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The Gambro Cartridge® Blood Set Low Weight-Low Volume is intended for single use in a hemodialysis treatment using the Phoenix® Dialysis Delivery System.

The Low-Weight - Low Volume model is used when a low extra-corporeal blood volume is recommended. The Low Weight - Low Volume model with a priming volume of 40 ml is indicated for patients with a body weight greater than 15 kg and lower or equal to 20 Kg.

The Gambro Cartridge® Blood Set Low Weight – Low Volume is single use sterile tubing set employed in the Gambro hemodialysis equipments extracorporeal circulation. It conveys the patient's blood from the arterial-venous access fistula to the dialyzing filter (arterial line) and back after purification (venous line) and it is commonly referred to as bloodline. A Gambro Cartridge® Blood Set Low Weight - Low Volume can be safely connected to hemodialyzers, vascular accesses and various perfusion lines, under the responsibility of the physician in charge.

The provided document describes a 510(k) submission for the Gambro Cartridge® Blood Set Low Weight - Low Volume. This submission focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance data, rather than clinical performance data or AI/ML model performance.

Therefore, many of the requested criteria related to AI/ML device testing (such as ground truth establishment, expert adjudication, MRMC studies, training set details) are not applicable to this submission.

Here's the information that can be extracted or deduced from the provided text:

Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative "acceptance criteria" in the format of a table with specific numerical thresholds for each test. Instead, it describes a series of non-clinical performance tests designed to establish the safety and effectiveness of the device as performing "as well as or better than the legally marketed predicate device." The "acceptance" for these tests would be successful completion, indicating the device performs as intended and is comparable to the predicate.

Study Information

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

   • The document describes non-clinical performance testing (bench testing). It does not refer to "test sets" in the context of patient data for a clinical or AI/ML study.
   • No information about specific sample sizes (e.g., number of devices tested for each bench test) or data provenance (country of origin, retrospective/prospective) is provided.

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

   • Not applicable as this is a non-clinical bench testing study, not a study requiring expert ground truth for clinical outcomes or diagnostic accuracy.

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

   • Not applicable for a non-clinical bench testing study. The results of the bench tests (e.g., pressure leak, flow rate, tensile strength) would be objectively measured against engineering specifications, not adjudicated by experts.

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:

   • No MRMC study was done. This document describes a traditional medical device (blood set) and its non-clinical testing, not an AI-powered device.

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

   • Not applicable. This is not an AI/ML algorithm.

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

   • For the non-clinical tests, the "ground truth" would be established by engineering specifications, material properties, and accepted industry standards for medical device performance and safety (e.g., ISO standards for blood contacting devices, internal Gambro specifications).

7. The sample size for the training set:

   • Not applicable. There is no AI/ML model for which a training set would be required.

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

   • Not applicable as there is no AI/ML model or training set.

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