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The St. Jude Medical Mond™ RVOT Stylet is designed to place SJM right ventricular active fixation leads in the right ventricular outflow tract.

The St Jude Medical (SJM) Mond RVOT Stylet is an accessory implant tool, intended for use in the placement of SJM right ventricular active fixation leads. The Mond stylet is configured in a modified "J" shape designed to position the leads in the RVOT. The stylet uses the same materials and similar manufacturing processes as the currently approved J (4090 and 4091) bradycardia stylets and the S-75-X tachycardia stylets.

The provided text describes a 510(k) premarket notification for the Kogo92 Mond™ RVOT Stylet. This type of submission relies on demonstrating substantial equivalence to a legally marketed predicate device, rather than explicit acceptance criteria with numerical performance targets for the new device.

Therefore, the study summary focuses on device comparison testing and verification and validation to assert equivalency and conformance to requirements, rather than presenting a performance study with defined acceptance criteria for a new device's efficacy or accuracy.

Here's a breakdown based on the available information:

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

Since this is a submission for substantial equivalence based on a predicate device, there are no explicit performance acceptance criteria stated for the Kogo92 Mond™ RVOT Stylet itself. The "acceptance criteria" are implicitly met by demonstrating that the new device is as safe and effective as the predicate devices. The "reported device performance" is essentially that it functions similarly and uses similar materials and processes to the predicate.

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

The document does not specify a "test set" in the context of device performance metrics, as it is a substantial equivalence submission. It mentions "device comparison testing," "verification and validation activities," and "product verification" (documented in QTR 2170). These imply internal technical and functional tests rather than clinical studies with patient data. Therefore, details like sample size for a test set, country of origin, or retrospective/prospective data are not provided.

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)

This information is not applicable to the provided text. The submission is about demonstrating equivalence of an accessory implant tool based on its design, materials, and manufacturing processes, not about establishing "ground truth" for a diagnostic or AI-driven device.

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

This information is not applicable. There's no mention of a test set requiring adjudication in the context of expert consensus.

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 MRMC study was done, as this device is a medical accessory (a stylet) and not an AI-driven or diagnostic imaging tool. Therefore, there's no discussion of human reader improvement with or without AI assistance.

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

This is not applicable, as the device is not an algorithm or AI system. It's a physical medical tool.

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

"Ground truth" as typically understood in performance studies (e.g., for diagnostic accuracy) is not relevant here. The 'truth' established is that the device's characteristics (materials, function, sterilization) are equivalent to those of existing, approved predicate devices, and that verification and validation activities were successfully performed to requirements.

8. The sample size for the training set

This is not applicable, as the device is not an AI/ML algorithm that requires a training set.

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

This is not applicable, as the device is not an AI/ML algorithm that requires a training set.

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