(27 days)
The AutoPulse Resuscitation System Model 100 is intended to be used as an adjunct to manual CPR, on adult patients only, in cases of clinical death as defined by lack of spontaneous breathing and pulse.
AutoPulse Resuscitation System Model 100 ("Device") is an automated, portable, battery powered device that compresses the chest of an adult human as an adjunct to manual CPR. The Device consists of a single use chest compression assembly (CCA) that includes a patient liner, and a reusable platform that contains a user control panel, a drive mechanism, a control system, and a power system (rechargeable battery).
The provided document describes a Special 510(k) for a device modification, focusing on substantial equivalence to predicate devices rather than providing detailed acceptance criteria and study proving its performance. The document explicitly states: "Appropriate product testing was conducted to evaluate conformance to product specification and substantial equivalence to predicate devices." However, it does not disclose the specific acceptance criteria or the full details of the studies conducted.
Therefore, many of the requested details cannot be extracted from this document.
Here's a summary of what can be inferred or directly stated, along with what is missing:
1. Table of Acceptance Criteria and Reported Device Performance:
This information is not provided in the document. The submission focuses on substantial equivalence based on overall function and materials, rather than specific performance metrics against defined acceptance criteria.
2. Sample size used for the test set and the data provenance:
- Sample size: Not specified.
- Data provenance: Not specified (e.g., country of origin, retrospective/prospective). The document only mentions "Appropriate product testing was conducted."
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not provided. The nature of this device (a resuscitation system) means "ground truth" would likely relate to physiological outcomes or device performance in simulated/clinical settings, not expert interpretation of data like images.
4. Adjudication method for the test set:
Not applicable and not provided. As no expert-based ground truth interpretation is mentioned, an adjudication method for such a ground truth would not be relevant.
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 device is an automated resuscitation system, not an AI-assisted diagnostic or interpretative device that augments human readers. Therefore, an MRMC study with human readers assisting AI or vice-versa is not applicable and not mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
The device is an "automated, portable, battery powered device that compresses the chest of an adult human as an adjunct to manual CPR." This implies its primary function is standalone mechanical operation, though as an "adjunct to manual CPR," it inherently involves human interaction. The document doesn't detail standalone performance tests, focusing broadly on "conformance to product specification and substantial equivalence."
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
The document does not explicitly state the type of ground truth. For a resuscitation device, ground truth would typically involve:
* Physiological measurements during CPR (e.g., compression depth, rate, effectiveness in maintaining blood flow).
* Biomechanical performance (e.g., force applied, consistency).
* Safety parameters.
The document broadly states "conformance to product specification," which would imply testing against internal specifications for these types of parameters.
8. The sample size for the training set:
Not applicable and not provided. This device is a mechanical resuscitation system, not a machine learning or AI-driven system that would require a "training set" in the conventional sense of machine learning.
9. How the ground truth for the training set was established:
Not applicable and not provided, as there is no mention of a training set for an AI/ML model for this device.
§ 870.5200 External cardiac compressor.
(a)
Identification. An external cardiac compressor is an externally applied prescription device that is electrically, pneumatically, or manually powered and is used to compress the chest periodically in the region of the heart to provide blood flow during cardiac arrest. External cardiac compressor devices are used as an adjunct to manual cardiopulmonary resuscitation (CPR) when effective manual CPR is not possible (e.g., during patient transport or extended CPR when fatigue may prohibit the delivery of effective/consistent compressions to the victim, or when insufficient EMS personnel are available to provide effective CPR).(b)
Classification. Class II (special controls). The special controls for this device are:(1) Nonclinical performance testing under simulated physiological conditions must demonstrate the reliability of the delivery of specific compression depth and rate over the intended duration of use.
(2) Labeling must include the following:
(i) The clinical training necessary for the safe use of this device;
(ii) Adjunctive use only indication prominently displayed on labels physically placed on the device and in any device manuals or other labeling;
(iii) Information on the patient population for which the device has been demonstrated to be effective (including patient size and/or age limitations,
e.g., adult, pediatric and/or infant); and(iv) Information on the time necessary to deploy the device as demonstrated in the performance testing.
(3) For devices that incorporate electrical components, appropriate analysis and testing must demonstrate that the device is electrically safe and electromagnetically compatible in its intended use environment.
(4) Human factors testing and analysis must validate that the device design and labeling are sufficient for effective use by the intended user, including an evaluation for the time necessary to deploy the device.
(5) For devices containing software, software verification, validation, and hazard analysis must be performed.
(6) Components of the device that come into human contact must be demonstrated to be biocompatible.