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510(k) Data Aggregation
(31 days)
POLYSYN SURGICAL SUTURE
POLYS YN™ Polyglycolic Acid (PGA) Suture is indicated for use in general soft tissue approximation and/or ligation, including use in ophthalmic procedures, but not for use in Cardiovascular and Neurological procedures.
PGA Sutures are supplied as braided or monofilament, dyed (violet) or undyed and coated or uncoated. The pigment for the violet is D&C Violet #2. Where applicable, the coating is a copolymer of polycarpolactone and calcium stearate. The PGA Suture is available in Size 2 through Size 10-0.
This document describes the 510(k) summary for the PolySynTM (PGA) Surgical Suture. The purpose of the submission is to demonstrate substantial equivalence to legally marketed predicate devices, specifically for additional diameter sizes (USP Size 7-0 through 10-0).
1. Table of Acceptance Criteria and Reported Device Performance:
The primary acceptance criteria for the PolySynTM (PGA) Surgical Suture are conformity to the USP monograph for absorbable sutures and demonstration of substantial equivalence to predicate devices through various performance tests.
Acceptance Criteria | Reported Device Performance |
---|---|
Conformity to USP monograph for absorbable sutures | "The results of this testing demonstrates that the PolySyn™ suture is substantially equivalent to the predicate devices." (Implies conformity to USP as part of substantial equivalence). |
Demonstration of substantial equivalence to predicate device including in vitro post-hydrolysis tensile testing | "The results of this testing demonstrates that the PolySyn™ suture is substantially equivalent to the predicate devices." |
2. Sample Size Used for the Test Set and Data Provenance:
The document does not explicitly state the specific sample sizes for each test in the non-clinical laboratory performance testing. It generally refers to "performance testing."
The data provenance is from non-clinical laboratory performance testing. The country of origin of the data is not explicitly stated, but it is implied to be generated by the applicant, Surgical Specialties Corporation, in the United States (given their address). The data is retrospective testing done to support the 510(k) submission.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:
Not applicable. This device is a surgical suture, and the evaluation is based on non-clinical laboratory performance testing against established standards (USP monograph) and predicate device characteristics, not on expert interpretation of medical images or patient data.
4. Adjudication Method for the Test Set:
Not applicable. The evaluation is based on objective laboratory measurements against predefined specifications and comparisons to predicate device data, not on subjective assessments 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, an MRMC study is irrelevant.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:
Not applicable. This is a traditional medical device (surgical suture), not an algorithm or AI system.
7. The Type of Ground Truth Used:
The ground truth used for evaluation is based on:
- USP (United States Pharmacopeia) monograph for absorbable sutures: These are established, scientifically vetted standards for the physical and performance characteristics of surgical sutures.
- Predicate device characteristics: These are the established performance parameters of the legally marketed devices (K965162 and K022269) to which the new device is compared for substantial equivalence.
8. The Sample Size for the Training Set:
Not applicable. This is a traditional medical device (surgical suture) and does not involve machine learning or AI, thus 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/ML algorithm, there is no ground truth established for it.
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