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Libre 2 Sensor users:

The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

Libre 2 Plus Sensor users:

The FreeStyle Libre 2 Flash Glucose Monitoring System is a continuous glucose monitoring (CGM) device with real time alarms capability indicated for the management of diabetes in persons age 2 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The System can be used alone or in conjunction with these digitally connected devices for the purpose of managing diabetes.

Libre 3 Sensor users:

The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes in persons age 4 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices. The System can be used alone or in conjunction with these digitally connected devices where the user manually controls actions for therapy decisions.

Libre 3 Plus Sensor users:

The FreeStyle Libre 3 Continuous Glucose Monitoring System is a real time continuous glucose monitoring (CGM) device with alarms capability indicated for the management of diabetes in persons age 2 and older. It is intended to replace blood glucose testing for diabetes treatment decisions, unless otherwise indicated.

The System also detects trends and tracks patterns and aids in the detection of episodes of hyperglycemia and hypoglycemia, facilitating both acute and long-term therapy adjustments. Interpretation of the System readings should be based on the glucose trends and several sequential readings over time.

The System is also intended to autonomously communicate with digitally connected devices, including automated insulin dosing (AID) systems. The System can be used alone or in conjunction with these digitally connected devices for the purpose of managing diabetes.

The FreeStyle Libre 3 Continuous Glucose Monitoring System (hereinafter also referred to as 'FSL3 System') and FreeStyle Libre 2 Flash Glucose Monitoring System (hereinafter also referred to as the 'FSL2 System') are integrated continuous glucose monitoring (iCGM) systems that provide continuous glucose measurements every minute to provide glucose levels, trends, and real-time alarms capability to aid in the management of diabetes. The FSL2 and FSL3 Systems also provide configurable alarms designed to warn the user of Low Glucose, High Glucose or Signal Loss. The user may make treatment decisions based in part on the sensor glucose results provided by both Systems. The FSL2 and FSL3 Systems require a prescription and are intended for home use.

The subject FSL3 System consists of a sensor (the FreeStyle Libre 3 Sensor (FSL3 Sensor) or FreeStyle Libre 3 Plus Sensor (FSL3 Plus Sensor)) and a primary display device (the FreeStyle Libre 3 Reader (FSL3 Reader) or the FreeStyle Libre App (FSL App) [iOS and Android] downloaded to a compatible phone). The FSL3 Reader and the FSL App do not interact with each other.

The subject FSL2 System consists of a sensor (the FreeStyle Libre 2 Sensor (FSL2 Sensor) or FreeStyle Libre 2 Plus Sensor (FSL2 Plus Sensor)) and a primary display device (the FreeStyle Libre 2 Reader (FSL2 Reader) or the FSL App (iOS and Android) downloaded to a compatible phone). The FSL2 Reader and FSL App do not interact with each other.

Both the FSL2 and FSL3 Systems are compatible with the Libre Data Sharing API cleared in K223537. The display device of the connected FSL2 or FSL3 Systems, which directly receives the data from the sensor, continues to serve as a primary display device for the glucose data and alarms.

This document is a 510(k) summary from Abbott Diabetes Care Inc. regarding their FreeStyle Libre 3 Continuous Glucose Monitoring System and FreeStyle Libre 2 Flash Glucose Monitoring System. It outlines the device details, indications for use, comparison to predicate devices, and a summary of performance testing.

Acceptance Criteria and Study Proving Device Meets Criteria

The provided text focuses on the substantial equivalence of the new FreeStyle Libre 2 Plus and 3 Plus sensors and updated app features to their predicate devices, rather than establishing new acceptance criteria for the entire system's glucose monitoring accuracy. The document states that clinical performance and human factors were "established in the predicate device (K223435) and are not affected by the introduction of the FSL App in this 510(k)." This implies that the glucose accuracy and user interface performance metrics (e.g., MARD, % in Zones A+B, usability outcomes) for the core glucose monitoring function were previously accepted for the predicate devices and are assumed to hold true for the current submission due to the nature of the changes being primarily related to sensor life, age ranges, and app features.

Therefore, the acceptance criteria and performance data discussed below are extrapolated from the types of performance studies conducted to demonstrate substantial equivalence for the modifications presented in this 510(k), particularly regarding safety and compatibility, and from the implied reliance on the predicate device's established clinical performance.

