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APTUS® Proximal Humerus System is indicated for fractures, osteotomies and non-unions of the proximal humerus.

The APTUS® K-Wire System is intended for use in fixation of bone fractures, for bone reconstruction, and as guide pins for insertion of other implants.

The subject device includes four (4) plate designs to expand the range of Medartis APTUS® Proximal Humerus Fixation devices previously cleared in K120108, and two (2) additional K-wire designs, to expand the range of Medartis APTUS® K-Wires previously cleared in K092038. The subject plates are provided in 10-hole and 14-hole designs, each in anatomic designs for the right and left proximal humerus. The subject plates range in length from approximately 180 mm (10-hole plate) to approximately 230 mm (14-hole plate). The proximal region of the subject plates is identical to the devices previously cleared in K120108. This submission includes a spiral locking blade for use with the subject plates that is similar to the spiral blades cleared in K120108. The subject plates are to be used with previously cleared 3.5 mm diameter locking screws and non-locking (cortical) screws. The subject device plates and spiral blades are made of unalloyed titanium conforming to ASTM F67.

The subject device K-wires have a diameter of 2.0 mm and an overall length of 150 mm, and are provided with a trocar tip or lancet (bayonet) tip. The subject device K-wires are also compatible with the subject plates, and with the proximal humerus plates cleared in K120108. The subject K-wires are made of stainless steel conforming to ASTM F138.

The provided document describes a 510(k) premarket notification for a medical device, the APTUS® Proximal Humerus System. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device, rather than proving that the device meets specific acceptance criteria based on studies involving human subjects or AI performance metrics.

Therefore, many of the requested details, such as acceptance criteria, reported device performance (in terms of sensitivity, specificity, etc.), sample sizes for test sets (for AI), number of experts for ground truth, adjudication methods, MRMC studies, standalone AI performance, type of ground truth used (for AI), training set sample size, and ground truth establishment for training sets, are not applicable to this type of regulatory submission as presented in the document.

The performance data mentioned in the document are focused on non-clinical mechanical testing to demonstrate the structural integrity and equivalence of the new device components to existing predicate devices.

Here's an breakdown of the available information based on your request:

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

• Acceptance Criteria: Not explicitly stated as pass/fail thresholds for clinical performance or AI metrics. The acceptance is based on demonstrating "substantial equivalence" to predicate devices through mechanical testing and comparison of design, materials, and intended use. The implicit acceptance criterion is that the new components perform at least as well as, or are equivalent to, the predicate devices in terms of mechanical properties.
• Reported Device Performance:

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not applicable. This submission focuses on non-clinical mechanical testing, not a clinical trial or AI evaluation with a test set of patient data. Therefore, there's no "test set" in the sense of patient data.

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. No ground truth established with experts for clinical or AI performance. The "ground truth" here is the established mechanical performance of the predicate devices.

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

• Not applicable. No adjudication method for clinical or AI performance is described.

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 is not an AI device. No MRMC study was performed or is relevant to this submission.

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

• Not applicable. This is not an algorithm or AI device.

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

• The "ground truth" in this context refers to the established mechanical and material properties of the predicate devices. The subject device's performance was compared against these established properties to demonstrate substantial equivalence.

8. The sample size for the training set

• Not applicable. This is not an AI device.

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

• Not applicable. This is not an AI device.

Summary from the document regarding the study:

The study conducted for the APTUS® Proximal Humerus System was a non-clinical evaluation designed to demonstrate substantial equivalence to previously cleared predicate devices. It involved:

• Biocompatibility assessment: Referenced from previous K-clearances (K120108 and K092038), stating that the materials (unalloyed titanium and stainless steel) are identical, supporting biocompatibility equivalence.
• Mechanical testing: Performed according to ASTM F382, demonstrating substantial equivalence to predicate devices K120108 and K041860. This standard likely involves various tests of mechanical properties relevant to bone fixation devices.
• Engineering analysis calculations: Specifically for out-of-plane bending moments of inertia, showing equivalence.
• Comparative dynamic testing: In a simulated fracture model, also demonstrating substantial equivalence to K120108 and K041860.

This approach is standard for 510(k) submissions for orthopedic implants, where the focus is on physical and mechanical properties, material equivalence, and design similarity to already validated devices, rather than clinical performance trials or AI specific evaluations.

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