Search Results

Stainless Steel Suture is indicated for use in abdominal wound closure, hernia repair, sternal closure and orthopaedic procedures including cerclage and tendon repair.

Stainless Steel Suture is a sterile monofilament or multifilament, non-absorbable sterile surgical suture composed of 316L stainless steel. The suture is also available coated with polyethylene. Stainless Steel Suture is available in a range of gauge sizes and lengths, non-needled or attached to needles of various types and sizes. Stainless Steel Suture complies with the requirements of the European Pharmacopoeia (Ph. Eur.) for Sterile Non-Absorbable Strands and the United States Pharmacopoeia (USP) for Non Absorbable Surgical Sutures. The European Pharmacopoeia recognizes units of measure Metric and Ph. Eur. sizes as equivalent which is reflected on the labeling.

Stainless Steel Suture elicits a minimal acute inflammatory reaction in tissue and is not absorbed. Implantation studies in animals show that no significant change in the retention of tensile strength of the suture occurs during the lifetime of the implantation.

The provided text describes a 510(k) premarket notification for a medical device called "Stainless Steel Suture" by Ethicon Incorporated. This is not a study proving device performance against acceptance criteria in the manner typically seen for new, innovative devices with novel performance characteristics or AI components. Instead, it's a submission to demonstrate substantial equivalence to a legally marketed predicate device.

Therefore, many of the requested categories (like sample sizes for test/training sets, number of experts, adjudication methods, MRMC studies, standalone performance of an algorithm, and ground truth types related to performance metrics) are not applicable in this context because the FDA cleared this device based on its substantial equivalence to an existing device, rather than novel performance data from a clinical or algorithm-based study.

The primary "acceptance criteria" here relate to demonstrating that the new device is as safe and effective as the predicate device, with a focus on updated labeling for MRI safety. The "study" isn't a complex clinical trial or AI algorithm validation, but rather a series of bench and design verification tests and a comparison to the predicate.

Here's the information extracted and adapted to the prompt, with explanations for why certain fields are not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated as this was a substantial equivalence submission based on bench testing of the product, not a clinical study with patient data. The tests would have used appropriate numbers of suture samples per the relevant ASTM and USP standards.
• Data Provenance: The data provenance is from bench/design verification activities performed by Ethicon Inc., in compliance with design control requirements. The data is prospective in the sense that these tests were conducted specifically for this 510(k) submission on the device to verify compliance with standards and MRI safety guidelines. Country of origin not specified, but likely where Ethicon's primary R&D/manufacturing takes place (e.g., USA given the submitter's address).

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of those Experts

• Not Applicable. For a device like a stainless steel suture cleared via substantial equivalence, ground truth as typically defined for AI or diagnostic studies (i.e., expert consensus on clinical findings, pathology confirmation) is not relevant. The "ground truth" for the bench tests would be the established scientific/engineering principles and the specifications within the referenced ASTM and USP standards. The "experts" involved are the testing personnel and engineers who conducted and validated the bench tests according to recognized industry standards.

4. Adjudication Method for the Test Set

• Not Applicable. Adjudication methods (like 2+1 or 3+1 consensus) are typically used in clinical studies or AI algorithm validation to resolve discrepancies among human readers or between AI and human reads. This submission is based on objective bench testing against pre-defined engineering and safety standards, not subjective interpretations requiring adjudication.

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 surgical suture, not an AI-powered diagnostic or assistive tool. Therefore, no MRMC study involving human readers or AI assistance was conducted or is relevant for its clearance.

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

• Not Applicable. This device is a physical surgical implant, not an algorithm or software. No standalone algorithm performance testing was performed.

7. The Type of Ground Truth Used

• Engineering Standards and Specifications: The "ground truth" for this device's performance is compliance with established and recognized engineering and medical device standards, specifically:
  • United States Pharmacopoeia (USP) monographs for sutures ( Needle Attachment, Tensile Strength, Diameter).
  • ASTM Standards for MRI Safety (F2503, F2052, F2119, F2182, F2213).
  • AAMI/ANSI/ISO 10993-7 for EO residual testing.
  • ANSI/AAMI ST72:2011 and USP , for Bacterial Endotoxin Testing (BET).
  • European Pharmacopoeia (Ph. Eur.) for Sterile Non-Absorbable Strands.
  • The "ground truth" also stems from the long history of safe and effective use of the predicate device (Surgical Stainless Steel Suture, K946173) which the proposed device is substantially equivalent to.

8. The Sample Size for the Training Set

• Not Applicable. This is not an AI/machine learning device, so there is no training set.

9. How the Ground Truth for the Training Set Was Established

• Not Applicable. As there is no training set for an AI algorithm, this question is not relevant.

Ask a specific question about this device