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Responder 2500 is intended to be used in a hospital environment by emergency personnel in evaluating and treating life-threatening arrhythmias.
♦ Responder 2500 is intended to be used by trained operators
♦ Responder 2500 is designed for external and internal defibrillation (including cardioversion)
♦ Responder 2500 is capable of monitoring the heart rate with adjustable alarm limits.
♦ Responder 2500 is designed for external pacing with adjustable current and rate.

Responder 2500 and Cardiac Care System is an emergency cardiac monitor/defibrillator with an external pacing option.

This 510(k) submission for the Marquette Electronics Responder 2500 Defibrillator and Cardiac Care System focuses on demonstrating substantial equivalence to predicate devices (Marquette Series 900 and Marquette Series 1500 Responder) rather than providing detailed acceptance criteria and a study proving device performance against those criteria in a typical AI/ML medical device context.

Therefore, many of the requested details regarding acceptance criteria, sample sizes, expert involvement, and ground truth establishment, which are standard for AI/ML device evaluations, are not present in this document. The submission relies on compliance with voluntary standards and general performance equivalency to predicate devices.

Here's an attempt to extract and interpret the available information according to your requested format, along with explicit statements about what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Study Description:

The document describes the "development" of the Responder 2500, which involved various testing and quality assurance measures to confirm compliance with listed voluntary standards and establish equivalence to predicate devices. These measures are:

• Requirements specification reviews
• Code inspections
• Software and hardware testing
• Safety testing
• Environmental testing
• Field tests on the monitor function
• Animal studies on the defibrillation and pacing functions
• Final validation

The results of these measurements "demonstrated that Responder 2500 is as safe, as effective, and performs as well as the predicate devices Marquette Series 900 and Marquette Series 1500 Responder."

Missing Information (Relative to AI/ML Device Evaluation):

Specific quantitative acceptance criteria (e.g., sensitivity, specificity, AUC thresholds) are not provided, as this is a traditional medical device submission focused on performance standards and predicate equivalence.

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

• Sample Size: Not specified. The document mentions "field tests on the monitor function" and "animal studies on the defibrillation and pacing functions," but provides no numbers for test subjects (human or animal) or the duration/scope of these tests.
• Data Provenance: Not specified. No mention of country of origin or whether data 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)

• Not applicable/Not specified. The evaluation relies on compliance with established engineering standards and functional performance, not on expert adjudication of diagnostic outcomes like an AI/ML algorithm might.

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

• Not applicable/Not specified. (See point 3)

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. This is not an AI/ML device and therefore no MRMC study, or impact on human readers, was conducted or reported.

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

• The device itself is a standalone functional unit. Performance was assessed based on its functional capabilities (monitoring, defibrillation, pacing) and compliance with standards. There isn't an "algorithm only" performance concept in the way it applies to AI/ML devices, as the device's function is inherently a standalone "performance."

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

• Functional Ground Truth: The "ground truth" for this device's performance would be defined by the technical specifications outlined in the voluntary standards referenced (e.g., ANSI/AAMI DF2-1996 for defibrillator performance, IEC 601-2-27 for ECG performance). For animal studies, the "ground truth" would be established by physiological measurements and successful or unsuccessful defibrillation/pacing events as determined by veterinarians or other qualified personnel. It is not expert consensus for image interpretation or pathology.

8. The sample size for the training set

• Not applicable. This device is a hardware/software system, not a machine learning model that undergoes a "training" phase with a dataset in the AI/ML sense. Its development involved traditional engineering, software development, and quality assurance processes.

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

• Not applicable. (See point 8)

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