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Use of the device for defibrillation is indicated on victims of cardiac arrest with apparent lack of circulation as indicated by:

• Unconsciousness, and .
• Absence of normal breathing, and .
• Absence of a pulse or signs of circulation. .

When the victim is less than 8 years of age or weighs less than 55 lbs (25 kg), the AED Pro unit should with the with ZOLL pedi-padz™ II defibiliation electrodes. Therapy should not be delayed to determine the patient's exact age or weight.

At the discretion of the rescuer, the Monitoring Mode feature can be used with the AED Pro ECG Cable At the ulscretion of the resource and meathing or responsive patient's ECG rhythm, regardless of their to provide a not-olaghostic display of a broaking of Nosperiorms a background analysis of the patient's rhythm and disables its defibrillator feature.

The ZOLL AED Pro External Defibrillator is a portable, battery powered, automated external defibrillator (AED) that uses voice prompts and visual messages to provide feedback to a user attempting a cardiac arrest rescue. The AED Pro acquires and analyzes an adult or pediatric patient's ECG signal and, if a shockable rhythm is detected, recommends delivery of a defibrillation shock via voice and visual prompts. Defibrillation therapy is provided by using defibrillation electrode products specifically designed to be attached to the ZOLL AED Pro. ZOLL pedirpadz™ II defibrillation electrodes enable users to provide therapy to children less than 8 years of age. When used in conjunction with ZOLL CPR-D-padz™, the AED Pro provides CPR compression performance feedback to the user through voice prompts. The ZOLL AED Pro also provides a non-diagnostic ECG monitoring feature and manual override capabilities for physicians and appropriately trained healthcare providers. A high-resolution LCD Screen will display ECG Data, Visual Prompts, Shock Count and CPR compression performance.

The ZOLL AED Pro External Defibrillator is a portable, battery-powered automated external defibrillator (AED) that uses voice prompts and visual messages. It acquires and analyzes an adult or pediatric patient's ECG signal and, if a shockable rhythm is detected, recommends defibrillation. It also provides a non-diagnostic ECG monitoring feature and manual override capabilities for trained healthcare providers.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state numerical acceptance criteria in terms of sensitivity and specificity as is common for diagnostic devices. Instead, it relies on demonstrating substantial equivalence to predicate devices through various performance and safety tests. The "reported device performance" is broadly stated as meeting functional requirements and performance specifications, and complying with industry and safety standards to prove substantial equivalence.

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

The provided 510(k) summary does not specify the sample size of any test set used or the provenance of data (e.g., country of origin, retrospective/prospective). It broadly states "extensive performance testing." This level of detail is typically not included in a public 510(k) summary document but would be part of the full submission to the FDA.

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

The 510(k) summary does not mention the use of experts to establish a ground truth for a test set. The evaluation seems to rely on performance and safety testing against established standards and comparisons to predicate devices, rather than a clinical study with expert-adjudicated ground truth.

4. Adjudication Method for the Test Set

No adjudication method is described, as the summary does not detail a study involving expert review of a test set.

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

No mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance is present in the provided text. The device is an AED, which provides advisories for human users, but the "AI" component (the interpretive algorithm) is the core of the device's decision-making, not an assistant to human readers interpreting ECGs.

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

The summary explicitly states that the device "acquires and analyzes an adult or pediatric patient's ECG signal and, if a shockable rhythm is detected, recommends delivery of a defibrillation shock via voice and visual prompts." This describes the standalone performance of the algorithm in detecting shockable rhythms and providing advisories. The "Performance Testing" section also refers to the "incorporated interpretive algorithm" demonstrating substantial equivalence regarding performance. Therefore, a standalone performance assessment of the algorithm's ability to detect shockable rhythms was implicitly conducted as part of the "extensive performance testing" to demonstrate equivalence.

7. Type of Ground Truth Used

The type of ground truth used for evaluating the device's interpretive algorithm is not explicitly stated. For AEDs, the "ground truth" for a shockable rhythm is typically established by using a database of ECGs classified by cardiologists or based on known clinical outcomes that definitively identify shockable rhythms (e.g., ventricular fibrillation, ventricular tachycardia) versus non-shockable rhythms (e.g., asystole, normal sinus rhythm). The summary's reliance on "performance and safety testing" and "comparison of technological characteristics" to predicate devices suggests that the algorithm's performance was validated against established benchmarks for rhythm analysis in AEDs.

8. Sample Size for the Training Set

The provided 510(k) summary does not mention any training set or its sample size. This information is typically proprietary to the manufacturer and not included in public summaries, especially for devices where the core technology might have been developed over many iterations prior to this specific product.

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

Since no training set is mentioned, the method for establishing its ground truth is also not provided. For such devices, if machine learning was used (which is implied by an "interpretive algorithm"), the ground truth for training data would typically be established by expert cardiologists or electrophysiologists classifying ECG waveforms.

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