Please note: This response does not contain specific quantitative acceptance criteria or precise performance metrics for glucose accuracy (e.g., MARD values) as these details are explicitly referred to as having been established in the predicate device (K223435) and are not re-evaluated in this submission. The focus of this 510(k) is on the changes from the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of this 510(k) (modifications to existing systems), the presented "acceptance criteria" are derived from the tests conducted to demonstrate that the changes do not adversely affect safety and effectiveness.

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

The document does not provide specific sample sizes for the detailed technical tests (Software V&V, Cybersecurity, Bench Testing for CT/MRI/X-ray). These types of tests typically involve controlled laboratory environments rather than human subject test sets in the traditional sense for clinical performance.

For the core clinical performance (glucose accuracy, etc.), the document explicitly states these characteristics were "established in the predicate device (K223435) and are not affected." Therefore, any sample size for these fundamental performance aspects would refer to those used in the studies for the predicate device, which are not detailed in this 510(k) summary.

Data Provenance: The document does not specify the country of origin for any testing data. The nature of the tests (Software V&V, Cybersecurity, Bench Testing) suggests they are likely conducted in a controlled environment, potentially in-house or by a certified testing facility. The "retrospective or prospective" nature is not specified, but these are typically prospective tests conducted as part of the development and verification process.

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

This type of information (number of experts, qualifications, and ground truth establishment) is typically relevant for clinical studies involving expert adjudication (e.g., for diagnostic imaging devices). Given that the 510(k) summary indicates clinical performance was "established in the predicate device" and that the current submission primarily addresses technical and software improvements/modifications, there's no mention of a new clinical study involving multiple experts to establish ground truth for this specific submission.

For the bench testing related to CT/MRI/X-ray compatibility, ground truth would be established by physical measurements and functional checks against engineering specifications, not by human experts.

4. Adjudication Method for the Test Set

Adjudication methods (e.g., 2+1, 3+1) are typically used in clinical studies, particularly for diagnostic accuracy where multiple human readers interpret data. As explained above, this 510(k) focuses on technical and software changes, and refers back to the predicate device for clinical performance. Therefore, no information on an adjudication method for a test set is provided or expected in this context.

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

No, an MRMC comparative effectiveness study was not done, and it is not typically relevant for a CGM device. MRMC studies are primarily conducted for diagnostic imaging devices to assess how AI assistance impacts human reader performance (e.g., radiologists interpreting scans). CGM devices provide quantitative glucose measurements, and their performance is evaluated directly against a reference method (e.g., laboratory glucose measurements), not through human reader interpretation of complex images or cases.

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

Yes, for the core glucose algorithm, a standalone performance evaluation is inherent to a CGM device. The document explicitly states:

• Glucose Algorithm: "ADC Glucose Algorithm"
• Location of Glucose Algorithm: "FSL2 System: Receiver", "FSL3 System: Sensor"

This implies that the algorithm processes the sensor's raw electrical signals to calculate a glucose value and trend independently. The clinical performance of this algorithm (e.g., MARD against a reference method) would have been part of the studies for the predicate device (K223435). This 510(k) does not present new data for this, but rather relies on the predicate's established performance, stating that ADC (Abbott Diabetes Care) Glucose Algorithm is the "Same" for the subject device.

7. The Type of Ground Truth Used

For Continuous Glucose Monitoring (CGM) devices, the ground truth for establishing accuracy is typically obtained from laboratory reference methods for blood glucose concentration, such as:

• YSI analysis (Yellow Springs Instrument): This is a widely accepted laboratory reference method for measuring glucose in blood or plasma samples.

While not explicitly stated in this 510(k) summary for the ground truth method, it is the standard for CGM accuracy studies which would have been performed for the predicate device to which this submission refers for clinical performance.

8. The Sample Size for the Training Set

The document does not provide a sample size for the training set of the glucose algorithm. This information would typically be detailed in the original predicate device's submission which established the clinical performance. The current 510(k) refers to the "ADC Glucose Algorithm" as being the same as the predicate, indicating no retraining or significant algorithm changes that would necessitate reporting new training data.

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

Similar to the sample size, the specifics of how the ground truth was established for the training set would have been detailed in the original submission for the predicate device. For CGM algorithms, this involves comparing the sensor's raw electrical signals to paired blood glucose values obtained from a highly accurate laboratory reference method (like YSI), collected over time from a cohort of study participants under various glycemic conditions. This establishes the true glucose concentration against which the algorithm learns to convert sensor signals into accurate glucose readings.

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