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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 ICM 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.

Reveal LINQ ICM includes the following accessories: LINQ Tool Kit Model LNQTOOL, Patient Assistant PA96000, Reveal LINQ™ Mobile Manager Model MSW002 used with Patient Connector Model 24967, CareLink Programmer Model 2090, Encore Programmer Model 29901, Reveal LINO Application Software Model SW026, MyCareLink Patient Monitor Models 24950 and 24955, CareLink Express Monitor Model 2020B, Device Data Management Application Model 2491, Device Command Library Model 2692 and Instrument Command Library Model 2691, CareLink Express Mobile Application Models 31302, and CareLink Network. New to the Reveal LINO ICM system is the AccuRhythm AI ECG Classification System Models ZA400, ZA410, ZA420, included in this submission.

The provided text describes the regulatory clearance of the Medtronic Reveal LINQ Insertable Cardiac Monitor (ICM) with AccuRhythm AI ECG Classification System. While it states that "Performance validation testing and analysis were completed to ensure the algorithms were able to reduce false alerts from ICM detected AF and Pause episodes while retaining true alerts," and "All results met or exceeded the criteria in the Validation Plan," the document does not explicitly detail the specific acceptance criteria or the numerical results of the device's performance against those criteria. It refers to the testing done for the predicate device (LINQ II ICM with AccuRhythm AI ECG Classification System K210484) and states that no new changes were made to the Reveal LINQ ICM itself.

Therefore,Based on the provided text, I can infer some aspects of the study and its criteria, but much of the specific numerical data requested (like actual performance results, sizes for test set, and detailed information about ground truth establishment for this specific submission) is not present. The document focuses on showing substantial equivalence to a previously cleared device (K210484), rather than providing a full detailed clinical study report for the AccuRhythm AI component.

Here's an attempt to answer your questions based on the available information:

Acceptance Criteria and Reported Device Performance

The document states that "All results met or exceeded the criteria in the Validation Plan." However, the specific numerical acceptance criteria and the reported device performance values are not provided in this document. The focus is on the function of the AI system: to "reduce false alerts from ICM detected AF and Pause episodes while retaining true alerts."

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: Not explicitly stated for the validation testing of the AccuRhythm AI ECG Classification System in this document. The document refers to "Performance validation testing and analysis."
   • Data Provenance: Not specified in this document (e.g., country of origin, retrospective or prospective).

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • This information is not provided in the document.

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

   • This information is not provided in the document.

4. 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:

   • The document does not mention an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The testing described is focused on the algorithm's ability to reduce false alerts and retain true alerts, implying a standalone performance evaluation or an evaluation within the device's existing automatic detection framework.

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

   • Yes, the description of "Performance validation testing and analysis were completed to ensure the algorithms were able to reduce false alerts from ICM detected AF and Pause episodes while retaining true alerts" strongly suggests a standalone (algorithm-only) performance evaluation was conducted for the AccuRhythm AI component. The specific metrics, however, are not provided.

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

   • The type of ground truth used is not explicitly stated. However, given the context of ECG classification for arrhythmias (AF and Pause), it is highly probable that the ground truth was established through expert adjudication of ECG recordings, possibly referring back to established clinical diagnoses or reference standards.

7. The sample size for the training set:

   • The sample size for the training set is not provided in this document.

8. How the ground truth for the training set was established:

   • This information is not provided in this document.

In summary, this FDA clearance letter emphasizes the substantial equivalence to a previously cleared device that includes the AccuRhythm AI system (K210484). While it states that performance validation was done and met criteria for reducing false alerts and retaining true alerts, the detailed specifics of the study design, sample sizes, expert involvement, and numerical performance metrics for the AI component itself are not included in this high-level summary. These details would typically be found in the full 510(k) submission and associated scientific documentation, not necessarily in the publicly available clearance letter.

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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 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 LINO ICM Model LNO11 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.

This document describes modifications to the Reveal LINQ Insertable Cardiac Monitor (ICM). While it mentions verification and validation activities were completed successfully and demonstrated no adverse impact, it does not provide detailed acceptance criteria and a study proving the device meets those criteria in a format that lends itself to the requested table.

