(27 days)
The EnSite 3000® System is indicated for patients for whom electrophysiology studies are indicated.
When used with the EnSite® Catheter, the EnSite 3000® System is intended to be used in the right atrium of patients with complex arrhythmias that may be difficult to identify using conventional mapping systems alone. OR When used with the EnSite NavX™ Surface Electrode Kit, the EnSite 3000® System is intended to display the position of conventional electrophysiology catheters in the heart.
The EnSite 3000® System is a computerized storage and display system for use in electrophysiology studies of the human heart. The system consists of a console workstation, patient interface unit, and an electrophysiology mapping catheter or surface electrode kit.
Unlike currently available electrode recording catheters, the EnSite Catheter does not require direct contact with the endocardium for the detection of intracardiac electrograms. The EnSite 3000® System is a system that facilitates mapping and treatment of arrhythmias. When used with the EnSite catheter, the system is useful for treating patients with complex, non-sustained, or poorly tolerated arrhythmias that are difficult, if not impossible, to map with current mapping techniques. By visualizing the global activation pattern seen on the color-coded isopotential maps in the EnSite 3000® System, in conjunction with the reconstructed electrograms, the electrophysiologist can identify the arrhythmia source and can navigate to the defined area for therapy. When used with NavX patches, the system is useful in treating patients with simpler arrhythmias by providing non-fluoroscopic navigation and visualization of conventional EP catheters
The provided text describes a 510(k) summary for the EnSite 3000® System. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a standalone study with specific acceptance criteria and performance metrics. Therefore, many of the requested sections about acceptance criteria, study sizes, and ground truth establishment cannot be fully populated from the given information.
However, based on the non-clinical performance data section, some inferences and summaries can be made regarding testing.
Here's an attempt to populate the requested information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The provided 510(k) summary states, "The changes made to the EnSite 3000® System underwent a battery of bench and user tests. Device validation testing was conducted in accordance with in-house procedures." It does not explicitly define specific numerical acceptance criteria or detail the quantitative performance of the device against such criteria. The conclusion simply states, "An evaluation of the device changes indicates that the device is as safe and effective as the previously marketed device to which it is being compared and does not raise any new issues of safety and effectiveness." This implies that the device met internal performance benchmarks consistent with the predicate device, but these are not specified.
| Aspect of Performance | Acceptance Criteria (Not explicitly stated in text) | Reported Device Performance (Summary from text) |
|---|---|---|
| Safety & Effectiveness | Consistent with legally marketed predicate device (K030129) and not raising new safety/effectiveness issues. | "As safe and effective as the previously marketed device... and does not raise any new issues of safety and effectiveness." |
| Functionality | (Implied) Successful operation in bench and user tests for described functions of the EnSite 3000® System components (console, patient interface unit, mapping catheter/surface electrode kit). | "Underwent a battery of bench and user tests." |
| Technological Characteristics | Identical to the legally marketed predicate device. | "The new device has the same technological characteristics as the legally marketed predicate device." |
2. Sample size used for the test set and the data provenance
The document refers to "bench and user tests" but does not specify the sample size (e.g., number of cases, number of subjects, or number of test scenarios) for these tests.
The data provenance is not specified (e.g., retrospective or prospective, country of origin).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not mention the use of experts to establish a "ground truth" for a test set. The evaluation seems to be focused on comparing the new device against the predicate and against internal validation procedures.
4. Adjudication method for the test set
Not applicable, as no external expert adjudication for a test set is described.
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 MRMC study is mentioned. The device described is an electrophysiology mapping system, not an AI-assisted diagnostic tool that would typically involve human "readers" in the context of image interpretation.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
The document does not describe a "standalone" algorithmic performance study in the context of AI. The testing described involves "bench and user tests" for a medical device system, which inherently includes the device's integrated functionalities.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The concept of "ground truth" in the context of evaluating a diagnostic/mapping system's accuracy (e.g., against true anatomical locations or arrhythmia sources) is not explicitly detailed in the provided summary. The evaluation focuses on the safety and effectiveness relative to a predicate device and adherence to internal validation procedures.
8. The sample size for the training set
Not applicable. The document does not describe an AI/machine learning model that would require a "training set." The device is a "computerized storage and display system" and mapping system, not a learning algorithm that is "trained."
9. How the ground truth for the training set was established
Not applicable, as there is no mention of a training set or AI/machine learning model.
§ 870.1425 Programmable diagnostic computer.
(a)
Identification. A programmable diagnostic computer is a device that can be programmed to compute various physiologic or blood flow parameters based on the output from one or more electrodes, transducers, or measuring devices; this device includes any associated commercially supplied programs.(b)
Classification. Class II (performance standards).