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The SleepCheck is intended for use in monitoring nasal and oral airflow. The device is intended for use as a prescreening tool to determine the need for clinical diagnosis and evaluation by polysomnography based on the patient's test score.
The SleepCheck is a small monitor designed to assess nasal and oral airflow. Apnea breathing events are counted based on a reduction in airflow. The device is intended for use as a screening device to determine the need for clinical diagnosis and evaluation by polysomnography based on the patient's score.

The SleepCheck is a pager-sized monitor that clips onto the patient's nightshirt. The monitor utilizes an air cannula sensor to capture the patient's nasal and oral airflow. The sensor cannula, containing 3 airflow ports, is placed under the nostrils, above the lip and held in place around the ears with a lanyard. The unit has an LCD readout that displays the total number of apneas and the apnealhypopnea index (AHI). The LCD also flashes a warning if the sensor is improperly applied or displaced. The LCD also provides a dynamic bar graph that displays the breathing pattern. As the patient inhales and exhales, the bar moves back and forth, verifying proper sensor application and monitor operation. When breathing decreases or is interrupted for 10 seconds or longer, this event is considered an apnea episode and is tallied on the LCD readout. The patient self-applies the unit before going to bed and wears the unit through the night. Upon waking, the patient will take off the unit and return it to the physician. The physician will check the LCD to learn the number of apneas and the rate of apnea events (AHI) that occurred throughout the night. The technical validity of the test is also indicated.

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

Acceptance Criteria and Device Performance

Note: The document only explicitly states "high sensitivity and specificity" as a general goal within the "Assessment of SleepCheck Performance Data" section. The predicate device's performance (SleepStrip) serves as an implicit benchmark for what constitutes an acceptable level of performance in the market. The SleepCheck's performance significantly exceeds that of the predicate device for all reported metrics, suggesting it meets or exceeds an implicit acceptance standard.

Study Information

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

• Sample Size: 111 sleep tests, totaling 151 sleep nights.
• Data Provenance: Not explicitly stated whether retrospective or prospective, or country of origin for the patient data. However, the studies were "sponsored by the National Institutes of Health" and conducted "both at-home and in the sleep laboratory," suggesting a U.S.-based context and potentially a mix of prospective and retrospective elements depending on study design.

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

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

4. Adjudication Method for the Test Set:

• Not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No, a MRMC comparative effectiveness study was not conducted with human readers comparing performance with and without AI assistance. The study focuses on the standalone performance of the device against a gold standard.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance:

• Yes, a standalone study was done. The reported performance metrics (Sensitivity, Specificity, r to PSG) are for the "SleepCheck" device directly, indicating its algorithmic performance in detecting apnea events. The physician only reads the LCD display of the device's calculated results.

7. Type of Ground Truth Used:

• Polysomnography (PSG): PSG is explicitly stated as the "gold standard" against which the SleepCheck was validated.

8. Sample Size for the Training Set:

• Not specified. The document describes clinical studies used for validation, but does not differentiate or provide details on any specific training set for the algorithm's development.

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

• Not specified. Assuming typical device development, ground truth for any training would also be established through expert interpretation of PSG data, similar to the validation set. However, this is not detailed in the provided text.

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