(270 days)
Corplex P/Theracor P/Allacor P is indicated for use in the management of the following wounds:
- Partial and full-thickness wounds
- Pressure ulcers
- Venous ulcers
- Diabetic ulcers
- Chronic vascular ulcers
- Tunneled/undermined wounds
- Surgical wounds (donor sites/grafts, post-Moh's surgery, post-laser surgery, podiatric, wound dehiscence)
- Trauma wounds (abrasions, lacerations, partial-thickness burns, and skin tears)
- Draining wounds
Corplex P/Theracor P/Allacor P is derived from human umbilical cord extracellular matrix (ECM) and is indicated for the management of a range of acute and chronic wounds. As a resorbable particulate device, Corplex P/Theracor P/Allacor P is lyophilized and packaged in a sterile vial, allowing the device to be rehydrated and applied directly to the wound.
This document describes a 510(k) premarket notification for a wound dressing device, Corplex P/Theracor P/Allacor P. It focuses on demonstrating substantial equivalence to a predicate device, Myriad Particles, rather than proving the device meets specific performance criteria through a comparative effectiveness study in the context of an AI-powered medical device.
Therefore, many of the requested categories related to AI device performance, such as sample size for test sets, data provenance, expert ground truth establishment, MRMC studies, standalone performance, and training set details, are not applicable to the information provided in this 510(k) submission.
This submission is about a traditional medical device (wound dressing) aiming for clearance based on substantial equivalence, not a novel AI/ML device that requires extensive clinical validation of its algorithm's performance against human readers or a robust ground truth.
Here's an attempt to answer the applicable questions based on the provided text:
1. Acceptance Criteria and Device Performance
The submission does not outline specific, quantified performance "acceptance criteria" in the way one would for an AI algorithm's diagnostic accuracy (e.g., "sensitivity must be >X%, specificity >Y%"). Instead, it demonstrates "substantial equivalence" to a predicate device by comparing various technological characteristics and presenting results of non-clinical (performance and biocompatibility) and clinical (human repeat insult patch test, skin prick test) testing to show that differences do not raise new safety or effectiveness concerns.
Table of Performance Testing (Non-Clinical):
| Test | Result |
|---|---|
| Pour Test – Dry | PASS |
| Solution Compatibility Test | PASS |
| Digestion Assay | PASS |
| Pour Test – Wet | PASS |
| Absorption Test | PASS |
| Evaporation Test | PASS |
| Extracellular Matrix Characterization | PASS |
Table of Biocompatibility Testing Results (All Met Requirements):
- Cytotoxicity: ISO 10993-5:2009
- Materials Mediated Pyrogenicity: ISO 10993-11:2017
- Sensitization: ISO 10993-10:2010
- Acute Systemic Toxicity: ISO 10993-11:2017
- Intracutaneous Reactivity: ISO 10993-10:2021
- Implantation: ISO 10993-6:2016
- Chemical Characterization and Toxicological Risk Assessment: ISO 10993-18:2020, ISO 10993-17:2002, ISO 21726:2019
- Genotoxicity: 10993-3:2014, ISO 10993-33:2015
- Viral Risk Assessment and Clearance Study
- Endotoxin: ANSI/AAMI/ST72
- Packaging System Cytotoxicity: ISO 10993-5:2009
Clinical Testing:
- Human Repeat Insult Patch Test
- Skin Prick Test
The text states that these tests were conducted to "demonstrate substantial equivalence... or to mitigate any potential performance risks" and that results "meet the requirements" (for biocompatibility). No specific numerical thresholds or performance metrics are provided for these tests, as the goal is conformance to standards and equivalence, not a quantitative measure of superior performance an AI device might exhibit.
2. Sample Size for the Test Set and Data Provenance
Not applicable in the context of an AI/ML device's test set. The clinical testing mentioned (Human Repeat Insult Patch Test, Skin Prick Test) would have used human subjects, but their sample sizes are not disclosed in this document. These are typical safety evaluations for skin-contacting devices, not performance evaluations like those for AI.
3. Number of Experts used to establish the Ground Truth for the Test Set and the Qualifications of those Experts
Not applicable. Ground truth, in the AI context of expert consensus, is not relevant here. The "ground truth" for this device's testing would be defined by the results of the specific ASTM, ISO, or other standardized tests performed (e.g., a certain level of endotoxin, or a negative cytotoxicity finding). These are objective measurements from laboratory tests, not subjective interpretations by human experts.
4. Adjudication Method for the Test Set
Not applicable. There's no subjective interpretation requiring adjudication in the tests described.
5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done
No, this is not an MRMC study. This is a submission for a wound dressing device, not an AI diagnostic tool.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. There is no algorithm or AI component in this device.
7. The Type of Ground Truth Used
The "ground truth" for this medical device's clearance is a combination of:
- Conformance to established standards: e.g., ISO 10993 series for biocompatibility.
- Comparison to a predicate device: showing that the new device's characteristics and performance are "substantially equivalent" and do not raise new safety or effectiveness concerns compared to a legally marketed device.
- Laboratory test results: demonstrating properties like moisture content, dissolution, etc., meet internal specifications or are comparable to the predicate.
8. The Sample Size for the Training Set
Not applicable. This is not an AI device that requires a training set.
9. How the Ground Truth for the Training Set was Established
Not applicable.
N/A