K Number
K181428
Date Cleared
2019-09-20

(476 days)

Product Code
Regulation Number
N/A
Panel
SU
Reference & Predicate Devices
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

Rx: Clyra Wound Irrigation Solution is intended for use by healthcare professionals for cleansing, moistening and debriding to remove wound debris from acute and chronic dermal lesions that are partial or full thickness wounds such as 1st and 2nd degree burns, stage I - IV pressure ulcers, stasis ulcers, abrasions and minor skin irritations, post surgical wounds, grafted and donor sites, in addition to moistening absorbent wound dressings.

Device Description

Clyra Wound Irrigation Solution is a clear hypotonic solution topically applied to skin and wound areas. The subject device is a wound management and cleansing solution that is intended for cleansing, irrigating, and debriding dermal wounds in addition to moistening absorbent wound dressings (e.g. gauze). The mechanical action of fluid moving across the wound provides for the mechanism of action and aids in the removal of foreign objects such as dirt and debris. Clyra Wound Irrigation Solution will be supplied in food grade 4 oz. plastic PET bottles with spray inserts and caps. Clyra Wound Irrigation contains Potassium Iodide and Copper Sulphate, which release iodine when combined, resulting in a concentration within the product of 250 parts per million (ppm). The iodine acts as a preservative to inhibit contamination within the solution.

AI/ML Overview

This document is a 510(k) summary for Clyra Wound Irrigation Solution, a wound cleansing solution. It demonstrates substantial equivalence to a predicate device, Puracyn Plus Skin and Wound Care (K133452).

There is no acceptance criteria or study proving device performance in the context of an AI/ML device provided in this document. The document describes a traditional medical device (wound irrigation solution) and its equivalence to a predicate device. Therefore, many of the requested fields regarding AI/ML performance metrics are not applicable.

Here's a breakdown of the information that is available or can be inferred, and where the requested information is not applicable:

1. Table of acceptance criteria and the reported device performance

  • Acceptance Criteria: For this type of device (a wound irrigation solution seeking 510(k) clearance), the "acceptance criteria" are primarily based on demonstrating substantial equivalence to a legally marketed predicate device. This involves showing similar indications for use, technological characteristics, and safety profiles. Specific quantitative or qualitative performance targets related to clinical effectiveness are not typically "acceptance criteria" for a 510(k) that relies on substantial equivalence for this device type, but rather evidence of biocompatibility and stability.
  • Reported Device Performance:
    • Pre-Clinical Testing:
      • pH: Tested (results not detailed, but implied to be acceptable for equivalence).
      • Shelf Life: 2 years (unopened, ambient temperature).
      • Chemical Stability: Tested (results not detailed).
      • Antimicrobial Preservative Effectiveness: Conforming to USP against P. aeruginosa, E. coli, S. aureus, C. albicans, and A. brasiliensis.
      • Biocompatibility (ISO 10993): Cytotoxicity (ISO 10993-5:2009), Sensitization (ISO 10993-10:2010), Irritation (ISO 10993-10:2010), Material mediated pyrogenicity, Chemical characterization and toxicological risk assessment for systemic toxicity. (All results implied to be acceptable for safety).
      • Porcine Wound Healing Study: Performed (results not detailed, likely for safety/efficacy comparison against predicate, or to support mechanism of action).

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

  • Not applicable as this is not an AI/ML device study. Biocompatibility and stability studies typically use material samples or animal models, not human "test sets" in the context of AI/ML performance evaluation. The provided document does not specify sample sizes for the pre-clinical tests performed, beyond noting the tests were conducted.

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)

  • Not applicable. This is not an AI/ML device study. Ground truth in this context would refer to established scientific and regulatory standards for biocompatibility and chemical stability, interpreted by qualified laboratory personnel, not clinical experts establishing ground truth for diagnostic accuracy in an AI/ML context.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

  • Not applicable. This is not an AI/ML device study.

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 is a wound irrigation solution, not an AI/ML diagnostic or assistive device.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

  • Not applicable. This is a wound irrigation solution, not an AI/ML algorithm.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

  • The "ground truth" for this device's safety and effectiveness determination is based on established scientific methods for chemical analysis, microbiology (USP ), biocompatibility (ISO 10993 standards), and shelf-life testing, as well as the demonstration of substantial equivalence to a legally marketed predicate device with a known safety and efficacy profile. "Expert consensus," "pathology," or "outcomes data" in the AI/ML sense are not the primary ground truth types here.

8. The sample size for the training set

  • Not applicable. This is not an AI/ML device study, so there is no training set in that context.

9. How the ground truth for the training set was established

  • Not applicable. This is not an AI/ML device study.

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