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Hepatostat Set is an absorbable compression system acting as a tourniquet on the hepatic, splenic and nephric tissue. It is used to achieve hemostasis in hepatic, splenic, and nephric resections. It can also be used for traumatic liver, kidney and spleen injuries. It is designed to provide compressive hemostasis to the wound site, and resorb naturally over time after hemostasis has been achieved.

The Hepatostat Set® is an absorbable compression device which allows to perform any type of hepatic resection, large or small, without any significant bleeding. It is made of four pre-perforated absorbable strips. These strips are connected together by twenty Safil® polyfilament ligatures introduced through the liver by a special tubular needle holder. Each suture is terminated by a stainless steel dart and already inserted into a stainless steel tubular needle holder, fitted to the liver thickness. Two clamps enable to bring together two strips.

The provided text describes a 510(k) premarket notification for the "Hepatostat" device, which is an absorbable compression system. The submission expands the intended use of a previously cleared device (K061796).

Here's an analysis of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not provide specific quantitative acceptance criteria (e.g., success rates, hemostasis time limits) or detailed reported device performance metrics from a clinical study in a table format.

The closest statement to device performance is:

• "Performance testing demonstrates that the Hepatostat is substantially equivalent to the predicate devices, and safe and effective to use, when used in accordance with the supplied instructions for use."

This is a general statement of equivalence and safety/efficacy rather than specific metrics against defined criteria.

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size for Test Set: Not specified. The document only mentions "Animal testing has been conducted to demonstrate the safety and efficacy for use in splenic and nephric resection." It does not provide the number of animals or specific cases.
• Data Provenance: Animal testing. The country of origin is not specified, but the applicant is Vygon Corporation in the USA. The testing would be considered prospective as it was conducted to support the expanded indication.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

• Not applicable. The ground truth was established through animal testing, which would involve direct observation of hemostasis and tissue response, likely by veterinary surgeons or animal researchers, rather than human expert interpretation of images or clinical outcomes in humans.

4. Adjudication Method for the Test Set:

• Not applicable. As the ground truth was established through direct observation in animal studies, an adjudication method for human expert review is not relevant.

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, an MRMC comparative effectiveness study was not done. The device described is a physical surgical device, not an AI or imaging diagnostic tool. Therefore, questions regarding human reader improvement with AI assistance are not relevant to this submission.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

• No, a standalone algorithm performance study was not done. As stated above, this is a physical surgical device, not an algorithm.

7. The Type of Ground Truth Used:

• The ground truth used was direct observation of hemostasis and tissue response in animal studies. This is a form of outcomes data in an animal model.

8. The Sample Size for the Training Set:

• Not applicable. The document describes animal testing done for performance demonstration to support regulatory submission, not machine learning model training.

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

• Not applicable, as no machine learning model training is described.

Summary of the Study that Proves the Device Meets Acceptance Criteria:

The study proving the device meets the acceptance criteria, as described in the document, is animal testing.

• Purpose: To demonstrate the safety and efficacy of the Hepatostat for use in splenic and nephric resection.
• Methodology (implied): Direct surgical application of the Hepatostat device in animals undergoing splenic and nephric resections. Observation of hemostasis achievement and potentially post-operative recovery and tissue resorption.
• Results (stated generally): "Performance testing demonstrates that the Hepatostat is substantially equivalent to the predicate devices, and safe and effective to use..."

The document suggests that the acceptance criterion for this animal testing was simply to demonstrate safety and efficacy and substantial equivalence to the previously cleared Hepatostat (K061796) for the expanded indications. Specific quantitative acceptance criteria or detailed results of the animal study are not provided in this 510(k) summary.

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Hepatostat Set is a compression system acting as a tourniquet on the hepatic tissue. It is intended to reduce the risk of bleeding and to achieve hemostasis in hepatic resections (from large to small superficial hepatectomy). It can also be used for traumatic liver injuries.

The Hepatostat Set is an absorbable compression device which was developed for the purpose of hepatic resection, large or small, without any significant bleeding. It consists of four pre-perforated absorbable strips which are sutured together with Safil polyfilament ligatures introduced through the liver with a tubular needle.

This document describes a 510(k) premarket notification for the "Hepatostat Set," an absorbable compression device intended for hepatic resection and traumatic liver injuries to reduce bleeding and achieve hemostasis. The submission references Biocompatibility and implant testing, performance testing, and risk assessment as the studies demonstrating safety and effectiveness.

However, the provided text does not contain the detailed acceptance criteria or numerical performance data typically found in a clinical study report for AI/ML devices. It focuses on the regulatory submission process for a physical medical device. Therefore, I will create a table outlining the stated acceptance criteria (which are more general compliance requirements for this type of device) and the reported device performance based on the information provided.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a submission for a traditional medical device (not an AI/ML device), the "acceptance criteria" are framed as regulatory and performance standards, and "device performance" refers to its ability to meet those standards as demonstrated by various tests.

Since the provided document is a 510(k) summary for a physical medical device and NOT an AI/ML device, the following points address what would typically be included for an AI/ML device but are not applicable or present in this specific document.

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

• Not applicable for this device. This information is typically for AI/ML performance evaluation using real-world data. For this physical device, "test set" would refer to samples used in laboratory and animal in vivo studies, but specific numbers and provenance of "data" in the context of AI are not present.

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

• Not applicable for this device. Ground truth for AI/ML models is often established by human experts labeling data. For this physical device, "ground truth" would relate to biological outcomes (e.g., hemostasis, tissue reaction) observed in pre-clinical studies, assessed by pathologists or researchers, not a consensus of experts on a dataset.

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

• Not applicable for this device. This term refers to how disagreements among annotators are resolved in AI/ML dataset creation.

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 for this device. MRMC studies are specific to evaluating AI/ML systems that assist human operators in tasks like image interpretation.

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

• Not applicable for this device. This refers to the performance of an AI/ML algorithm independently.

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

• For the biological studies mentioned (biocompatibility, implantation, hemostasis, absorption), the ground truth would be established through:
  • Histopathology: for tissue reactions, inflammation, and degradation in implantation studies.
  • Chemical analysis: for degradation products and absorption rates.
  • Direct observation/measurement: of hemostasis efficacy in in vivo models.
• The document doesn't explicitly detail the methodology of "ground truth" for these pre-clinical studies but implies standard methods for material and medical device testing.

8. The sample size for the training set:

• Not applicable for this device. This information is for AI/ML model development.

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

• Not applicable for this device. This information is for AI/ML model development.

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