Search Results

• Indicated for treatment of the chronic or acute uremic patient where hemodialysis, including high flux dialysis, is prescribed by the physician.
• Indicated for hemodialysis in a variety of environments, to include acute care facility, chronic dialysis facility, self care facility, or home setting, where the patient has been trained and certified to be competent in the use of this device by the attending physician.

The PHD Personal Hemodialysis System is an automated high flux hemodialysis system using a double needle configuration. It is designed to perform safe and effective personal hemodialysis as prescribed by the patient's physician, while minimizing the time and effort to perform each treatment. A computer-like touch screen prompts the operator through each step of the procedure. The PHD System consists of several components that combine to deliver hemodialysis and perform ultrafiltration. These include the PHD Instrument (PHDi), the PHD Water Pre-Treatment System (WPS), the PHD Blood Tubing Set (BTS), the PHD Chemical Concentrate Bottles (CCBs), and the PHD Transducer Protector (TP). The PHD Instrument orchestrates and performs the entire pre-treatment, treatment, and post treatment cycle. It accomplishes this by working in conjunction with the WPS (or equivalent), BTS, CCBs, TP, and a commercially available hollow fiber membrane dialyzer. Between treatments, the system automatically cleans and disinfects the blood tubing set, dialyzer, and all other components in the fluid pathway. Also, the machine checks the efficiency and integrity of the dialyzer. The PHD Instrument electronically stores information about the patient, prescription, treatment schedule, and machine set-up while allowing for updates according to individual needs.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Aksys PHD® Personal Hemodialysis System:

The document is a 510(k) summary for the Aksys PHD Personal Hemodialysis System. It focuses on demonstrating substantial equivalence to predicate devices rather than establishing novel safety and effectiveness through extensive clinical trials with predefined, quantitative acceptance criteria for device performance.

Therefore, many of the specific details requested in your prompt (like MRMC studies, sample sizes for training/test sets, expert qualifications for ground truth) are not present in this type of regulatory submission. This document highlights a predicate pathway, where the new device is compared to existing, legally marketed devices.

Let's break down what information is available:

1. Table of Acceptance Criteria and Reported Device Performance

Instead of clearly defined acceptance criteria with specific thresholds, the document states general equivalency.

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

• Sample Size for Test Set: Twenty-three (23) patients
• Data Provenance: The document does not specify the country of origin. It indicates data was collected from "3 sites." It is a prospective study as it's described as a "clinical study" designed to "establish the substantial equivalence."

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

This information is not provided in the 510(k) summary. Given the nature of hemodialysis output (Kt/V, ultrafiltration measurements), the "truth" is likely derived from the machine's own measurements or standard clinical assessments rather than expert consensus on images.

4. Adjudication Method for the Test Set

This information is not provided. For parameters like Kt/V and ultrafiltration, it's unlikely that a traditional expert adjudication method (e.g., 2+1) would be used. The "truth" would be objective measurements. Adverse events would be recorded and reviewed by the clinical study team and potentially a data safety monitoring board, but not "adjudication" in the sense of resolving conflicting interpretations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No, an MRMC study was not done. MRMC studies are typically for diagnostic imaging devices where human readers interpret images. This device is a treatment system, and the study focused on clinical outcomes and machine performance. Therefore, there's no "effect size of how much human readers improve with AI vs without AI assistance" to report. The PHD System is a standalone treatment device, not an AI-assisted diagnostic tool.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself operates in an "algorithm only" mode for delivering treatment parameters once programmed by a clinician and operated by a trained user. The clinical study assessed the performance of the entire system (device + intended use) against conventional methods. It's not a diagnostic algorithm that would typically have a "standalone" performance evaluation separated from human interpretation. The system performs its functions autonomously once set up.

7. The Type of Ground Truth Used

The ground truth implicitly used for the clinical study's parameters (Kt/V accuracy, ultrafiltration accuracy, adverse events) would be:

• Kt/V and Ultrafiltration: Clinical measurements and calculations performed by the device and/or standard clinical protocols, compared against expected or benchmark values from conventional systems.
• Adverse Events: Documented clinical observations and patient outcomes.

8. The Sample Size for the Training Set

This information is not provided. The PHD System is a medical device, not a machine learning algorithm in the modern sense that requires a "training set" of data to learn from. Its operational parameters are based on engineering design, physics, and medical principles, not on iterative learning from a dataset.

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

Since there is no "training set" in the context of a machine learning algorithm, this question is not applicable. The device's functionality is designed and validated through engineering principles and testing, not by training on patient data.

In summary: The provided 510(k) summary describes a traditional medical device submission for a hemodialysis system, demonstrating substantial equivalence to existing predicate devices. It does not contain information about AI/ML algorithm development, reader studies, or detailed performance metrics and ground truth establishment typically found in submissions for AI-powered diagnostic tools. The clinical study was primarily designed to show that the new device performs comparably to established hemodialysis equipment in terms of safety and efficacy parameters relevant to hemodialysis treatment.

Ask a specific question about this device