Here's a breakdown of what can be extracted and what is missing based on your request:

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

This information is not explicitly provided in the document. The document states: "The results of the above verification and validation testing met the specified acceptance criteria and did not raise new safety or performance issues." However, it does not detail what those acceptance criteria were or present specific performance metrics against them. The modification was related to "RAMware to ensure the detection parameters are appropriately configured after a partial electrical reset," implying the acceptance criteria would be related to the correct configuration of these detection parameters.

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

This information is not provided. The document mentions "device verification and system validation testing" and "design verification and validation activities" but gives no details about the sample sizes of devices or patient data used, nor its provenance or whether it was retrospective or prospective.

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):

This information is not provided. The document describes a technical modification related to device configuration, not an algorithm that interprets medical data requiring expert ground truth.

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

This information is not provided. The nature of the modification (RAMware for detection parameter configuration) does not suggest a need for a human adjudication process as would be typical for clinical diagnostic algorithms.

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:

This is not applicable/not provided. The device is an Insertable Cardiac Monitor (ICM) that automatically records arrhythmias, and the specific modification discussed is a technical fix related to internal device configuration. There is no mention of an "AI" component or a "human-in-the-loop" study in this context.

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

This is not applicable/not provided in the context of an "algorithm only" performance study. The modification is a technical change to the device's internal software/firmware to ensure correct parameter configuration. The document implies device-level testing of this configuration.

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

Given the nature of the modification (RAMware to ensure detection parameters are appropriately configured after a partial electrical reset), the "ground truth" would likely be the expected correct configuration of the detection parameters as defined by the device's design specifications. This would be established through engineering and software validation processes, not clinical expert consensus or pathology.

8. The sample size for the training set:

There is no mention of a training set. The modification is a specific technical fix to ensure proper configuration, not a machine learning model that would require a training set.

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

There is no mention of a training set, so this information is not applicable.

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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 ICM (Model LNQ11) in association with the LINQ Mobile Manager Application (Model MSW001 or MSW002) and patient connector (Model 24965 and 24967) (referred to as the LINQ Mobile Manager system) are substantially equivalent to the following predicate device: Reveal LINQ ICM (Model LNO11) cleared via K160689 on April 22, 2016.

This document, K163460, is a 510(k) Premarket Notification for the Medtronic Reveal LINQ Insertable Cardiac Monitor, Model LNQ11. It's a submission to demonstrate substantial equivalence to a predicate device, not necessarily to prove the device meets new acceptance criteria for novel functionality with detailed performance data typically associated with AI/ML devices.

Therefore, many of the requested points regarding acceptance criteria, training/test sets, expert adjudication, MRMC studies, and standalone performance data for AI are not present in this document, as this device primarily relies on proving equivalence to an existing, already approved medical device, and the changes appear to be in a patient connector component, rather than core AI algorithms.

However, based on the information provided, here's what can be extracted and inferred:

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

The document does not provide a table of new acceptance criteria and their corresponding performance metrics because the submission is for showing substantial equivalence to a predicate device, not for a novel device with new performance claims. The performance data provided are generally related to safety and functionality to ensure the new component (patient connector Model 24967) does not negatively impact the existing device's performance.

The "acceptance criteria" here are implicitly that the new device component performs the same as the predicate device component and meets relevant safety standards.

• Implicit Acceptance Criteria (for the new CareLink SmartSync Device Manager, patient connector, Model 24967):

  • Biocompatibility: Must pass cytotoxicity, sensitization, and irritation tests.
  • Electrical Safety and EMC: Must comply with IEC 60601-1 and IEC 60601-1-2 standards.
  • Software: Verification and validation testing must be passed, with the software considered a "major" level of concern.
  • Mechanical: Must pass inspections for design features, function, workmanship, labeling, control activation forces, chemical resistance, environmental/drop testing, and button/contact reliability.
  • No negative impact on existing Reveal LINQ ICM functionalities (e.g., R-wave sensing, sampling rate, detection algorithms).

