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The extriCARE® 3600 Negative Pressure Wound Therapy System is indicated for wound management via the application of negative pressure to the wound by the removal of wound exudate, infectious materials, and tissue debris from the wound bed. The extriCARE® 3600 Negative Pressure Wound Therapy System is indicated for the following wound types: chronic, acute, traumatic, subacute and dehisced wounds, partial-thickness burns, ulcers (such as diabetic or pressure), flaps and grafis.

The system consists of a vacuum pump, canister, tubing, NPWT bandage or NPWT Foam Kits. In operation, the device is attached via the tubing to a Negative Pressure Wound bandage or foam kits. Maximum pressure and continuous mode or intermittent mode selection are digitally programmable. The extriCARE 3600 pump can be sold alone or as a part of the extriCARE 3600 system with extriCARE bandages or foam kits. The extriCARE bandages are an all-in-one wound dressing; with no wound packing required which is cleared in the predicate K110078. The foam kit consists of Polyurethane foam, transparent film drape, paper ruler, Suction Bell with connecting (drainage) tube and clamp, which is included in this submission for review.

The provided 510(k) summary describes a Negative Pressure Wound Therapy (NPWT) system, which is a medical device and not an AI/ML algorithm. Therefore, many of the requested categories related to AI/ML device performance (e.g., sample size for training, expert ground truth, MRMC study, AI improvement effect size) are not applicable to this document.

However, I can extract the information provided regarding the device's acceptance criteria and the studies conducted to demonstrate its performance as a medical device.

Acceptance Criteria and Device Performance for extriCARE® 3600 Negative Pressure Wound Therapy System

Since this is a medical device (Negative Pressure Wound Therapy System) and not an AI/ML algorithm, the acceptance criteria are not typically expressed in terms of metrics like sensitivity, specificity, or AUC, but rather in terms of compliance with functional, safety, and performance standards. The "reported device performance" in this context refers to the successful completion of these tests.

Study Details:

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

   • Test Set Sample Size: The document does not specify a numerical sample size for "test sets" in the context of clinical trials or data-driven algorithm validation. Instead, it mentions "representative samples of the device" for functional, mechanical, EMC, and electrical safety testing. For biocompatibility, "bandage samples and foam kits samples" were tested. No specific number of devices or clinical cases is provided.
   • Data Provenance: The document does not provide information on the country of origin of data or whether tests were retrospective or prospective, as these are not relevant for the type of bench and biocompatibility testing described.

2. 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 question is not applicable as the document describes a physical medical device undergoing bench and biocompatibility testing, not an AI/ML algorithm requiring expert-labeled ground truth for a test set.

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

   • This question is not applicable for the same reasons as above. Adjudication methods are relevant for resolving discrepancies in expert-labeled ground truth in AI/ML performance studies.

4. 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:

   • This question is not applicable. An MRMC comparative effectiveness study is used for evaluating the impact of AI on human reader performance, which is not relevant for a physical NPWT system.

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

   • This question is not applicable as the document concerns a physical medical device, not a standalone AI algorithm.

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

   • For functional and safety testing, the "ground truth" is adherence to established engineering specifications, international standards (e.g., IEC 60601 series), and biocompatibility standards (ISO-10993). There isn't a "ground truth" in the sense of clinical diagnoses or outcomes data for this type of device submission.

7. The sample size for the training set:

   • This question is not applicable as this is a physical medical device, not an AI/ML algorithm that requires a training set.

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

   • This question is not applicable for the same reason mentioned above.

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