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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 closed by maintaining a closed environment and removing exudate via the application of negative pressure wound therapy.

Negative pressure wound therapy system

The provided document is a 510(k) premarket notification for a medical device (Prevena Incision Management System). It does not describe a study involving an AI/Machine Learning device or outline particular acceptance criteria usually associated with such devices (e.g., sensitivity, specificity, AUC). Instead, it discusses the substantial equivalence of the device to a predicate device, focusing on functional and safety aspects for a medical device.

Therefore, I cannot extract the acceptance criteria, study details, sample sizes, expert qualifications, adjudication methods, MRMC study results, standalone performance, or ground truth information relevant to an AI/ML device from the provided text.

The document primarily focuses on:

• Device Description and Indications for Use: The Prevena Incision Management System manages surgical incisions by maintaining a closed environment and removing exudate via negative pressure wound therapy.
• Comparison to Predicate Device: The current device is substantially equivalent to a previously cleared Prevena Incision Management System (K121883). The core technology (negative pressure wound therapy) and primary components are the same.
• Technological Differences: Minor changes were made to the software to reduce "nuisance alerts" related to the Leak Alert and Low Battery Alert mute times and a slight increase in the air leak threshold.
• Non-Clinical Testing: Bench tests were conducted to confirm the therapy unit delivers negative pressure within specifications, software verification and validation, and battery life testing.
• Clinical Testing: No clinical tests were deemed necessary for substantial equivalence. However, usability testing was conducted with patient users to assess the impact of labeling changes due to software modifications, and all usability goals were met.

In summary, this document is for a traditional medical device demonstrating substantial equivalence, not an AI/ML device.

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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 closed environment and removing exudate yia the application of negative pressure wound therapy.

The Prevena Incision Management System provides surgical incision management via the application of negative pressure wound therapy over an incision site that has been surgically closed with sutures or staples. The Prevena System is applied to the incision site immediately after surgery for a minimum of 2 days up to a maximum of 7 days depending on the surgeon's preference. The Prevena Incision Management System consists of: A Prevena Dressing (Prevena Peel & Place Dressing or Prevena Customizable Dressing and A source of negative pressure wound therapy, which may be one of the following KCI therapy units with its associated canisters: Prevena 125 Therapy Unit ActiV.A.C. Therapy Unit V.A.C. Freedom Therapy Unit InfoV.A.C. Therapy Unit

The provided text describes the Prevena Incision Management System and its substantial equivalence to predicate devices. It presents a "Prevena Dressing Pressure Manifold Test" as the primary performance data, but the description of this test does not align with the typical structure of acceptance criteria and reported performance for an AI/ML device, nor does it provide the detailed information requested in the prompt.

Based on the provided information, it is not possible to fully answer the request as it pertains to an AI/ML device. The document is a 510(k) summary for a medical device that uses negative pressure wound therapy, not an AI/ML-driven diagnostic or prognostic tool. Therefore, many of the requested fields, such as "sample size used for the test set," "number of experts used to establish ground truth," "adjudication method," "MRMC study," "standalone performance," "training set size," and "how ground truth for training set was established" are not applicable in this context.

However, I can extract the information that is present concerning the device's performance testing for the non-AI device.

1. Table of acceptance criteria and the 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)

• Test set sample size: Not specified. The test involved a "simulated wound bed."
• Data provenance: Not specified, but likely laboratory testing. This was a non-clinical test.

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)

• Number of experts: Not applicable. This was a physical performance test of a mechanical device.
• Qualifications of experts: Not applicable.

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

• Not applicable. This was a physical performance test, not one requiring expert human judgment or 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

• Not applicable. This is not an AI device. The document mentions usability testing with clinicians and patients for instructional materials, but this is not an MRMC study comparing AI performance.

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

• Not applicable. This is not an AI algorithm. The pressure manifold test is inherently a "standalone" test of the device's physical performance, but not in the context of AI.

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

• The ground truth was based on the physical measurement of negative pressure distribution on a simulated wound bed. This is a functional performance metric, not a clinical "ground truth" in the diagnostic sense.

8. The sample size for the training set

• Not applicable. This is not an AI device, so there is no training set for an algorithm.

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

• Not applicable. There is no training set for an AI algorithm.

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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.

This K133232 510(k) summary describes the Prevena Incision Management System, a negative pressure wound therapy device.

Here's an analysis of the provided text regarding acceptance criteria and studies:

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

The provided document does not contain explicit acceptance criteria or a direct table comparing them to reported device performance. This 510(k) is a submission for a labeling change for an existing device, and it relies on substantial equivalence to previously cleared predicate devices (K123878 and K100821). The submission states: "There have been no technological changes to the predicate device for the purpose of the proposed labeling change."

Instead of performance criteria, the document asserts:

• "The Prevena Incision Management System and its predicate are substantially equivalent in terms of safety, function and indications for use."
• "The safety and effectiveness of the Prevena Incision Management System is adequately supported by the substantial equivalent information and data provided in this Premarket Notification."

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

• Sample Size for Test Set: Not applicable. No new clinical or non-clinical tests were performed for this particular 510(k) submission.
• Data Provenance: The document states that KCI conducted a systematic literature review of studies involving the Prevena Incision Management System and is adding a bibliography of published studies to the labeling. This suggests the data provenance is from retrospective, published clinical studies potentially from various countries, but specific details on the countries of origin are not provided in this summary.

