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510(k) Data Aggregation
(144 days)
DABRA Laser System (DABRA Laser model RA-308 and DABRA Catheter model 101)
The DABRA Laser System is indicated for crossing chronic total occlusions in patients with symptomatic infrainguinal lower extremity vascular disease.
The Ra Medical Systems' DABRA™ Laser Catheter model 101 and RA-308 Excimer Laser is composed of an Excimer laser light source and gamma sterilized single use catheter consisting of an extruded fluorinated ethylene propylene (FEP) tube coated on the inner diameter with an amorphous fluoropolymer thin film resulting in total internal reflection of 308 nm light capped at each end by optical fiber (fused silica).
The laser light is generated by a software-controlled 308nm Excimer source though a discharge excitation process within a gas filled resonating cavity. The light is directed to the catheter through a lens. The fiber and the fluid use total internal reflection to direct the ultraviolet laser energy from the laser light source to the tip of the catheter. The catheter is connected to the laser for the procedure, and then inserted into the patient's vasculature along the length of a previously inserted guide catheter, support catheter, quiding sheath, or introducer sheath allowing the physician to target the laser energy to the lesion.
The laser energy photoablates the lesion creating a lumen that permits blood flow. The lumen can be used for other interventional treatment devices. The system is designed to be used in a catheterization laboratory. This setting includes fluoroscopy devices, injecting devices, patient monitoring devices, a table for the patient, and personnel to assist the physician in performing the treatment.
This document is a 510(k) summary for the DABRA Laser System (K210664). It details a modification to an already cleared device (predicate device K170349). The information provided focuses on demonstrating substantial equivalence rather than a full de novo study with a new device and extensive clinical performance data beyond what's typically required for device modifications.
Therefore, many of the requested sections regarding acceptance criteria and study details (like sample sizes, expert ground truth establishment, MRMC studies, standalone performance, training data) are not explicitly present or are not applicable in the context of this 510(k) submission because it's a modification to an already cleared device. The performance data provided are primarily engineering and bench testing to demonstrate that the changes do not adversely affect safety or effectiveness.
Here's the breakdown of what can be extracted and what is not available from the provided text, based on the nature of a 510(k) for a device modification:
1. Table of acceptance criteria and reported device performance:
The document mentions several types of performance testing but does not provide a table with specific quantitative acceptance criteria or detailed reported outcomes for each test. Instead, it makes a general statement: "Performance testing has demonstrated that the subject device met the applicable design output requirements." This implies that the device did meet pre-defined criteria, but those specific criteria and results are not listed.
Acceptance Criteria | Reported Device Performance |
---|---|
Not explicitly detailed in the provided document for each specific test. Implied: Meet applicable design output requirements. | Met applicable design output requirements (as stated under "Conclusions"). |
2. Sample sizes used for the test set and the data provenance:
- Sample Sizes: Not specified. For engineering and bench testing, sample sizes would typically be determined by statistical methods or industry standards relevant to the specific tests (e.g., ISO standards for biocompatibility).
- Data Provenance: Not explicitly stated, though implicitly, these are likely laboratory-based bench and simulated use tests conducted by or for Ra Medical Systems, Inc. in the US. There's no mention of patient data or clinical trials in the performance data section for this 510(k) submission.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not Applicable / Not Specified: For this type of 510(k) submission (device modification), there is no mention of "ground truth" established by experts in the context of clinical performance, as it relies on engineering and bench testing rather than clinical study data requiring expert interpretation of medical images or outcomes.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
- Not Applicable / Not Specified: Adjudication methods are relevant for clinical studies where expert consensus is needed to establish ground truth from subjective data (like image interpretation). This 510(k) focuses on non-clinical performance data.
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:
- Not Applicable: This device is a laser system for ablating lesions, not an AI-assisted diagnostic device, so an MRMC study is not relevant. The submission is about a physical medical device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Not Applicable: This is a physical medical device, not an algorithm. Therefore, "standalone algorithm performance" is not a relevant concept for this submission.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- Not Applicable / Not Specified: The "ground truth" in this context refers to the successful meeting of engineering specifications and testing standards (e.g., package integrity, catheter performance, safety, biocompatibility). These are objective measurements against pre-defined engineering criteria, not subjective medical diagnoses requiring expert consensus or pathology.
8. The sample size for the training set:
- Not Applicable: This document describes a physical medical device and its modifications, not an AI/ML algorithm. Therefore, there is no "training set" in the computational sense.
9. How the ground truth for the training set was established:
- Not Applicable: As there is no training set for an AI/ML algorithm, this question is not relevant.
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