• Reported Device Performance (from the document):

  • Biocompatibility: "The battery of testing for materials used in the Model 24967 included the following tests: Cytotoxicity, Sensitization, Irritation." (Implies successful completion for acceptance).
  • Electrical safety and electromagnetic compatibility (EMC): "The system complies with the IEC 60601-1, standards for safety and the IEC 60601-1-2 third and fourth edition versions of the standard for EMC." (Implies successful completion for acceptance).
  • Software Verification and Validation Testing: "Software verification and validation testing were conducted and documentation was provided as recommended by FDA's Guidance..." (Implies successful completion for acceptance).
  • Mechanical Testing: "The following is a list of testing performed: Inspection of the required mechanical design features and function...Workmanship inspection...Product labeling inspection...Forces required to activate controls...Chemical resistance testing...Environmental and drop testing...Reliability testing of buttons, electrical contacts, user connector insertions, and replaceable or moving mechanical components." (Implies successful completion for acceptance).
  • Functional Equivalence: The comparison table (page 4) explicitly states "Same" for virtually all functional characteristics between the new configuration and the predicate device (e.g., R Wave Sensing, Sampling Rate, Storage Time, Noise Reversion, Brady Detection, Asystole Detection, Ventricular Tachycardia Detection, Afib Detection, Detection Algorithms, etc.). This implies performance is identical to the predicate which would have already met its own performance criteria.

2. Sample sizes used for the test set and the data provenance

The document describes non-clinical performance testing (biocompatibility, electrical safety, mechanical, software V&V) of a new component (patient connector) that connects to an existing, approved device. These tests typically involve lab-based testing of the specific component or system, not "test sets" in the sense of patient data for evaluating an algorithm's performance on clinical outcomes.

• Sample sizes: Not specified in terms of patient data. The tests performed are engineering and lab-based (e.g., number of test units for mechanical testing, number of samples for biocompatibility tests).
• Data provenance: Not applicable in the context of clinical data for AI/ML validation. These are engineering test results.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The ground truth for biocompatibility is established by standard ISO tests. For electrical and mechanical safety, it's defined by engineering standards (e.g., IEC 60601 series). For software, it's based on verification and validation against requirements. There's no indication of clinical expert review for "ground truth" establishment in this type of submission for a system where the core algorithmic functionality is unchanged and equivalence is being sought for a specific component.

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

Not applicable, as noted above, this is about engineering and safety testing, not clinical data adjudication.

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 such study was performed or is mentioned. This submission is about a device (Insertable Cardiac Monitor) that records ECG and performs automated detection. It does not describe an AI that assists human readers in interpreting images or complex signals that would warrant an MRMC study.

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

The device itself includes detection algorithms (e.g., Brady, Asystole, VT, Afib). The document states these are "Same" as the predicate device. For the predicate device, or this device in itself, the performance of these automated detection algorithms would be considered a form of standalone performance. However, this submission specifically highlights that the algorithms themselves are unchanged from the predicate device (K160689). The focus of this submission is on the safety and performance of a new patient connector model that interfaces with the existing device.

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

For the device's core functionality (i.e., its ability to detect arrhythmias), the ground truth in its initial approval would have likely been established through clinical trials comparing its detections against adjudicated ECGs, perhaps from expert cardiologists or verified clinical events. However, this information pertains to the predicate device's original approval and is not detailed in this 510(k) summary, as the algorithms are stated to be "Same."

For the new component (patient connector Model 24967) being approved in this submission, the "ground truth" for its acceptance criteria are established by:

• Biocompatibility standards: ISO 10993 series.
• Electrical safety standards: IEC 60601-1, IEC 60601-1-2.
• Software V&V best practices: FDA guidance for software in medical devices.
• Mechanical engineering standards: Internal Medtronic standards and potentially relevant industry standards for durability, force, etc.

8. The sample size for the training set

Not applicable. This document is not describing the development or validation of a new AI algorithm where a training set would be used. It's about demonstrating substantial equivalence for a hardware component.

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

Not applicable, as there is no training set discussed for a new AI algorithm.

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The Reveal LINQ Insertable Cardiac Monitor is an implantable patient-activated and 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 Patient Assistant is intended for unsupervised patient use away from a hospital or clinic. The Patient Assistant activates the data management features in the Reveal LINQ ICM to initiate recording of cardiac event data in the implanted device memory.

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) ventrioular 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 LINO model LNO11 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.

A set of tools are provided with the Reveal LINQ ICM to create the small incision in the skin and to easily form a tight pocket and insert the ICM into the shallow subcutaneous pocket. There are two implant tools: the Incision Tool, used to make a small incision through the patient's skin; and the Insertion Tool, used to insert the device through the incision and into the patient's body at the desired location.

The Reveal Patient Assistant Model 9538 is a hand-held, battery-operated telemetry devices The Patient Assistant activates the data management features in the Reveal LINQ ICM to initiate recording of cardiac event data in the implanted device memory.