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

• Number of Experts: Not applicable. No new test set requiring expert ground truth establishment was created for this submission. The systematic literature review might have reviewed studies where expert consensus was used, but this is not detailed in the provided text.
• Qualifications of Experts: Not applicable.

4. Adjudication method for the test set

• Adjudication Method: Not applicable. No new test set required adjudication for this submission.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not performed. The device in question is a medical device (Negative Pressure Wound Therapy System), not an AI diagnostic tool that would involve human readers and AI assistance.

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

• Standalone Performance Study: No. This is not an AI algorithm. It's a medical device that provides negative pressure wound therapy.

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

• Type of Ground Truth: For the systematic literature review, the "ground truth" would implicitly refer to the outcomes reported in the published clinical studies. These outcomes would likely include measures relevant to wound healing, infection rates, drainage management, and patient safety, which are typically derived from clinical observation, patient records, and potentially pathology reports depending on the specific study designs. The document states the system "is intended to manage the environment of surgical incisions that continue to drain... by maintaining a closed environment and removing exudate," implying ground truth would relate to these aspects.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This document pertains to a resubmission for a labeling change for an existing medical device, not a new device requiring a training set or an AI algorithm.

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

• Ground Truth for Training Set Establishment: Not applicable.

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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.

Ask a specific question about this device

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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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 PrevenaTM Incision Management System consists of the following components: A single use, sterile dressing that is applied in a simple peel and place process. Negative pressure is provided to the dressing via a negative pressure therapy unit. Wound fluids are collected in a sterile, disposable canister. The PrevenaTM Incision Management System is intended for continuous application of negative pressure wound therapy to the closed surgical incision immediately after surgery.

The provided document is a 510(k) summary for the Prevena™ Incision Management System. It primarily focuses on demonstrating substantial equivalence to a predicate device (ActiV.A.C.® Therapy System) and includes various tests to assure conformance to design specifications.

However, the document does not describe acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy) for a device as one would typically expect for an AI/algorithm-driven device's clinical performance. Instead, the acceptance criteria are implicitly tied to the successful completion of various engineering, biocompatibility, and usability tests, as well as demonstrating equivalence in delivering negative pressure wound therapy.

Therefore, the requested tables and details related to performance metrics, sample sizes, ground truth establishment, expert adjudication, MRMC studies, or standalone algorithm performance are not available and cannot be extracted from the provided text. The device is a physical system that delivers negative pressure wound therapy, not an algorithm that makes diagnostic or prognostic predictions requiring such clinical performance criteria.

Here's a breakdown of what can be extracted, reinterpreting "acceptance criteria" as the criteria for demonstrating substantial equivalence and product safety/efficacy based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

As noted, the document doesn't present acceptance criteria in terms of clinical performance metrics like sensitivity or specificity. Instead, the "acceptance criteria" are implied by the successful completion of various tests designed to demonstrate substantial equivalence and functional safety. The "reported device performance" refers to the outcomes of these tests.

• Escherichia coli: Day 1=2.2, Day 3=4.0, Day 5=3.9, Day 7=4.5
• Pseudomonas aeruginosa: Day 1=2.0, Day 3=3.9, Day 5=3.5, Day 7=3.7
• Staphylococcus aureus: Day 1=1.6, Day 3=3.6, Day 5=3.6, Day 7=3.5
• Klebsiella pneumonia: Day 1=1.4, Day 3=1.8, Day 5=2.7, Day 7=3.5
• Candida albicans: Day 1=2.5, Day 3=3.1, Day 5=3.2, Day 7=3.2
• Aspergillus niger: Day 1=2.2, Day 3=4.1, Day 5=4.0, Day 7=3.6 |
  | Biocompatibility: Meet ISO 10993-1 standards (cytotoxicity, irritation, sensitization). | Cytotoxicity, irritation, and sensitization testing performed in accordance with ISO 10993-1 standards demonstrated the device is biocompatible. |
  | Usability: System can be used by lay users and healthcare professionals (understand labeling, respond to alarms). | A validation of the usability was assessed by a simulated patient group (without healthcare experience) and a group of operating room nurses. Both studies confirmed the ability of participants to use the therapy system, understand labeling, and respond to alarms. |

Regarding the other requested information (2-9):

The provided document describes a medical device, specifically a negative pressure wound therapy system, not an AI/algorithm-driven diagnostic or assistive tool. Therefore, the concepts of "test set," "training set," "data provenance," "number of experts for ground truth," "adjudication method," "MRMC study," and "standalone algorithm performance" as typically applied to AI systems are not applicable to this device and are not mentioned in the document.

The "studies" described are design verification and validation tests for the physical device, its components, and its functional properties, rather than studies assessing the performance of an algorithm against a ground truth dataset.

Specifically:

2. Sample size used for the test set and the data provenance: Not applicable. The "tests" are engineering and biological evaluations, not clinical performance studies on a "test set" of patients or data in the context of AI.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for clinical performance is not established as this is not an interpretive AI device. Experts were involved in the usability studies (operating room nurses, simulated patient group), but not for establishing a "ground truth" for a dataset.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable.
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 device is a therapy system, not an AI assistance tool for human readers.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not applicable. No algorithm is being assessed for standalone performance.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable in the context of an AI device. The "ground truth" for the tests performed relates to physical properties (e.g., presence/absence of viral penetration, wicking capability, microbial count reduction, electrical safety standards compliance).
8. The sample size for the training set: Not applicable. No training set for an AI/algorithm.
9. How the ground truth for the training set was established: Not applicable.

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