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Indicated for use when low plasticizer solubility is desired (e.g., for administration of high lipid content solutions such as parenteral fat emulsion).

The Non-DEHP IV Fat Emulsion Administration Sets product line consists of single use disposable devices intended for the administration of fluids from a container into the patient's vascular system through a needle or catheter inserted into a vein. The Non-DEHP IV Fat Emulsion Administration Sets are indicated for use when low plasticizer solubility is desired (e.g., for administration of high lipid content solutions such as parenteral fat emulsion). These sets are comprised of a drip chamber with a non-vented spike, non-DEHP polyvinyl chloride (PVC) tubing, and a two piece Luer lock. On all sets there is a fixture slide clamp and a regulating roller clamp. Configurations of these sets differ in drip chamber (60 drops/mL or 10 drops/mL). These devices are nonpyrogenic, sterile and can be used for gravity or pump infusion of I.V. fluids using Sigma Spectrum and other Baxter Infusion Pumps.

This document describes the non-clinical performance testing of the "Non-DEHP IV Fat Emulsion Administration Sets" (K172544) by Baxter Healthcare Corporation.

1. Acceptance Criteria and Reported Device Performance

The provided document lists various performance tests undertaken for the device. The reported performance for all tests is that they "All tests met the acceptance criteria." The specific acceptance criteria are broadly defined as "Per Baxter Test Method" for each test.

Here's a table summarizing the tests and the reported performance:

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

The document states that testing was performed on "subject device model code set configurations 2R1145 and 2R1146" or on "a worst case/representative sample."

• Sample Size: The exact number of units or samples used for each test is not explicitly stated.
• Data Provenance: The tests are described as bench tests conducted by Baxter Healthcare Corporation. The document does not specify the country of origin of the data or whether it was retrospective or prospective, but it implies prospective testing for the purpose of the 510(k) submission.

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

This section is not applicable as the evaluation involves non-clinical bench testing of physical device properties and performance, not a diagnostic or clinical assessment requiring expert-established ground truth.

4. Adjudication Method for the Test Set

This section is not applicable for non-clinical bench testing. The evaluation relies on specific test methods and their quantitative or qualitative outcomes.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done. This type of study is typically performed for diagnostic imaging devices or software where human readers interpret medical images, often with and without AI assistance. The submitted device is an administration set, which does not involve human image interpretation in that context.

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

This section is not applicable. The device is an intravenous administration set, not an algorithm. The performance testing is for the physical device itself.

7. The Type of Ground Truth Used

For the non-clinical performance tests, the "ground truth" is established by defined engineering specifications, industry standards, and internal Baxter test methods. These methods determine the acceptable range or outcome for each performance characteristic (e.g., tensile strength, leak integrity, flow rate, biocompatibility).

8. The Sample Size for the Training Set

This section is not applicable as the device is a physical medical device, not a machine learning algorithm that requires a training set.

9. How the Ground Truth for the Training Set was Established

This section is not applicable as the device is a physical medical device, not a machine learning algorithm.

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