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The Libre Rio Continuous Glucose Monitoring System is an over-the-counter (OTC) integrated continuous glucose monitoring (iCGM) device indicated for non-insulin using persons age 18 and older. The System detects trends and tracks patterns and aids in the detection of euglycemia, and hypoglycemia. The System is also intended to autonomously communicate with digitally connected devices.

The Libre Rio Continuous Glucose Monitoring System (herein referred to as the 'System') is an integrated continuous glucose monitoring system (iCGM) that provides continuous glucose measurements every minute to facilitate calculation of glucose values accompanied by trend information (glucose arrows) and historical glucose information (glucose graph). The System is intended for over-the-counter use in a home setting. The System consists of the following components: a Sensor which transmits via Bluetooth Low Energy (BLE), and a mobile application Libre Rio App that is downloaded to a compatible smartphone running iOS and Android operating system.

Here are the details regarding 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

The provided document does not explicitly present a table of acceptance criteria with corresponding performance metrics for the Libre Rio Continuous Glucose Monitoring System. It primarily focuses on the device's substantial equivalence to a predicate device and summarizes various performance testing categories. The "Reported Device Performance" for each category usually states that acceptance criteria were met, without detailing the specific metrics or thresholds.

However, based on the type of testing mentioned and common practices for CGM devices, we can infer some general areas where acceptance criteria would have been established and reportedly met:

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

The document does not explicitly state the sample sizes for the test sets used in the listed performance tests. It mentions "studies" and "testing" but does not quantify the number of participants or data points.

The data provenance is not explicitly mentioned for the reported tests. However, the study is for the Libre Rio Continuous Glucose Monitoring System, which appears to be a new device (or an updated version) from Abbott Diabetes Care, Inc., located in Alameda, CA, USA. This suggests the data would likely be generated in the USA, and primarily from prospective testing conducted specifically for this submission, especially for areas like human factors, bench testing for new contraindications, and software validation. The clinical performance data is stated to be derived from the predicate device (K222447), which would have its own provenance details.

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth for any of the mentioned test sets. It broadly refers to "studies" and "testing" but does not detail the methodology of ground truth establishment for specific components like software, cybersecurity, or human factors.

4. Adjudication Method for the Test Set

The document does not specify any adjudication methods (e.g., 2+1, 3+1, none) used for establishing ground truth in the performance testing.

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

No, the document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The device is a continuous glucose monitoring system, which typically involves direct measurement and user interaction, not interpretation by multiple human readers of clinical cases. The "Human Factors" testing mentioned focuses on usability and user interface, not on comparative effectiveness with human interpretation of readings.

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

Yes, implicit in the description of the device and testing, especially for "Software Verification and Validation" and the "Sensor glucose algorithm," is that standalone algorithm performance testing was conducted. The device's "Sensor glucose algorithm" is stated to be the "ADC Glucose Algorithm established for the predicate device," which would have undergone rigorous standalone validation. The "Libre Rio App" is designed to autonomously receive and display glucose data, suggesting the algorithm operates independently of immediate human intervention for value generation. The bench testing of the sensor's compatibility with CT, MRI, and X-ray would also involve objective measurements without human interpretation in the loop to assess the sensor's function.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used for each specific test. However, based on the nature of the device and the tests:

• Clinical Performance (inherited from predicate): For CGM devices, the gold standard for ground truth is typically blood glucose measurements obtained from a laboratory reference method (e.g., YSI analyzer) at various glucose levels.
• Software Verification and Validation: Ground truth would be based on functional specifications and expected outputs for given inputs.
• Cybersecurity: Ground truth involves adherence to security protocols and identified risk mitigations.
• Human Factors: Ground truth is established through user task completion rates, error rates, and subjective feedback against predefined usability goals and safety requirements.
• Bench Testing (CT, MRI, X-ray): Ground truth would be based on physical and electrical performance standards and expected device behavior under specific environmental conditions, potentially using calibrated instruments to verify sensor output accuracy.

8. The Sample Size for the Training Set

The document does not provide information about the sample size for the training set. Given that the sensor glucose algorithm is "ADC Glucose Algorithm established for the predicate device," the training data would be associated with the development of that original algorithm, not necessarily new training for the Libre Rio specifically (unless modifications were made, which is not indicated for the algorithm itself).

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

The document does not specify how the ground truth for the training set (for the inherited "ADC Glucose Algorithm") was established. For glucose monitoring algorithms, ground truth for training data is typically established through paired comparisons with laboratory reference methods (e.g., YSI blood glucose measurements) across a diverse range of glucose values, patient populations, and physiological conditions.

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