Here's a breakdown of the acceptance criteria and supporting study details based on the provided text, focusing on the Reveal LINQ ICM (Model LNQ11) as the primary device mentioned with performance testing.

1. Table of Acceptance Criteria and Reported Device Performance

For the Reveal LINQ ICM, the primary performance criterion explicitly detailed in the document relates to its ability to detect Atrial Fibrillation (AF). The relevant study is the XPECT Trial, which was conducted on a predicate device (Reveal XT), but the results are used to support the new device's capabilities.

Note: The document states "The sensitivity, specificity, positive predictive value, and negative predictive value for identifying patients with any AF were 96.1%, 85.4%, 79.3%, and 97.4%, respectively. Overall accuracy reported for detecting AF was 98.5% for the Reveal XT ICM." These are presented as the performance metrics that presumably satisfied the acceptance criteria for AF detection.

Other performance tests mentioned are at a high level and don't provide specific numerical acceptance criteria or performance figures in this summary:

• Bench Testing: Electromagnetic compatibility (EMC), Electrical safety, Firmware and Hardware verification, Mechanical Verification, Implant Tools Verification, Packaging Design Verification, Sterilization, Biocompatibility, MRI compatibility, Sensing and Detection performance validation, System Validation.
• Animal Testing: Reveal LINQ GLP Study.
• Human Factors Testing: Formative and Validation Testing.

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

• XPECT Trial (AF detection):
  • Sample Size: N = 247
  • Data Provenance: Not explicitly stated (e.g., country of origin). The study type is "Validation," and it utilized a "specialized Holter to record the accuracy of the AF algorithm." It implies a prospective collection of data to validate the algorithm against a reference standard (Holter).
• AIM Study (Insertion Force):
  • Sample Size: N = 41
  • Data Provenance: Not explicitly stated, described as "Prospective, multicenter, feasibility" study.
• MapReveal Study (Signal Amplitude):
  • Sample Size: N = 42
  • Data Provenance: Not explicitly stated, described as "Prospective, multicenter, feasibility" study, with follow-up from 24 hours to > 1 month.
• Subcutaneous Implant Migration (SubQ-IM) Study (Migration Observation/Insertion Force/ECG measurements):
  • Sample Size: N = 40 healthy volunteers (implanted with 1 or 2 nonfunctional device prototypes)
  • Data Provenance: Not explicitly stated, described as "Prospective, multicenter, randomized feasibility" study.

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

The document does not explicitly state the number or qualifications of experts used to establish ground truth for any of the studies mentioned. Specifically for the XPECT trial, it mentions a "specialized Holter to record the accuracy of the AF algorithm," which implies a reference standard, but doesn't detail human expert involvement in its interpretation or adjudication.

4. Adjudication Method for the Test Set

The document does not explicitly mention an adjudication method (e.g., 2+1, 3+1, none) for any of the studies.

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

No MRMC comparative effectiveness study is mentioned in the provided text, nor is there any discussion of an effect size of how much human readers improve with AI vs. without AI assistance. The focus of the clinical performance data (XPECT trial) is on the device's algorithm performance in detecting AF rather than human-in-the-loop performance.

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

Yes, a standalone algorithm performance study was done for AF detection. The XPECT Trial was designed "To assess whether the AF detection algorithm reliably detected the presence or absence of AF." This directly evaluates the device's algorithm without explicit human intervention in the detection process quantified by the reported sensitivity, specificity, and accuracy.

7. The Type of Ground Truth Used

• XPECT Trial (AF detection): The ground truth was established by a "specialized Holter to record the accuracy of the AF algorithm." This suggests a reference standard based on another established physiological measurement device and its interpretation.
• AIM Study (Insertion Force): Ground truth was likely direct measurement of insertion force.
• MapReveal Study (Signal Amplitude): Ground truth likely involved direct measurement of R-wave amplitude from ECG and subcutaneous ECG data.
• SubQ-IM Study (Migration): Ground truth for migration was "Using fluoroscopy imaging."

8. The Sample Size for the Training Set

The document does not provide information about the training set size for any algorithms or models used in the device. The XPECT trial is described as a "Validation" study, implying the algorithm was already developed and this study was for its validation using a test set.

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

The document does not provide information on how the ground truth for any training set was established, as details about a training set are not included.

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