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510(k) Data Aggregation
(131 days)
AFM ULTRA AG DRESSINGS
AFM Ultra Ag Dressings are indicated for management of partial thickness burns, incisions, skin grafts, donor sites, lacerations, and Stage I-1V dermal ulcers (vascular, venous, pressure and diabetic).
The AFM Ultra Ag Dressings are sterile, single-use wound care dressings for use in moist wound management. The dressings are comprised of 4 layers, each performing a specific function; an occlusive synthetic top layer, a polyurethane foam layer, a hotmelt adhesive and a layer of a silver-containing knitted composite fabric.
The provided document is a 510(k) summary for the AFM Ultra Ag Dressings. It focuses on demonstrating substantial equivalence to predicate devices rather than establishing novel performance criteria through a clinical study. Therefore, most of the requested information regarding acceptance criteria, study design, expert involvement, and ground truth establishment is not present in this type of submission.
However, I can extract the information that is available:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Explicitly Stated or Implied) | Reported Device Performance |
|---|---|
| Biocompatibility (suitability for intended use) | "Results of the biocompatibility tests demonstrate that the device is suitable for its intended use." |
| Antimicrobial effectiveness for the dressing | "Antimicrobial testing was performed which showed that AFM Ultra Ag Dressings provide an effective microbial barrier to the dressing itself." |
| Substantial Equivalence in design, function, and intended use to predicate devices (K051445 and K022416) | "Milliken believes that the data included in this submission including the technical characteristics, physical properties, silver extraction, zone-of-inhibition and antimicrobial testing demonstrates that AFM Ultra Ag Dressings are substantially equivalent in design, function and intended use to Milliken Silver Wound Dressings (K051445) and Contreet Foam Adhesive/Nonadhesive (K022416)." |
| Safety and Effectiveness (not adversely affected by construction/silver concentration differences) | "The differences between AFM Ultra Ag Dressings and the predicate devices include construction details and slightly different silver concentrations, which are minor and do not affect safety and effectiveness of the device, as demonstrated by the biocompatibility and efficacy testing." |
2. Sample size used for the test set and the data provenance:
- Not applicable. This submission relies on biocompatibility and in vitro antimicrobial testing, along with a comparison of technical characteristics to predicate devices. There is no mention of a clinical "test set" with patient data.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. No clinical test set with ground truth established by experts is described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not applicable. No clinical 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:
- Not applicable. This device is a wound dressing, not an AI-powered diagnostic tool. MRMC studies are irrelevant here.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
- Not applicable. This device is a wound dressing, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For biocompatibility: Ground truth would be established by standardized in vitro and in vivo biological evaluations according to ISO standards, assessing cellular response, irritation, sensitization, etc.
- For antimicrobial testing: Ground truth would be established by standardized microbiological assays (e.g., zone of inhibition, bacterial reduction tests) against specified microorganisms.
8. The sample size for the training set:
- Not applicable. This is not an AI/machine learning device.
9. How the ground truth for the training set was established:
- Not applicable. This is not an AI/machine learning device.
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