(99 days)
The LINQ II ICM is an insertable automatically-activated and patient-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 LINQ II Insertable Cardiac Monitor (ICM) Model LNQ22 is a programmable device that continuously monitors a patient's ECG and other physiological parameters. The device records cardiac information in response to automatically detected arrhythmias and patient-initiated activation or markings. The device is designed to automatically record the occurrence of an episode of arrhythmia in a patient. Note: Arrhythmias are classified as tachyarrhythmia, bradyarrhythmia, pause, atrial tachyarrhythmia, or atrial fibrillation. Patients may also manually record symptoms. In order to manually record symptoms, the patient will also need either the MyCareLink Heart App (patient app on mobile device) or the Patient Assistant Model PA97000. The patient can use the MyCareLink Heart App or the Patient to manually record his or her cardiac rhythm while experiencing or immediately after a symptomatic event. LINQ II ICM and this submission includes the following accessories: LINO Tool Kit Model LNO22TK, Reveal LINQTM Mobile Manager Model MSW002, Device Command Library Model 2692, and Instrument Command Library Model 2691.
It appears you've provided a 510(k) summary for the Medtronic LINQ II Insertable Cardiac Monitor (ICM), which outlines the device's technical characteristics, indications for use, and a summary of testing performed to demonstrate substantial equivalence to a predicate device.
However, this document does not contain the specific details about the acceptance criteria and the study proving the device meets those criteria in the format you've requested regarding AI/algorithm performance. The information provided heavily focuses on regulatory aspects, engineering verification (mechanical, electrical, EMC, sterilization, biocompatibility, MRI compatibility, human factors, security), and software validation (firmware, regression, system design). While it mentions "Sensing and detection performance validation," it does not break down the specific performance metrics (like sensitivity, specificity, accuracy for arrhythmia detection), the study design used for that validation (e.g., test set demographics, ground truth establishment, expert adjudication), or whether this involved a comparative effectiveness study with human readers (MRMC).
The document states: "The LINQ II ICM includes minor changes to enhance the arrhythmia detection algorithms and diagnostics which include pause detection and PVC detector." However, it does not provide the performance data for these enhanced algorithms or a study design to assess their performance against acceptance criteria.
Therefore, based solely on the provided text, I cannot complete your request for a table of acceptance criteria and reported device performance related to the detection algorithm's diagnostic accuracy, nor can I answer questions about:
- Sample size and data provenance for the test set of the algorithm's diagnostic performance.
- Number of experts and their qualifications for ground truth establishment.
- Adjudication method for the test set.
- MRMC comparative effectiveness study results (effect size of human reader improvement with AI).
- Standalone algorithm performance metrics.
- Type of ground truth used for algorithm performance.
- Sample size for the training set.
- How ground truth for the training set was established.
The document primarily focuses on establishing substantial equivalence to a predicate device, meaning the new device's safety and effectiveness are comparable to a legally marketed device. This often involves demonstrating that changes do not introduce new safety or efficacy concerns and that the device performs as intended in typical engineering and regulatory verification tests. It does not typically include a detailed clinical diagnostic accuracy study of an AI algorithm in the way your prompt describes, especially one that would involve human-in-the-loop performance studies like MRMC.
To answer your question thoroughly, I would need a different type of document, such as a detailed clinical study report specifically focused on the performance of the "arrhythmia detection algorithms and diagnostics" mentioned.
§ 870.1025 Arrhythmia detector and alarm (including ST-segment measurement and alarm).
(a)
Identification. The arrhythmia detector and alarm device monitors an electrocardiogram and is designed to produce a visible or audible signal or alarm when atrial or ventricular arrhythmia, such as premature contraction or ventricular fibrillation, occurs.(b)
Classification. Class II (special controls). The guidance document entitled “Class II Special Controls Guidance Document: Arrhythmia Detector and Alarm” will serve as the special control. See § 870.1 for the availability of this guidance document.