Search Results

Revaclear hemodialyzers/diafilters are intended to purify blood in hemodialysis and hemodiafiltration. Revaclear devices are indicated for the treatment of chronic or acute renal failure.

The Revaclear hemodialyzers/diafilters are intended to purify blood in hemodialysis and hemodiafiltration. Revaclear devices are for single use, steam sterilized with sterile and non-pyrogenic fluid pathways. Revaclear devices are part of an extracorporeal system for dialysis treatments to be used under care of trained professionals of dialysis centers or hospitals. The Revaclear hemodialyzer family uses the hollow fiber dialyzer technology. Blood enters a blood inlet port, where it is distributed into membrane hollow fibers. At either end of the device, the membrane hollow fibers are potted in polyurethane to isolate the blood compartment from the dialysate compartment. By means of hydrostatic pressure, or transmembrane pressure (which is created by a combination of positive and negative pressures across the membrane), water along with certain low molecular weight solutes of the plasma pass through the membrane to the filtrate or dialysate compartment of the device. Toxins and waste products are removed from the patient's blood by means of diffusion and convection; they are eliminated via the dialysate/filtrate and the membrane during the treatment session. The dialysate exits the device via a dialysate/filtrate outlet port.

The provided text describes a 510(k) premarket notification for a medical device, the Revaclear hemodialyzer. The information is largely focused on demonstrating substantial equivalence to a predicate device based on technological characteristics and performance testing.

However, the document does not contain the kind of detailed information about acceptance criteria and study designs that would typically be found for an AI-based medical device. Specifically, it lacks:

• Acceptance criteria for AI performance: There are no metrics like sensitivity, specificity, AUC, or F1-score listed with numerical targets.
• AI study design details: No mention of training sets, test sets, data provenance for AI (country, retrospective/prospective), number of experts for ground truth, adjudication methods, MRMC studies, standalone AI performance, or how ground truth was established for AI models.

The "Performance Data" section in the document refers to performance characteristics of the hemodialyzer itself, such as clearance rates and flow resistance, which are assessed according to ISO 8637-1:2017. These are engineering and biological performance metrics for a physical device, not for an AI algorithm.

Therefore, based solely on the provided text, I cannot describe the acceptance criteria and the study that proves an AI device meets those criteria, as the document pertains to a traditional medical device (hemodialyzer) and not an AI/ML product.

If we assume this document were for an AI device and hypothetically fill in the blanks based on common FDA requirements for AI/ML medical devices, it would look something like this:

Hypothetical Acceptance Criteria and Study for an AI-Based Device (Not based on the provided text)

This section outlines the hypothetical acceptance criteria and a study design for an AI/ML-based medical device, as the provided document pertains to a physical hemodialysis device, not an AI product.

1. Table of Acceptance Criteria and Reported Device Performance (Hypothetical for an AI Device)

Let's assume the AI device assists in detecting a specific medical condition from images.

Note: These are hypothetical criteria and performance values for an AI diagnostic device, as the provided text describes a physical hemodialyzer.

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

• Sample Size: 500 unique patient studies (e.g., medical images, lab results).
• Data Provenance:
  • Country of Origin: Data sourced from multiple diverse institutions across the United States, United Kingdom, and Germany to ensure generalizability.
  • Retrospective/Prospective: Primarily retrospective data collected over the past 5 years. A smaller prospective cohort of 50 cases was also included to validate acute performance.

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

• Number of Experts: Three board-certified clinical experts.
• Qualifications:
  • Expert 1: Radiologist with 15 years of experience in the specific disease area.
  • Expert 2: Pathologist with 12 years of experience and specialized knowledge in the disease's histopathology.
  • Expert 3: Clinician (e.g., Oncologist or Cardiologist, depending on the disease) with 10 years of experience in patient management for the condition.

4. Adjudication Method for the Test Set (Hypothetical)

• Method: "2+1" Adjudication.
  • Initial review by two independent experts.
  • If the two experts agreed on the ground truth, that label was accepted.
  • If the two experts disagreed, a third, senior expert (the "1" in 2+1) reviewed the case and made the final decision. Cases where even the third expert could not definitively establish ground truth were excluded from the primary test set.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study (Hypothetical)

• Was it done? Yes, an MRMC study was conducted to evaluate the impact of AI assistance on human reader performance.
• Effect Size of Improvement:
  • Without AI Assistance (Human-Only Baseline): Average AUC = 0.75
  • With AI Assistance (Human + AI): Average AUC = 0.85
  • Effect Size: Human readers demonstrated a statistically significant improvement of 0.10 AUC points when assisted by the AI device compared to unassisted reading. This translates to a 13.3% relative improvement in diagnostic accuracy.

6. Standalone (Algorithm Only) Performance (Hypothetical)

• Was it done? Yes.
• Standalone Performance: The algorithm-only performance on the test set showed:
  • AUC: 0.90
  • Sensitivity: 86%
  • Specificity: 77%

7. Type of Ground Truth Used (Hypothetical)

• Type of Ground Truth: Hybrid approach:
  • Expert Consensus: Primary ground truth established by expert consensus (as per point 4).
  • Pathology: Confirmed by histopathological diagnosis for all cases where tissue biopsy was available (considered the gold standard for many conditions).
  • Outcomes Data: Long-term patient follow-up and clinical outcomes data (e.g., disease progression, response to treatment) were used to corroborate uncertain cases where pathology was not available.

8. Sample Size for the Training Set (Hypothetical)

• Sample Size: 10,000 unique patient studies.
  • This included a mix of positive and negative cases, as well as various disease severities and typical confounders.

9. How Ground Truth for the Training Set Was Established (Hypothetical)

• Ground Truth Establishment:
  • For the initial large training set, a combination of clinical reports, electronic health records (EHR), and a larger set of less stringent expert review (e.g., single expert review or review by clinical residents overseen by an attending) was used.
  • A subset of the training data (e.g., 20%) was fully annotated and reviewed by multiple experts, similar to the test set ground truth establishment process, to ensure high-quality labels for critical boundary cases and rare examples.
  • Automated extraction of positive findings from structured diagnostic reports was also utilized where reliability was high, followed by manual validation of a representative sample.

Crucially, none of the above hypothetical information regarding AI acceptance criteria and study details is present in the provided text. The provided text is solely about the performance and safety of a physical hemodialyzer.

Ask a specific question about this device