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The Assurance™ Alar Sensor is indicated for single patient use for continuous noninvasive monitoring of functional arterial oxygen saturation and pulse rate from the nasal alar of adult and pediatric patients, (weighing >30kg). The sensor can be used in a variety of healthcare environments where compatible pulse oximetry monitors are indicated for use, under professional supervision.

The Assurance™ Alar Sensor is a disposable, single patient use Pulse Oximetry sensor designed to attach to the patient's nasal alar region - the fleshy region at the side of the nose. Skin contact and adhesive free sensor retention is via soft silicone rubber cushions encapsulating the optical components. The Assurance™ Alar Sensor with its associated patient cable, terminates in a DB-9 connector compatible with monitors employing Nellcor OxiSensor II SpO2 technology such as the Nellcor N-395. The sensor utilizes red and IR LED light sources of 660 nm and 890 nm respectively along with a silicon photodiode detector to detect changes in oxygen saturation in the blood. Since oxygen saturated blood absorbs different amounts of light at each wavelength (red and infrared) as compared with unsaturated blood, the amount of light absorbed at each wavelength by the blood in each pulse can be used to calculate oxygen saturation.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, 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

• Sample Size for Clinical Testing (Hypoxia Performance): 12 healthy volunteer subjects.
• Data Provenance: The study was a "Controlled desaturation testing," which implies a prospective, controlled clinical study. The country of origin of the data is not explicitly stated, but the context of an FDA submission suggests it was likely performed in the US or under standards accepted by the FDA.

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

• The document mentions "Reference blood samples were drawn from an indwelling arterial catheter and analyzed on a Co-oximeter." This indicates that the ground truth for SpO2 was established through laboratory analysis by a Co-oximeter, which is a highly accurate method for blood gas analysis, rather than expert interpretation of images or other subjective assessments. Therefore, the concept of a "number of experts" for ground truth as might apply to image-based diagnostics is not directly applicable here.

4. Adjudication Method for the Test Set

• Not applicable as the ground truth was established by direct measurement with a Co-oximeter on blood samples, not through expert consensus or interpretation requiring adjudication.

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

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or indicated. This type of study is more common for diagnostic imaging AI, where human readers interpret cases with and without AI assistance. This submission focuses on a sensor's accuracy compared to a gold standard.

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

• Yes, the hypoxia performance testing describes a standalone evaluation of the device. The sensor was connected to monitors, and the SpO2 values were recorded. The statistical analysis of the data pairs yielded the device's accuracy (Arms) independently, without human interpretation influencing the primary SpO2 measurement recorded by the device.

7. The Type of Ground Truth Used

• The type of ground truth used for SpO2 accuracy was outcomes data/reference standard measurement obtained from Co-oximeter analysis of arterial blood samples (SaO2).

8. The Sample Size for the Training Set

• The document does not report a separate training set size for the Assurance™ Alar Sensor. This device is a pulse oximeter sensor, which typically relies on established biophysical principles and calibration during manufacturing rather than machine learning algorithms that require extensive training data in the same way an AI diagnostic tool would. The accuracy is likely inherent to its design and calibration, not learned from a dataset.

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

• Not applicable, as no training set for a machine learning algorithm is mentioned. The device's operation is based on spectrophotometric principles, not a learned model from a training set.

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