This document describes the verification and validation of a finished device's performance, not the development or training of an algorithm using a dataset. The device is a physiological data logger, not an AI/ML algorithm that requires a training set in the typical sense.

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

Not applicable, as there is no training set for an AI/ML algorithm described.

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