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The Reveal LINQ ICM is an insertable automatically-activated monitoring system that records subcutaneous ECG and is indicated in the following cases:
· patients with clinical syndromes or situations at increased risk of cardiac arrhythmias
· patients who experience transient symptoms such as dizziness, palpitation, syncope, and chest pain that may suggest a cardiac arrhythmia
The device has not been tested specifically for pediatric use.

The Reveal LINQ Model LNQ11 Insertable Cardiac Monitors (ICM) is designed to automatically record the occurrence of arrhythmias in a patient. Arrhythmia may be classified as atrial tachyarrhythmia/atrial fibrillation (AT/AF), bradyarrhythmia, pause, or (fast) ventricular tachyarrhythmia. The Reveal LINQ ICM provides storage of ECG and Marker Channel during patient-activated and automatically-detected (auto-activated) events. Auto activation may help to detect abnormal heart rhythms in patients who may not activate/trigger the ICM.
The Reveal LINQ model LNQ11 is a small, leadless device that is typically implanted under the skin, in the chest. Two electrodes on the body of the device continuously monitor the patient's subcutaneous ECG.

The provided text is a 510(k) summary for the Medtronic Reveal LINQ LNQ11 Insertable Cardiac Monitor. The submission is for a modification to an already cleared device, K150614, and specifically addresses changes to the Recommended Replacement Time (RRT) algorithm and minor updates to power supply management (K-factor) behavior.

Based on the document, here's an analysis of the acceptance criteria and supporting study information:

Description of Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

The submission establishes substantial equivalence to a predicate device (Reveal LINQ LNQ11 Insertable Cardiac Monitor, K150614) based on the assertion that the modified device has the same intended use/indications for use, operating principle, design features, device functionality, biological safety, packaging materials, and shelf life. The key differences are algorithmic changes related to RRT and power supply management. The testing described focuses on demonstrating that these changes do not introduce new safety or performance issues and that the device continues to meet established performance criteria.

1. A table of acceptance criteria and the reported device performance

The document does not provide a direct table of specific quantitative acceptance criteria for arrhythmias or algorithm performance. Instead, it states that "The results of the above testing met the specified acceptance criteria and did not raise new safety or performance issues."

The performance is reported in terms of successful completion of various tests:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document does not specify a sample size for any clinical test set, nor does it mention data provenance (country of origin, retrospective/prospective). This is largely because the submission describes bench testing for an algorithmic modification, not a clinical study with patients. The "870 regression tests" likely refer to software test cases, not patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not mention using experts or establishing ground truth by experts in the context of this 510(k) submission. The testing described is primarily focused on software and system verification against established design inputs and previous designs.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Given that the document does not describe a clinical test set requiring expert interpretation, there is no mention of an adjudication method.

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

No MRMC comparative effectiveness study was done or reported in this submission. The device is an insertable cardiac monitor that automatically detects arrhythmias, 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

Yes, the testing described is effectively a standalone (algorithm and system only) performance evaluation. The Reveal LINQ ICM is an "automatically-activated monitoring system" that records ECG and detects events. The modifications are to internal algorithms (RRT and power management). The firmware regression testing and design verification are evaluations of the algorithm and system's performance in a standalone capacity.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For the firmware regression testing and design verification, the "ground truth" would be the expected behavior and outputs defined by the device's design input requirements and the behavior of the predicate device. The aim was to ensure the modified algorithms continued to perform as intended and did not introduce unintended changes or errors compared to the original design. This is a form of design specification-based ground truth rather than clinical outcomes or expert consensus.

8. The sample size for the training set

The document does not mention a training set size. The changes described are to existing algorithms (RRT and power management) for an already cleared device, not the development of a new machine learning algorithm that typically requires a specific training set.

9. How the ground truth for the training set was established

Since no training set is mentioned, this information is not applicable.

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