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The STERN FIX Sternal Stabilization System is intended for closure and stabilization of the sternum following sternotomy, through the intercostal spaces, in order to promote its fusion.

The STERN FIX is a long-term implantable postoperative sternal stabilization system that closes and stabilizes the sternum after a sternotomy. The STERN FIX is a clamping device consisting of two parts, male and female, which match telescopically with one another to form the sternal stabilization system. Both male and female parts have a curved arm that catches one of the two halves of the sternum laterally and approximates them until the sternum is securely fixed. Five sizes of the STERN FIX are available for use with different sternum thicknesses. The STERN FIX can be cut and removed for emergent, and long-term, re-entry through the sternum. The predicate device was originally made of PEEK-OPTIMA™. This 510(k) introduces a new variant of the product made of carbon-fiber reinforced (CFR) PEEK-OPTIMA™. This new variant also presents some minor geometry changes with respect to the predicate device, to adapt the product to the new material.

The provided document is a 510(k) Premarket Notification for the STERN FIX Sternal Stabilization System and primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device (K211613). It does not present acceptance criteria or detailed study results for a medical device that relies on AI/ML or requires extensive clinical performance testing with human readers.

The device in question is a physical implant used for sternal closure, not a diagnostic or AI-powered imaging device. Therefore, many of the requested criteria (such as multi-reader multi-case studies, ground truth establishment for AI/ML, and training/test set sample sizes for algorithms) are not applicable to this type of medical device submission.

The document discusses performance testing but this refers to biomechanical and functional testing of the physical implant, not a study evaluating an AI algorithm's performance on a dataset. The "conclusions" under "Test" describe whether the physical components meet specifications.

Here's how the information provided in the document relates to your request, with an emphasis on what is not present given the nature of the device:

Device: STERN FIX Sternal Stabilization System

Date of Submission: June 16, 2022
FDA Review Date: October 19, 2022

Type of Device: Long-term implantable postoperative sternal stabilization system. It's a physical device, not an AI/ML diagnostic or assistive tool.

Purpose of Submission: To introduce a new variant of the product made of carbon-fiber reinforced (CFR) PEEK-OPTIMA™ with minor geometry changes, demonstrating substantial equivalence to the predicate device (STERN FIX Sternal Stabilization System - K211613).

1. A table of acceptance criteria and the reported device performance:

The document describes performance testing for the physical device. The "acceptance criteria" are implied to be "meeting the specifications," and the "reported performance" is a general statement that the device meets these specifications. No specific quantitative criteria or numerical results are provided in the summary.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified for any of the performance tests. The document only states "All tested samples."
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). This is typically lab-based testing of manufactured units, not clinical data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not Applicable. This is a physical device. Ground truth, in the context of an AI/ML device, would involve expert labeling of medical images or data. For this device, "ground truth" relates to material properties and mechanical performance, established through engineering specifications and test standards.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

• Not Applicable. Pertains to expert review of data/images for AI/ML.

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. This is not an AI/ML device. "Clinical testing" was explicitly stated as "No clinical testing was performed to support this submission."

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

• Not Applicable. This is not an AI/ML device.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

• For this physical device, the "ground truth" is defined by engineering specifications and recognized consensus standards (e.g., ASTM F2026-17, ISO 14971, ISO 14630 for general requirements, biomechanical behavior, and risk management). The tests performed (breaking strength, fatigue, lateral distraction) are designed to confirm compliance with these pre-defined standards and specifications, which serve as the "ground truth" for performance.

8. The sample size for the training set:

• Not Applicable. This is a physical device, not an AI/ML algorithm requiring a training set.

9. How the ground truth for the training set was established:

• Not Applicable. As above, no training set for an AI/ML algorithm.

Summary:

This 510(k) submission successfully demonstrated substantial equivalence of a modified physical sternal stabilization system to a predicate device. The evidence provided focused on material composition changes, minor geometry adjustments, and performance testing (functional and biomechanical) against established engineering specifications and recognized consensus standards. Since the device is not an AI/ML system, the typical acceptance criteria and study designs associated with AI/ML (e.g., MRMC studies, ground truth for image labeling, training/test set details) are irrelevant to this submission and are therefore not present. No clinical testing, in the sense of human subject trials, was performed or required for this 510(k).

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The STERN FIX Sternal Stabilization System is intended for closure and stabilization of the sternum following sternotomy, through the intercostal spaces, in order to promote its fusion.

The STERN FIX is a long-term implantable postoperative sternal stabilization system that closes and stabilizes the sternum after a sternotomy. The STERN FIX is a clamping device consisting of two parts, male and female, which match telescopically with one another to form the sternal stabilization system. Both male and female parts have a curved arm that catches one of the two halves of the sternum laterally and approximates them until the sternum is securely fixed. Five sizes of the STERN FIX are available for use with different sternum thicknesses. The STERN FIX can be cut and removed for emergent, and long-term, re-entry through the sternum.

Here's an analysis of the provided text regarding the Stern Fix Sternal Stabilization System, focusing on acceptance criteria and study details.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are generally framed as the device meeting "specifications" and demonstrating "adequate performance." The document does not provide a specific table of quantitative acceptance criteria with numerical targets. Instead, it offers qualitative conclusions based on performance testing.

2. Sample Sizes and Data Provenance

The document does not specify the exact sample sizes used for the functional, biomechanical, or cadaveric testing. It consistently refers to "All tested samples," but the number of samples is not provided.

• Test Set Sample Size: Not specified.
• Data Provenance: The studies appear to be in-house or contracted laboratory testing based on the context. No country of origin is mentioned, nor is it specified if the data is retrospective or prospective. Given the nature of the testing (functional, biomechanical, cadaveric), it's inherently prospective data generation for this submission.

3. Number of Experts and Qualifications for Ground Truth of Test Set

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

For the cadaver testing, it mentions "Correct devices implantation is verified in a simulated real-life situation," which implies assessment by individuals with expertise in surgical procedures or device implantation, but their specific qualifications or number are not detailed. For the other tests (breaking strength, fatigue, lateral distraction), "ground truth" refers to the measured physical properties against established engineering specifications, rather than expert interpretation of medical images or observations.

4. Adjudication Method for the Test Set

Not applicable. The tests described are objective measurements of physical properties (e.g., breaking strength, fatigue resistance) or direct observation of device implantation in cadavers, not subjective interpretations requiring adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The document explicitly states: "No clinical testing was performed to support this submission." The studies were limited to performance testing (functional, biomechanical, cadaveric) in a non-clinical setting.

6. Standalone (Algorithm Only) Performance Study

Not applicable. The Stern Fix Sternal Stabilization System is a physical medical device (an implant for sternal closure), not an AI algorithm. Therefore, "standalone (algorithm only)" performance is not relevant to this device.

7. Type of Ground Truth Used

• Functional and Biomechanical Testing: The ground truth is based on engineering specifications and physical measurements (e.g., in a material testing machine) which determine if the device meets required strength, durability, and resistance parameters.
• Cadaver Testing: The ground truth is the successful and correct implantation of the device in a simulated real-life situation, likely assessed by observing the implementation and stability of the device during and after placement. This would be observed performance against an expected surgical/mechanical outcome.

8. Sample Size for the Training Set

Not applicable. This is a physical medical device, not an AI or machine learning model that requires a training set.

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

Not applicable, as no training set was used.

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