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The Access sTfR assay is a paramagnetic particle, chemiluminescent immunoassay for the quantitative determination of soluble transferrin receptor (sTfR) levels in human serum and plasma (heparin) using the Access Immunoassay Systems. This assay is intended as an aid in the diagnosis of Iron Deficiency Anemia (IDA), and for the differential diagnosis of IDA and Anemia of Chronic Disease (ACD).

This assay may also be used in conjunction with a ferritin measurement to provide a calculated sTfR/log ferritin index. This index is intended as an aid in the diagnosis of IDA, and for the differential diagnosis of IDA and ACD.

The Access sTfR Calibrators are intended to calibrate the Access sTfR assay for the quantitative determination of soluble transferrin receptor levels in human serum and plasma (heparin) using the Access Immunoassay Systems.

The Access sTfR QC is intended for monitoring system performance of the Access sTfR assay.

The Access sTfR reagent, calibrators, controls, and the Access Immunoassay Analyzers (Access, Access 2, Synchron LXi 725, UniCel DxC 600i, UniCel Dxl 600, and UniCel Dxl 800) comprise the Access Immunoassay Systems for the quantitative determination of soluble transferrin receptor in human serum and plasma.

Automated; Paramagnetic particles coated with mouse monoclonal antibody against sTfR. Uses the same mouse monoclonal antibodies against sTfR in the capture phase and signal phase as the predicate device.

Utilizes dioxetane-based chemiluminescent substrate; measures light production from a chemiluminescent reaction.

Calibrators are comprised of natural sTfR at 6 levels (0, 3, 10, 30, 80, and 150 nmol/L) in a buffered matrix.

QCs are human sTfR provided as a liquid at 3 levels (~10, ~25, ~90 nmol/L) in a buffered matrix.

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1. Table of Acceptance Criteria and Reported Device Performance

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

• Methods Comparison (Analytical Study):

  • Sample Size: 271 samples
  • Data Provenance: Not explicitly stated, but implies clinical samples used for comparing the new assay to the predicate. The "External Site" suggests multiple locations.
  • Retrospective/Prospective: Not specified.

• Clinical Studies (Clinical Trial):

  • Sample Size: Not explicitly stated for the clinical trial itself, but implied to be sufficient to derive sensitivity and specificity values.
  • Data Provenance: "Prospective multicenter clinical trial." This indicates data was collected forward-in-time from multiple clinical sites. The country of origin is not specified.
  • Retrospective/Prospective: Prospective.

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

This information is not provided in the document. For the clinical studies, "diagnosis of Iron Deficiency Anemia (IDA), and for the differential diagnosis of IDA and Anemia of Chronic Disease (ACD)" would typically involve clinical experts (e.g., hematologists, clinical pathologists), but the number and qualifications are not mentioned.

4. Adjudication Method for the Test Set

This information is not provided in the document. For clinical diagnoses used as ground truth, an adjudication process (e.g., consensus by multiple experts) is often used, but it's not detailed here.

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

There was no MRMC comparative effectiveness study described. This device is an in vitro diagnostic (IVD) assay for measuring a biomarker (sTfR), not an AI-powered image analysis or diagnostic aid that would assist human readers in interpreting images or other complex data. Therefore, the concept of "human readers improve with AI vs without AI assistance" does not apply to this specific device.

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

Yes, the studies described are for the device's standalone performance as an in vitro diagnostic assay. The "Access sTfR reagents, calibrators, controls, and the Access Immunoassay Analyzers" perform the quantitative determination of sTfR levels. There isn't a human-in-the-loop component in the actual measurement and result generation of the sTfR value itself. The clinical interpretation of that sTfR result (and sTfR/log ferritin index) is then performed by a clinician.

7. The Type of Ground Truth Used

• Analytical Studies (Methods Comparison): The ground truth for method comparison was values obtained from the predicate device (Quantikine® IVD® sTfR ELISA). This is a common approach for demonstrating substantial equivalence for quantitative assays.
• Clinical Studies: The ground truth for the clinical study was the diagnosis of Iron Deficiency Anemia (IDA) and Anemia of Chronic Disease (ACD). This would typically be established through a combination of clinical assessment, laboratory tests (beyond just sTfR), and potentially bone marrow biopsy or response to iron therapy, which constitutes outcomes data or expert consensus-based clinical diagnosis. The document does not specify the exact methods for defining IDA and ACD beyond the general diagnoses.

8. The Sample Size for the Training Set

The document does not explicitly describe a "training set" in the context of an algorithm or machine learning model. This is an immunoassay (laboratory test), not a software/AI device that typically undergoes separate training and test phases.

• The "analytical studies" and "clinical studies" described serve as validation studies for the device's performance, much like a traditional device's verification and validation.
• There's no mention of a development phase where a model would be "trained" on a specific dataset.

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

As there is no explicit "training set" mentioned in the context of typical machine learning, this question is not applicable. The development and optimization of the immunoassay reagents and protocols are based on scientific principles of immunology and chemistry, rather than an algorithmic training process on ground truth data.

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