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The Prevena Incision Management System is intended to manage the environment of surgical incisions that continue to drain following sutured or stapled closure by maintaining a closed environment and removing exudate via the application of negative pressure wound therapy.

Negative pressure wound therapy system for application to surgically closed incisions.

The provided document is a 510(k) Summary for the Prevena Incision Management System with Customizable Dressing. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed performance metrics for a new or significantly modified device. Therefore, much of the requested information regarding detailed acceptance criteria, specific device performance, sample sizes for test and training sets, expert ground truthing, and MRMC studies is not present in this document.

However, based on the information provided, here's what can be extracted and what cannot:

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

Study Proving Acceptance Criteria:

The document states: "The Prevena Incision Management System with Customizable Dressing was evaluated under a number of design verification and validation tests to assure safety, efficacy, conformance to design specifications and equivalence to the predicate device."

Specifically, the following tests were conducted:

• Biocompatibility testing according to ISO 10993-1.
• Equivalency testing with respect to delivery of negative pressure wound therapy.

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

• Sample size for test set: Not specified. The document only mentions "design verification and validation tests" and "non-clinical tests."
• Data provenance: Not specified. As these were non-clinical tests, the concept of "country of origin" for patient data isn't directly applicable.

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. The tests mentioned are non-clinical (biocompatibility and functional equivalency) and do not involve human interpretation or ground truthing by medical experts in the way an AI diagnostic device would.

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

• Not applicable. The tests mentioned are non-clinical and do not require expert adjudication.

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. The document explicitly states: "No clinical tests were necessary." This device is not an AI diagnostic tool that would typically involve MRMC studies.

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

• Not applicable. This is a medical device (negative pressure wound therapy system), not an algorithm or AI. The "performance" refers to its mechanical and biological functionality, not an analytical algorithm's output.

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

• For biocompatibility: Ground truth would be established by validated laboratory assays and compliance with ISO 10993-1 standards.
• For equivalency in negative pressure delivery: Ground truth would be established by engineering specifications and direct measurement/comparison to the predicate device's performance.

8. The sample size for the training set

• Not applicable. This is not an AI/machine learning device that requires a training set.

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

• Not applicable. This is not an AI/machine learning device that requires a training set.

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The Prevena Incision Management System is intended to manage the environment of surgical incisions that continue to drain following sutured or stapled closure by maintaining a closed environment and removing exudate via the application of negative pressure wound therapy.

Negative pressure wound therapy system for application to surgically closed incisions.

The provided text describes a 510(k) submission for the "Prevena Incision Management System with Customizable Dressing." The purpose of the submission is to demonstrate substantial equivalence to a predicate device, not to prove the device meets specific acceptance criteria in the typical sense of a new clinical claim. Therefore, much of the requested information regarding detailed acceptance criteria, specific performance metrics, sample sizes for test/training sets, expert adjudication methods, MRMC studies, or standalone algorithm performance is not present in this regulatory document.

However, based on the provided text, I can extract and infer some information regarding the "acceptance criteria" (understood here as the demonstration of substantial equivalence) and the studies conducted to support it.

1. Table of "Acceptance Criteria" and Reported Device Performance

For a 510(k) submission, "acceptance criteria" are generally framed around demonstrating that the new device is as safe and effective as a legally marketed predicate device, and does not raise new questions of safety or effectiveness. The key "performance" here is equivalence to the predicate.

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

The document does not explicitly state the sample sizes for any test sets used in the biocompatibility, equivalency, or software verification and validation tests. The term "test set" as typically used in AI/Machine Learning evaluation (i.e., a dataset of cases used to evaluate an algorithm's performance) is not applicable here as this is a medical device regulatory submission focused on mechanical and functional equivalence, not an AI product.

Data provenance (e.g., country of origin, retrospective/prospective) is not mentioned as the studies are likely laboratory-based and engineering verification/validation tests, not clinical studies with patient data in the typical sense.

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 to the type of studies described. The "ground truth" for these tests would be established by engineering specifications, validated measurement techniques, and regulatory standards (e.g., ISO 10993-1) rather than expert consensus on medical images or clinical outcomes.

4. Adjudication method for the test set

Not applicable. Adjudication methods (e.g., 2+1, 3+1) are typically used in clinical studies or for establishing ground truth in AI datasets where expert opinions might differ. The studies mentioned (biocompatibility, equivalency testing, software V&V) are engineering and laboratory-based.

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 comparative effectiveness study was done or mentioned. This device is not an AI-assisted diagnostic or prognostic tool for human readers; it is a negative pressure wound therapy system.

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

This question is not applicable. The device is a physical medical device (negative pressure wound therapy system), not an algorithm or AI.

7. The type of ground truth used

The "ground truth" in this context refers to the established standards or predicate device performance against which the new device is compared.

• Engineering Specifications: For functional performance (e.g., negative pressure delivery).
• Regulatory Standards: For biocompatibility (ISO 10993-1).
• Predicate Device Performance: For demonstrating equivalence in indicated wound types, functional components, and therapy unit operation.

8. The sample size for the training set

Not applicable. This device is not an AI/Machine Learning algorithm, so there is no "training set."

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

Not applicable, as there is no training set for this device.

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