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510(k) Data Aggregation
(150 days)
Augmented VaultLock Glenoid
The Augmented VaultLock Glenoid is indicated in replacement(s) when conditions include severe pain or significant disability resulting from degenerative, traumatic disease, or injury of the glenohumeral joint; non-union humeral head fractures of long duration; irreducible 2- and 4- part proximal humeral fractures; avascular necrosis of the humeral head; or, other difficult clinical management problems where arthrodesis or resectional arthroplasty is not acceptable.
The glenoid components are designed fixation in the joint and must only be used with appropriate bone cement.
The Augmented VaultLock Glenoid is made of the same materials as the predicate (UHMWPE). The Augmented VaultLock Glenoid is designed with a half-wedge augment. The proposed device has an identical spherical articulating surface as that of the previously cleared glenoids and is available in 4 nominal sizes. The proposed device is a line extension to the Arthrex VaultLock Glenoid cleared under K161108.
This document is a 510(k) premarket notification for the "Augmented VaultLock Glenoid" by Arthrex Inc. It is a medical device, specifically a shoulder prosthesis. The document mainly focuses on proving the substantial equivalence of the new device to existing predicate devices, rather than establishing acceptance criteria and proving performance through a standalone clinical study with specific metrics like sensitivity, specificity, or reader improvement.
Therefore, many of the requested details, such as sample size for test sets, data provenance, number of experts for ground truth, adjudication methods, multi-reader multi-case studies, and details on training sets for an AI device, are not applicable to this type of submission. This is a submission for a physical medical implant, not an AI or algorithmic diagnostic device.
The "Performance Data" section in the document refers to mechanical testing and biocompatibility testing, which are standard for physical implants, not clinical performance metrics in the way typically discussed for diagnostic algorithms.
Here's an attempt to fill in the table and address the questions based only on the provided text, recognizing that many fields will be "Not Applicable" for this type of device submission.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Acceptance Criteria (if stated) | Reported Device Performance |
|---|---|---|
| Mechanical Testing | Meets standards requirements (per ASTM F2028) | Demonstrated that the proposed device meets standards requirements (Rocking horse testing) |
| Biocompatibility | Meets pyrogen limit specifications (per ANSI/AAMI ST72:2011/(R)2016, USP , USP , EP 2.6.14) | Bacterial Endotoxin test conducted and meets specifications |
| MRI Safety | N/A (implied to be safe in MR environment) | MRI testing conducted in accordance with FDA guidance and ASTM F2182 |
| Substantial Equivalence | Demonstrates equivalence to predicate device in terms of design features and intended use, with minor differences not raising safety/effectiveness questions. | Conclusion states: "The mechanical testing data demonstrates that the proposed device performance is equivalent to the predicate device for the desired indications. Any differences between the proposed device and the predicate device are considered minor and do not raise questions regarding safety or effectiveness." |
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. This submission describes mechanical and biocompatibility testing for a physical implant, not a clinical test set for an algorithmic device to establish diagnostic performance. The "tests" mentioned are physical and laboratory-based.
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. Ground truth establishment by experts is relevant for diagnostic algorithms or subjective clinical assessments. This submission focuses on engineering performance (mechanical strength, material safety).
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
- Not applicable. Adjudication methods are typically used for reconciling expert opinions in diagnostic studies. This is not a diagnostic 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. An MRMC study is relevant for evaluating the impact of AI on human reader performance in diagnostic tasks. This device is a physical shoulder implant.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Not applicable. This device is a physical medical implant, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- The "ground truth" for this device's performance is based on engineering standards (e.g., ASTM F2028 for mechanical testing) and international standards for biocompatibility (e.g., ANSI/AAMI ST72). Compliance with these established industry standards serves as the benchmark for safety and performance in this context.
8. The sample size for the training set
- Not applicable. There is no "training set" in the context of an AI/algorithmic device for this physical implant.
9. How the ground truth for the training set was established
- Not applicable. There is no "training set" for this physical implant.
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