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510(k) Data Aggregation
(29 days)
The SNM FirmGrip™ - Peripherally Inserted Catheter Device is intended for use in patients requiring repeated access to the peripheral venous system for infusion or injection intravenous therapies and/or blood sampling.
Flexicath's SNM FirmGrip™ is actually the same device cleared under K080793 except for the addition of Safety Needle Mechanism (SNM) to the Cleared Needle Unit Assemblv. The Safety Needle Mechanism is a unique feature enables needle retraction into a tubular housing once it is taken out from the patient vein. Once the needle is withdrawn from the vein, the safety mechanism is activated by a simple press on the safety mechanism Activation Button (Locking Clip). The SNM has a male connector which is used to be inserted into the FirmGrip™ PeelGuard's female connector when being integrated with the FirmGrip™ Catheter and Catheter Sleeve at the Catheter insertion stage. The compressed spring is being restrained by a Locking clip which used to hold it in place until safety mechanism is activated. Once the Locking clip button is pushed, the SNM is being activated and the needle is being retracted and fully covered. At this stage the FirmGrip Needle Unit Assembly and the SNM are ready to be disposed.
The provided text describes a Special 510(k) submission for the SNM FirmGrip™ Peripherally Inserted Catheter Device, which is a modification of an already cleared device. The key change is the addition of a Safety Needle Mechanism (SNM).
Here's an analysis of the acceptance criteria and the study information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria | Reported Device Performance |
---|---|
According to Flexicath's legally marketed device specification and applicable standards | "All acceptance criteria were met." |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not explicitly stated. The document mentions "non-clinical tests" but does not detail the number of units tested.
- Data Provenance: Not explicitly stated (e.g., country of origin). The company is based in Israel ("Tel Aviv 67443 Israel"), but the origin of the test data is not specified beyond being "non-clinical performance data." The tests were conducted to support claims for a device intended for the US market. The study is retrospective in the sense that it's a verification and validation of a modified existing device.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications
Not applicable. This was a non-clinical device performance study, not one requiring expert human interpretation of medical images or data for ground truth establishment.
4. Adjudication Method for the Test Set
Not applicable. This was a non-clinical device performance study, not one requiring human adjudication.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly mentioned or indicated. The study focused on the non-clinical performance of the device's safety mechanism and its substantial equivalence to a predicate device.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done
While the term "standalone" is typically used for AI/algorithm performance, in the context of this device, the non-clinical performance tests can be considered "standalone" in that they evaluated the device's physical and functional characteristics without human intervention as a variable in the performance assessment itself. The tests were purely mechanical/functional.
7. The Type of Ground Truth Used
The "ground truth" for the non-clinical performance tests would be defined by the device's design specifications and applicable industry standards. The device's performance was compared against these established technical requirements (e.g., strength, retraction mechanism functionality, material integrity, etc.).
8. The Sample Size for the Training Set
Not applicable. This is not a study involving a "training set" in the context of machine learning or AI. It is a device performance verification and validation study.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as there was no "training set." The performance was evaluated against engineering and design specifications.
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