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Belzer MPS® (UW Machine Perfusion Solution) (BMPS-001) is indicated for the in vitro flushing and continuous hypothermic machine perfusion of explanted abdominal organs.

Belzer MPS® (UW Machine Perfusion Solution) (BMPS-001) is a clear to straw-colored, sterile, non-pyrogenic solution for the in-vitro flushing and continuous perfusion of explanted abdominal organs. The Belzer MPS® (UW Machine Perfusion Solution) (BMPS-001) has the same composition and indication for use as MaPerSol® Organ Preservation Solution.

The solution is consistent with an extracellular solution, based on its sodium/potassium ratio and has a calculated potassium concentration of 25 mEq/L, a sodium concentration of 100 mEq/L, an osmolarity of 300 mosmol/kg, and a pH of approximately 7.4 at 20°C.

The Belzer MPS® (UW Machine Perfusion Solution) (BMPS-001) is supplied in single use 1000mL solution bags, made from flexible PVC free material (e.g., laminated EVA film) with at least two integrated ports including ports for delivery and component addition. Each individual bag is enclosed in a protective outer overwrap bag. The solution is sterile and is intended for one single use.

The provided text describes a 510(k) premarket notification for a medical device called Belzer MPS® (UW Machine Perfusion Solution) (BMPS-001). This document focuses on demonstrating substantial equivalence to a previously cleared predicate device (MaPerSol® Solution, K080432), rather than providing detailed acceptance criteria and performance study data typically associated with de novo clearances or PMAs for novel devices or software.

Therefore, much of the requested information regarding "acceptance criteria" and "the study that proves the device meets the acceptance criteria" in terms of algorithm performance for an AI/ML device is not applicable in this specific document. The "acceptance criteria" in this context are for demonstrating substantial equivalence of a solution composition and function, not algorithmic performance.

Here's what can be extracted and what cannot:

1. Table of acceptance criteria and reported device performance:

• Acceptance Criteria (for Substantial Equivalence to Predicate): The core acceptance criterion is that Belzer MPS® (BMPS-001) is "substantially equivalent in composition, safety and efficacy" to the predicate device, MaPerSol® Solution. This is primarily demonstrated by:
  • Same Indications for Use: Flushing and continuous hypothermic machine perfusion of explanted abdominal organs.
  • Same Composition: The exact chemical composition (components and g/L) is identical to the predicate.
  • Same Technological Characteristics: pH, Osmolality, Product State (liquid), Single Use, Storage Temperature, Pre-cooling requirements, Internal cooling mechanism, Product Bag materials, Pyrogenicity, Sterility methods, Biocompatibility (as per ISO standards), Fluid Volume.
• Reported Device Performance: The document states that the device is "safe and effective as predicate and reference devices" and that "The non-clinical data supports and demonstrates the safety of the device." No specific quantitative performance metrics (e.g., organ viability rates, post-transplant outcomes) are provided for the Belzer MPS® itself, as its equivalence is established based on its identical composition and intended use to a proven predicate. The "performance" is implicitly deemed equivalent to that of the predicate.

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

• This document does not describe a clinical study in the form typically seen for AI device validation with test sets of patient data. The "test set" here refers to the parameters/characteristics of the solution itself and its manufacturing process, not patient data.
• Data Provenance: Not applicable in the context of patient data. The data provenance relates to the chemical composition, physical properties, manufacturing processes, sterilization, and biocompatibility studies conducted on the solution and its packaging materials. These are typically generated through laboratory testing of the product itself.

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

• Not applicable. This is not an AI/ML device that generates diagnoses or interpretations requiring expert ground truth in the radiological sense. The "ground truth" for this device relates to its chemical and physical properties meeting specified standards, and its safety/efficacy being equivalent to a known predicate. This is established through standard laboratory testing, chemical analysis, and biocompatibility assessments by qualified personnel in those fields, not expert radiologists creating consensus labels.

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

• Not applicable. This methodology is for clinical image interpretation or similar tasks requiring human consensus or adjudication, which is not relevant to a medical solution like Belzer MPS®.

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-assisted interpretation device. No MRMC study was conducted or is applicable.

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

• Not applicable. This is not an algorithm. Therefore, no standalone performance study was done.

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

• The "ground truth" for this device is based on analytical chemistry, physical property measurements, biocompatibility testing (in vitro and in vivo animal models often), and sterility testing to confirm the product's specifications and safety. The ultimate "ground truth" for "substantial equivalence" rests on the established safety and efficacy of the predicate device, MaPerSol® Solution, which presumably underwent its own validation studies years prior (K080432).

8. The sample size for the training set:

• Not applicable. This is a chemical solution, not an AI/ML model that requires training data.

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

• Not applicable. As above, no training set or ground truth in the AI/ML context.

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