Search Results

Intended for gastric decompression and delivery of fluids, including irrigation, nutritional supplements, and medication during the time period that gastric decompression is required. The device is intended for patients with age of two-year and older.

The product is a double lumen sump tube available in 5 sizes, 10Fr – 18Fr, and lengths of 36 – 48 inches. The device is equipped with a Multi-functional port to allow alternating use for gastric decompression (via a suction connector) or delivery of fluids, including irrigation, nutritional supplements, and medication via a syringe or feeding set equipped with a female ENFit connector.

The provided text is a 510(k) Summary for a medical device (Salem Sump™ Dual Lumen Stomach Tube with Multi-functional Port). It focuses on demonstrating substantial equivalence to a predicate device, primarily through non-clinical testing. This type of document typically does not include the detailed information requested in the prompt regarding acceptance criteria, study methodologies for performance evaluation, or clinical effectiveness studies in the way an AI/ML device submission would.

Therefore, for almost all categories, the answer will be that the information is not provided in this document.

However, I can extract what is present related to non-clinical testing and general acceptance.

Here's a breakdown based on the provided text:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly present a table of acceptance criteria with corresponding reported performance for a specific clinical or diagnostic metric in the way an AI/ML device would. Instead, it describes various non-clinical tests performed to ensure the device maintains safety and effectiveness equivalent to the predicate device, especially with the addition of the new ENFit connector.

Non-Clinical Tests Mentioned for the ENFit Connector (Implied Acceptance):

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

• Sample Size for Test Set: Not specified. The document refers to non-clinical tests on the device components, not a test set of patient data.
• Data Provenance: Not applicable, as no patient data or clinical study data is referenced for evaluation. The tests are non-clinical, likely conducted in a lab environment.

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

Not applicable. The ground truth, in this context, would be established by engineering specifications and standards for device performance, not expert review of medical images or patient outcomes.

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

Not applicable, as no expert adjudication of a test set is mentioned.

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/ML device, and no MRMC study was conducted.

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

Not applicable. This is not an AI/ML device.

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

For the non-clinical tests, the "ground truth" is defined by the established engineering standards, regulatory requirements (e.g., ISO 10993-1:2009, AAMI/CN3:2014 (PS) Part 3), and internal design specifications for device performance and safety (e.g., for fluid leakage, tensile strength, biocompatibility).

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so there is no training set in that context.

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

Not applicable. As there is no training set for an AI/ML algorithm, this question doesn't apply.

Ask a specific question about this device