(83 days)
NU-GEL* Wound Dressing is a sterile hydrogel formulation of preserved polyvinyt pyrrolidone in water. The gel is supported by a fusible fiber fabric scrim and protected on both sides by polyethylene film. NU-GEL* Wound Dressing maintains a moist wound environment. A moist wound environment supports the wound healing process by encouraging autolytic debridement thus enabling granulation to proceed under optimum conditions. It protects against dehydration, bacterial contamination and absorbs exudate from the wound.
NU-GEL Wound Dressing is indicated for dry, light and moderately exuding partial and full thickness wounds such as:
- First and second degree burns .
- Severe sunburns ◆
- Superficial injuries, superficial lacerations, cuts, abrasions, incisions/surgical . wounds, and skin tears
NU-GEL* Wound Dressing should be used under health care professional direction for the following indications:
- Burns caused by radiation oncology procedures .
- Pressure ulcers Stage I-IV .
- Lower extremity ulcers .
- . Venous ulcers
- Arterial ulcers .
- Ulcers of mixed etiology ●
- Diabetic ulcers ●
- Donor sites and skin grafts .
NU-GEL* Wound Dressing is a sterile hydrogel formulation of preserved polyviny pyrrolidone in water. The gel is supported by a fusible fiber fabric scrim and protected on both sides by polyethylene film. NU-GEL* Wound Dressing maintains a moist wound environment. A moist wound environment supports the wound healing process by encouraging autolytic debridement thus enabling granulation to proceed under optimum conditions. It protects against dehydration, bacterial contamination and absorbs exudate from the wound.
Here's an analysis of the provided text regarding the NU-GEL Wound Dressing, structured to address your specific requests.
Based on the provided document, the "acceptance criteria" discussed are related to the biocompatibility and wound healing performance of the NU-GEL Wound Dressing, primarily for demonstrating substantial equivalence to a predicate device. This is a 510(k) submission, which focuses on demonstrating equivalence rather than a full clinical trial for novel device approval.
It's important to note that this submission describes preclinical (animal) studies and in-vitro tests, not clinical studies involving human subjects or AI algorithms. Therefore, many of your specific questions related to human reader studies, AI assistance, and large-scale clinical data provenance are not applicable to this 1998 510(k) document.
Acceptance Criteria and Reported Device Performance
| Acceptance Criteria (Test) | Reported Device Performance (Results) |
|---|---|
| Cytotoxicity | Non-toxic |
| Primary Skin Irritation | Non-irritating |
| Wound Healing (Guinea Pig Excision) | No Adverse Effects |
| Burn Healing Study (Mice) | Reduced Inflammation - equivalent to immediately immersing the burn in ice water for 3 minutes |
| Porcine Partial Thickness Wound Healing | No Adverse Effects |
| Porcine Burn Wound Healing | No Adverse Effects |
| Kligman Chamber Scarification | Irritation Potential - Low |
Study Details (Preclinical/Benchtop)
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Sample size used for the test set and the data provenance:
- Sample Size: Not explicitly stated for each test (e.g., number of guinea pigs, mice, or porcine subjects). The studies are described in terms of the animal model used.
- Data Provenance: Preclinical animal studies and in-vitro lab tests. The country of origin for these specific tests is not mentioned in the provided text. These are retrospective in the sense that the data was collected and then submitted, but they are prospective in their study design (i.e., experiments were designed and then conducted).
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- Not applicable. These are preclinical tests in animal models and in-vitro studies, not clinical studies requiring expert interpretation of medical images or human patient outcomes. The "ground truth" for these tests would be objective measurements or observations by the researchers conducting the animal and lab studies.
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Adjudication method for the test set:
- Not applicable. This concept typically refers to expert consensus in clinical studies, which is not relevant here. The results of the biocompatibility and animal healing studies are likely based on direct observation, histological analysis, and standardized scoring methods within the preclinical research setting.
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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, this device (a wound dressing from 1998) does not involve AI or human readers in the context of diagnostic interpretation. Therefore, an MRMC study with AI assistance is not applicable.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- No, this device is a physical wound dressing and does not involve any algorithm or software. Therefore, a standalone algorithm performance study is not applicable.
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The type of ground truth used:
- Preclinical Endpoints:
- Cytotoxicity: Measured in-vitro, likely by assessing cell viability or metabolic activity in culture after exposure to the device material.
- Skin Irritation/Kligman Chamber Scarification: Assessed through visual scoring of skin reactions (e.g., erythema, edema) on animal subjects (or human volunteers for Kligman, though not specified here) according to standardized protocols.
- Wound/Burn Healing (Guinea Pig, Mice, Porcine): Assessed through gross observation of wound closure, visual assessment of inflammation, histological analysis of tissue samples, and potentially planimetry (wound area measurement) to track healing progression. "No Adverse Effects" or "Reduced Inflammation" are the reported ground truths.
- Preclinical Endpoints:
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The sample size for the training set:
- Not applicable. This device does not use machine learning or AI, so there is no concept of a "training set" in the context of data used to train an algorithm. The preclinical studies themselves could be considered "training" in a broad sense for product development, but not for an algorithm.
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How the ground truth for the training set was established:
- Not applicable, as there is no training set for an AI algorithm. The ground truth for the preclinical studies was established through the scientific methods appropriate for each test (e.g., lab assays, animal observations, histology).
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