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1. Acute Hepatic Encephalopathy: The BioLogic-DT System is indicated for the treatment of acute hepatic encephalopathy due to decompensation of chronic liver disease or fulminant hepatic failure.
2. Drug Overdose and Poisonings: The BioLogic-DT System is indicated for the treatment of drug overdose and poisonings. The only requirement is that the drug or chemical be dialyzable (in unbound form) and bound by charcoal, such as acetaminophen, tricyclics, barbiturates, tranquilizers, anticancer agents, antimicrobials, theophylline, herbicides, and insecticides.

The BioLogic-DT System is a sorbent regenerated detoxification system consisting of the BioLogic-DT Machine and the single use BioLogic-DT-1000-TK (Treatment Kit). In many ways, it is similar to a standard hemodialysis machine in that blood is removed from the body, passed through a cellulosic dialyzer, and returned to the body. Within the dialyzer, diffusion causes many chemicals and toxins to pass from the dialysate surrounding the membranes. Depending on the binding characteristics of the sorbents in suspension in the dialysate, some chemicals remain at low concentration in the dialysate, and are therefore efficiently removed from the blood, while others reach concentrations similar to blood, and are therefore not removed from the blood. Like existing single-access dialysis systems, the BioLogic-DT System alternately withdraws and returns blood through a single-lumen catheter. Unlike standard dialysis machines, which use roller pumps to pass blood through the membranes, the DT applies an alternating pressure/ vacuum cycle to the sorbent suspension causing the alternating expansion and compression of the dialyzer's parallel plate cellulosic membranes. This expansion and compression of the membranes is used to pump blood through the system.

An improvement in software of the DT-1000 System mixes the sorbents before prime, obviating the need for the operator to shake the bag.

This document (K984546) describes a 510(k) submission for the BioLogic-DT System, an improved version of a sorbent-based blood treatment system. The improvements primarily concern a software change to automatically mix the sorbent suspension during device setup. The study focuses on this specific change and its impact on the device's performance.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 7 repetitions of the DT System setup with automatic mixing.
• Data Provenance: Retrospective, internally generated by HemoCleanse Inc. The study described is non-clinical performance data.

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

• Not applicable for this type of non-clinical, performance-based study. The ground truth (clogging or free flow) was an observable physical outcome.

4. Adjudication Method for the Test Set

• Not applicable. The outcome was a direct observation of the device's physical performance (clogging vs. free flow).

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

• No, a MRMC comparative effectiveness study was not done. The study was a non-clinical evaluation of a device function.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) was Done

• Yes, this was a standalone performance evaluation of the device's new automatic mixing function. The "algorithm" here refers to the software change that automated the mixing process. The study directly assessed the device's ability to mix the sorbent and prevent clogging without human intervention during the mixing step.

7. The Type of Ground Truth Used

• Direct Observational Outcome: The ground truth was the direct observation of "sorbent flow freely through the dialyzer" or "sorbent clogging."

8. The Sample Size for the Training Set

• The document does not explicitly mention a training set. The "software change" was implemented, and then its performance was tested with 7 repetitions. It's implied that any development and internal testing prior to this documented study would have involved some form of iterative development, but a formal "training set" in the machine learning sense is not described.

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

• As a formal "training set" is not explicitly mentioned, there's no information on how its ground truth would have been established. The development of the software change likely involved engineering and design principles, with internal validation steps rather than a distinct "training set" with ground truth in the context of supervised learning. The previous method's 1 out of 4 (25%) clogging rate served as a baseline or justification for the need for improvement.

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