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510(k) Data Aggregation
(268 days)
Multi-Lumen Acute Hemodialysis Catheter for High Volume Infusions
The device is indicated for use in attaining short-term (less than 30 days) vascular access for hemodialysis, apheresis, rapid fluid administration, intravenous therapy, blood sampling, pressure injection of contrast media, and central venous pressure monitoring. The catheter may also be used in a variety of renal replacement therapies, such as hemofiltration and hemoperfusion. The catheter may be inserted into the jugular, subclavian, or femoral veins. The maximum pressure injection flow rate is 6 mL / sec.
The Multi-Lumen Acute Hemodialysis Catheter for High Volume (hereafter referred to as the 3-L AHDC) is a short-term, single use catheter designed to provide access to the central venous system in a healthcare facility environment. It consists of a non-tapered, radiopaque polyurethane extruded catheter body with a softer, contoured Blue Flex Tip. The catheter is available in a 12 Fr. triple-lumen configuration with usable lengths of 16-25 cm. The catheters can be used for the injection of contrast media. The maximum recommended pressure injection flow rate is 6 mL/sec.
The catheters are packaged sterile with various components to facilitate insertion.
This document is a 510(k) summary for a medical device called the "Multi-Lumen Acute Hemodialysis Catheter for High Volume Infusions," not a study describing AI algorithm. It details the device's indications for use, its substantial equivalence to predicate devices, and non-clinical testing performed.
Therefore, I cannot provide the requested information about acceptance criteria and studies related to an AI device's performance because the provided text is about a physical medical device (a catheter) and does not mention any AI component or algorithm.
Specifically, there is no mention of:
- Acceptance criteria table and reported device performance for an AI algorithm.
- Sample size for a test set, data provenance, ground truth establishment, or expert qualifications for an AI study.
- Adjudication methods for an AI test set.
- Multi-reader multi-case (MRMC) comparative effectiveness studies.
- Standalone performance of an AI algorithm.
- Ground truth types for an AI algorithm.
- Training set sample size or ground truth establishment for an AI algorithm.
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