(163 days)
COOLIEF* Radiofrequency Generator (CRG) System is indicated for use to create lesions during neurological lesion procedures and for the coagulation and decompression of disc material to treat symptomatic patients with contained herniated discs. The CRG is to be used with only Avanos RF Probes and accessories, such as Avanos TransDiscal Probes, COOLIEF*/Standard RF Pain Management Probes, Cannulas, Introducers, and Fluid Delivery Introducers. Use of CRG System is limited to the indications cleared under these Avanos devices and accessories.
The COOLIEF* Radiofrequency Generator (CRG) System uses controlled radiofrequency energy to generate heat that is used to create lesions in neurological tissues or to coagulate and decompress disc material to treat symptomatic patients with contained herniated discs. The following components of the predicate Pain Management Generator-TD (K072478), which must be used together, have been modified and are the subject of this 510(k): a) COOLIEF* Radiofrequency Generator (CRG), b) COOLIEF* Quad Pump Unit (QPU), and c) COOLIEF* Therapy Cables.
The provided text describes the 510(k) premarket notification for the COOLIEF* Radiofrequency Generator (CRG) System. It lists general and technological characteristics of the device and compares them to a predicate device, along with non-clinical performance data.
Here's a breakdown of the requested information based on the provided text:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state "acceptance criteria" in a quantitative manner (e.g., minimum accuracy percentages). Instead, it lists various performance tests conducted and their successful completion, implying that "Pass" is the acceptance criterion for each test.
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| COOLIEF* RF Generator Hardware Performance | Pass |
| Quad Pump Unit Flow-Rate Verification | Pass |
| Therapy Cable Mechanical Testing | Pass |
| Software Verification and Validation | Pass |
| Temperature Control - Benchtop and Perfused Tissue | Pass |
| Bench-Top Lesion Validation | Pass |
| Transportation and Handling (Packaging) | Pass |
| IEC 60601-1 (Medical Electrical Equipment – General Requirements for basic safety and essential performance 2012, Edition 3.1, Class 1) | Pass |
| IEC 60601-1-2: 2014 (Medical Electrical Equipment - General requirements for basic safety and essential performance – Collateral standard: Electromagnetic compatibility – Requirements and tests) | Pass |
| IEC 60601-2-2: 2009 (Fifth Ed.) Medical electrical equipment Part 2-2: Particular requirements for basic safety and essential performance of high frequency surgical equipment and high frequency surgical accessories | Pass |
| IEC 62366-1:2015 (Part 1: Application of Usability Engineering to Medical Devices) | Pass |
| IEC 62304:2006 A1:2015 (Medical device software – software life cycle processes) | Pass |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document primarily discusses non-clinical testing (performance testing). For these tests, specific sample sizes are not explicitly stated. For example, "Therapy Cable Mechanical Testing" implies that multiple cables were tested, but the number is not provided. Similarly, for "Bench-Top Lesion Validation," the number of lesions or trials is not detailed.
No clinical data was used for this submission, therefore, none of the typical provenance information (country of origin, retrospective/prospective) applies.
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)
This section is not applicable as the submission relies on non-clinical performance data and engineering verification tests. The "ground truth" for these tests would be established by the expected technical specifications and standards (e.g., a flow rate of >18mL/min, successful completion of a software test protocol). It doesn't involve subjective expert review in the way medical image analysis or diagnostic decision-making would.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This section is not applicable as the submission focuses on non-clinical performance testing and engineering verification. Adjudication methods are typically used in clinical studies or studies involving expert interpretation where there might be disagreements on ground truth.
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
A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted and is not applicable to this submission. The device is a radiofrequency generator, not an AI-powered diagnostic or assistive tool for human readers. No AI assistance or human reader performance is mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This section is not applicable. The device is a hardware system (radiofrequency generator, pump, cables) and is not an algorithm or AI product capable of standalone performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The ground truth for the non-clinical performance tests is based on engineering specifications, established standards (e.g., IEC 60601 series), and expected functional performance. For instance, for "Quad Pump Unit Flow-Rate Verification," the ground truth is whether the measured flow rate meets the specified >18mL/min. For "Software Verification and Validation," the ground truth is compliance with the software requirements and absence of critical bugs. There is no mention of expert consensus, pathology, or outcomes data being used to establish ground truth for this type of submission.
8. The sample size for the training set
This section is not applicable. The submission describes a medical device (radiofrequency generator system), not a machine learning or AI model that requires a training set.
9. How the ground truth for the training set was established
This section is not applicable as there is no training set for this device.
§ 882.4400 Radiofrequency lesion generator.
(a)
Identification. A radiofrequency lesion generator is a device used to produce lesions in the nervous system or other tissue by the direct application of radiofrequency currents to selected sites.(b)
Classification. Class II (performance standards).