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510(k) Data Aggregation
(28 days)
The SJM™ Attune Annuloplasty Ring is indicated for use in the repair of a mitral or tricuspid valve that is diseased or damaged due to acquired or congenital valvular disease.
The SJM Attune Flexible Adjustable Annuloplasty Ring is a fully flexible ring fabricated from a medical grade silicone core surrounded by a polyester sewing cuff and containing a suture that will allow for adjustment after implantation.
This submission (K092876) describes the SJM Attune Flexible Adjustable Annuloplasty Ring, which is a fully flexible ring for mitral or tricuspid heart valve repair. The submission focuses on demonstrating substantial equivalence to a predicate device (SJM Attune Ring, K083835) and primarily relies on non-clinical performance testing.
1. A table of acceptance criteria and the reported device performance
The provided document details that the SJM Attune Flexible Adjustable Annuloplasty Ring was found to be substantially equivalent to its predicate device (K083835) based on a comparison of technological characteristics and non-clinical performance testing. The specific acceptance criteria and detailed performance results from these tests are not provided in this 510(k) summary. The document lists the types of tests performed but does not quantify the results or state the specific criteria for acceptance for each.
| Acceptance Criteria (Not explicitly stated in the document) | Reported Device Performance (Implied as meeting equivalence) |
|---|---|
| Ring Tensile Strength requirements | Performance deemed substantially equivalent to predicate |
| Suture Pullout Test requirements | Performance deemed substantially equivalent to predicate |
| Adjustment Suture Testing requirements | Performance deemed substantially equivalent to predicate |
| MR Safety Evaluation requirements | Performance deemed substantially equivalent to predicate |
| Biological Evaluation requirements | Performance deemed substantially equivalent to predicate |
| Sterilization Parameter Evaluation requirements | Performance deemed substantially equivalent to predicate |
| Manufacturing and Design specifications | Substantially equivalent to predicate |
2. Sample sizes used for the test set and the data provenance
The document does not specify the sample sizes used for the non-clinical tests (Ring Tensile Strength, Suture Pullout Test, etc.). It also does not mention the data provenance in terms of country of origin or whether it was retrospective or prospective, as these are typically not applicable to non-clinical bench or laboratory testing.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This information is not applicable as the study involved non-clinical performance testing (bench testing) and not studies requiring expert interpretation or ground truth establishment in a clinical context.
4. Adjudication method for the test set
This information is not applicable as the study involved non-clinical performance testing and not studies requiring adjudication of output.
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
No such study was conducted or mentioned in this 510(k) submission. The device is an annuloplasty ring for heart valve repair, not an AI diagnostic or assistance tool.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
No standalone algorithm performance study was conducted. The device is a physical medical implant.
7. The type of ground truth used
This information is not applicable as the studies were non-clinical performance tests, not clinical studies requiring a ground truth (e.g., pathology, expert consensus). The "ground truth" for the non-clinical tests would be the established engineering/performance standards and the characteristics of the predicate device.
8. The sample size for the training set
This information is not applicable as the device is a physical medical implant, not an AI/machine learning model that requires a training set.
9. How the ground truth for the training set was established
This information is not applicable as the device is a physical medical implant, not an AI/machine learning model.
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